The Intellify

The Intellify

Augmented Reality (AR), & Virtual Reality (VR)

5.00/5 (5 Reviews)
About The Intellify
We Are the New-Gen Making Everything Possible! The Intellify offers feature-rich and business-specific apps that are meticulously designed and developed using the top tier technologies of Augmented Reality, Virtual Reality and Mixed Reality. We give your illusion an innov...
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$25 - $49/hr
10 - 49
2018
India, United States
The Intellify
Augmented Reality (AR), & Virtual Reality (VR)
5.00/5 (5 Reviews)
20 Questions
There’s an interesting story behind the finding of mobile business intelligence services. Industry experts got thinking if the meteorology department was equipped, through the use of accumulated data and satellite images, to predict the adverse weather conditions, why can’t a similar mechanism be implemented in businesses? A system that could help them predict underlying risks that emerge from wrong business decisions, more or less, which in turn would help them mitigate issues in advance.  Their long quest ended with the introduction of BI software.In layman’s language, BI or Business Intelligence presents data in the form of business intelligence dashboards and reports. Its main job is to visualize complex information in a more natural, approachable, and understandable way. Such intelligence software helps business units, top executives, managers, and other operational workers make better-informed decisions backed up with accurate data. Not surprisingly, today, you’ll find various tools developed by the companies to gather this information.Realizing the importance of mobile business intelligence has recently become a critical business concept to make BI a more inclusive business activity in the digital era.  However,  with an ever-growing business population embracing the mobile platform, the scope of BI expanded to mobile devices. So, it has become imperative that business aids like BI tools are also adopted for the mobile platform.  Mobile BI software solution1) Microsoft Power BIMicrosoft BI is cloud-based software to monitor data in real-time. The BI mobile app can keep a pulse on the business via live operational dashboards. Users can access on-premises data stored in SQL Server or data in the cloud.  Users can share live reports and dashboards directly from the app to keep your team on the same page.    2) Amazon QuicksightQuickSight allows you to create and publish interactive dashboards that include ML Insights easily.  The users can access an app on Android and iOS devices both. The user can have total control over the dashboards; it offers features for offline viewing. The dashboard view is optimized for your mobile device by showing the visuals in a vertical stack view, with each visual expanded to full width.3) SisenseThe Sisense has two ways to get data insights on mobile devices. Sisense Mobile BI App designed to work on any screen and any device. Sisense gives you the ability to combine data from many disparate sources into a single repository.  4) SAP Business object BI /  SAP RoamBIOne of the areas SAP is well-known for is its BI tools. Within SAP BusinessObjects Mobile users can get the most up to date information on the screen.  WebI reports created on the SAP BusinessObjects XI 4 platform can be published to the mobile server and will be served directly onto your mobile device without any hassle. Reports are displayed attractively and intuitively on the iPad and are drill enabled.  Roambi Analytics provides an easy way to consume interactive reports, dashboards, charts, KPIs, and data visualizations.  It is free to use for five users, and a free trial is available. Roambi was built from a mobile-first perspective, to help deliver data to everyone in a simple, engaging experience that can be accessed anytime, anywhere. SAP RoamBI can use it with any device, smartphone, tablet, pc, or mac. 5) DOMOWith DOMO’s mobile BI, all of your data is brought together and is made accessible to your entire team, wherever they want to access it.  The mobile-first app keeps your data automatically updated, and no additional development is required. The intuitive data exploration tools allow you to dig deeper into the business data.  Anyone with the Domo app can share and see live previews of any Domo card within iMessage.6) Oracle Business IntelligenceOracle Intelligence Mobile presents you with the full spectrum of BI functionality on ad hoc query, OLAP analysis, scorecards, and dashboards.  It can turn insight into action directly from your dashboards and mobile reports.  Business users on the go can be notified of time-critical information via alerts and notifications.7) TableauWith mobile Tableau, users can view metrics across multiple dashboards directly on their phone. With Tableau, any workbook you create is automatically mobile-enabled when accessed from a device. You can swipe to scroll, pinch to zoom, and leverage other touch-optimized interactions like quick filters. The solution helps businesses to gather data from multiple source points such as SQL databases, spreadsheets, cloud apps like Google Analytics and Salesforce to create a collective dataset.Characteristics of Ideal mobile business intelligence,Easy to installUser-friendly interfaceAlerts user on any abnormalityRobust security features and integrate easily with existing security protocolsOffline accessScalableSuperior monitoring capabilities ( client-side monitoring)Minimum maintenance costEasily integrate with other technologiesIn-depth reportingIt is better if it supports web browser as well as device-specific applicationThere are a few other mobile business intelligence software you would like to take a look like  IBM Cognos, MicroStrategy, Qlik, TIBCO, Dundas BI, Cluvio, Zoho Analytics, Sigma, Looker, Phocas BI. 
There’s an interesting story behind the finding of mobile business intelligence services. Industry experts got thinking if the meteorology department was equipped, through the use of accumulated data and satellite images, to predict the adverse weather conditions, why can’t a similar mechanism be implemented in businesses? A system that could help them predict underlying risks that emerge from wrong business decisions, more or less, which in turn would help them mitigate issues in advance.  Their long quest ended with the introduction of BI software.In layman’s language, BI or Business Intelligence presents data in the form of business intelligence dashboards and reports. Its main job is to visualize complex information in a more natural, approachable, and understandable way. Such intelligence software helps business units, top executives, managers, and other operational workers make better-informed decisions backed up with accurate data. Not surprisingly, today, you’ll find various tools developed by the companies to gather this information.Realizing the importance of mobile business intelligence has recently become a critical business concept to make BI a more inclusive business activity in the digital era.  However,  with an ever-growing business population embracing the mobile platform, the scope of BI expanded to mobile devices. So, it has become imperative that business aids like BI tools are also adopted for the mobile platform.  Mobile BI software solution1) Microsoft Power BIMicrosoft BI is cloud-based software to monitor data in real-time. The BI mobile app can keep a pulse on the business via live operational dashboards. Users can access on-premises data stored in SQL Server or data in the cloud.  Users can share live reports and dashboards directly from the app to keep your team on the same page.    2) Amazon QuicksightQuickSight allows you to create and publish interactive dashboards that include ML Insights easily.  The users can access an app on Android and iOS devices both. The user can have total control over the dashboards; it offers features for offline viewing. The dashboard view is optimized for your mobile device by showing the visuals in a vertical stack view, with each visual expanded to full width.3) SisenseThe Sisense has two ways to get data insights on mobile devices. Sisense Mobile BI App designed to work on any screen and any device. Sisense gives you the ability to combine data from many disparate sources into a single repository.  4) SAP Business object BI /  SAP RoamBIOne of the areas SAP is well-known for is its BI tools. Within SAP BusinessObjects Mobile users can get the most up to date information on the screen.  WebI reports created on the SAP BusinessObjects XI 4 platform can be published to the mobile server and will be served directly onto your mobile device without any hassle. Reports are displayed attractively and intuitively on the iPad and are drill enabled.  Roambi Analytics provides an easy way to consume interactive reports, dashboards, charts, KPIs, and data visualizations.  It is free to use for five users, and a free trial is available. Roambi was built from a mobile-first perspective, to help deliver data to everyone in a simple, engaging experience that can be accessed anytime, anywhere. SAP RoamBI can use it with any device, smartphone, tablet, pc, or mac. 5) DOMOWith DOMO’s mobile BI, all of your data is brought together and is made accessible to your entire team, wherever they want to access it.  The mobile-first app keeps your data automatically updated, and no additional development is required. The intuitive data exploration tools allow you to dig deeper into the business data.  Anyone with the Domo app can share and see live previews of any Domo card within iMessage.6) Oracle Business IntelligenceOracle Intelligence Mobile presents you with the full spectrum of BI functionality on ad hoc query, OLAP analysis, scorecards, and dashboards.  It can turn insight into action directly from your dashboards and mobile reports.  Business users on the go can be notified of time-critical information via alerts and notifications.7) TableauWith mobile Tableau, users can view metrics across multiple dashboards directly on their phone. With Tableau, any workbook you create is automatically mobile-enabled when accessed from a device. You can swipe to scroll, pinch to zoom, and leverage other touch-optimized interactions like quick filters. The solution helps businesses to gather data from multiple source points such as SQL databases, spreadsheets, cloud apps like Google Analytics and Salesforce to create a collective dataset.Characteristics of Ideal mobile business intelligence,Easy to installUser-friendly interfaceAlerts user on any abnormalityRobust security features and integrate easily with existing security protocolsOffline accessScalableSuperior monitoring capabilities ( client-side monitoring)Minimum maintenance costEasily integrate with other technologiesIn-depth reportingIt is better if it supports web browser as well as device-specific applicationThere are a few other mobile business intelligence software you would like to take a look like  IBM Cognos, MicroStrategy, Qlik, TIBCO, Dundas BI, Cluvio, Zoho Analytics, Sigma, Looker, Phocas BI. 

There’s an interesting story behind the finding of mobile business intelligence services. 

Industry experts got thinking if the meteorology department was equipped, through the use of accumulated data and satellite images, to predict the adverse weather conditions, why can’t a similar mechanism be implemented in businesses? A system that could help them predict underlying risks that emerge from wrong business decisions, more or less, which in turn would help them mitigate issues in advance. 

 

Their long quest ended with the introduction of BI software.

In layman’s language, BI or Business Intelligence presents data in the form of business intelligence dashboards and reports. Its main job is to visualize complex information in a more natural, approachable, and understandable way. Such intelligence software helps business units, top executives, managers, and other operational workers make better-informed decisions backed up with accurate data. Not surprisingly, today, you’ll find various tools developed by the companies to gather this information.

Realizing the importance of mobile business intelligence has recently become a critical business concept to make BI a more inclusive business activity in the digital era.  However,  with an ever-growing business population embracing the mobile platform, the scope of BI expanded to mobile devices. So, it has become imperative that business aids like BI tools are also adopted for the mobile platform.  

Mobile BI software solution

1) Microsoft Power BI

Microsoft BI is cloud-based software to monitor data in real-time. The BI mobile app can keep a pulse on the business via live operational dashboards. Users can access on-premises data stored in SQL Server or data in the cloud.  Users can share live reports and dashboards directly from the app to keep your team on the same page.    

2) Amazon Quicksight

QuickSight allows you to create and publish interactive dashboards that include ML Insights easily.  The users can access an app on Android and iOS devices both. The user can have total control over the dashboards; it offers features for offline viewing. The dashboard view is optimized for your mobile device by showing the visuals in a vertical stack view, with each visual expanded to full width.

3) Sisense

The Sisense has two ways to get data insights on mobile devices. Sisense Mobile BI App designed to work on any screen and any device. Sisense gives you the ability to combine data from many disparate sources into a single repository.  

4) SAP Business object BI /  SAP RoamBI

One of the areas SAP is well-known for is its BI tools. Within SAP BusinessObjects Mobile users can get the most up to date information on the screen.  WebI reports created on the SAP BusinessObjects XI 4 platform can be published to the mobile server and will be served directly onto your mobile device without any hassle. Reports are displayed attractively and intuitively on the iPad and are drill enabled.  

Roambi Analytics provides an easy way to consume interactive reports, dashboards, charts, KPIs, and data visualizations.  It is free to use for five users, and a free trial is available. Roambi was built from a mobile-first perspective, to help deliver data to everyone in a simple, engaging experience that can be accessed anytime, anywhere. SAP RoamBI can use it with any device, smartphone, tablet, pc, or mac. 

5) DOMO

With DOMO’s mobile BI, all of your data is brought together and is made accessible to your entire team, wherever they want to access it.  The mobile-first app keeps your data automatically updated, and no additional development is required. The intuitive data exploration tools allow you to dig deeper into the business data.  Anyone with the Domo app can share and see live previews of any Domo card within iMessage.

6) Oracle Business Intelligence

Oracle Intelligence Mobile presents you with the full spectrum of BI functionality on ad hoc query, OLAP analysis, scorecards, and dashboards.  It can turn insight into action directly from your dashboards and mobile reports.  Business users on the go can be notified of time-critical information via alerts and notifications.

7) Tableau

With mobile Tableau, users can view metrics across multiple dashboards directly on their phone. With Tableau, any workbook you create is automatically mobile-enabled when accessed from a device. You can swipe to scroll, pinch to zoom, and leverage other touch-optimized interactions like quick filters. The solution helps businesses to gather data from multiple source points such as SQL databases, spreadsheets, cloud apps like Google Analytics and Salesforce to create a collective dataset.

Characteristics of Ideal mobile business intelligence,

  • Easy to install
  • User-friendly interface
  • Alerts user on any abnormality
  • Robust security features and integrate easily with existing security protocols
  • Offline access
  • Scalable
  • Superior monitoring capabilities ( client-side monitoring)
  • Minimum maintenance cost
  • Easily integrate with other technologies
  • In-depth reporting
  • It is better if it supports web browser as well as device-specific application

There are a few other mobile business intelligence software you would like to take a look like  IBM Cognos, MicroStrategy, Qlik, TIBCO, Dundas BI, Cluvio, Zoho Analytics, Sigma, Looker, Phocas BI. 

AI to knowAI makes a machine’s ability to stimulate human intelligence. Learning, reasoning, logic, perception, and creativity earlier considered as unique features of human intelligence are today totally replaced by technology. The chatbots, robots, smart cars, logistics, banking, healthcare, and other IoT devices have marked their presence to replace human mind and revolutionizing the human life to the next level of technology every now and then.How AI works?Undoubtedly, the intelligent machine goes through a number of components and methodologies to reach the final result. It is build by studying the way a human brain works, thinks and decides and then, those biological mechanisms are applied to computers. Generally, the classic coding requires putting the exact inputs, output, and logic by the coders, AI provides a machine the desires input, output and lets the machine develop its own path for achieving its objectives. This makes the machines optimize a situation better than the humans. Streamlining financial processes and optimizing supply chain logistics are some good examples in this regard. The voice assistants such as Siri, Alexa, Cortana, and Google’s Assistant are the ones who quickly found the way to most customer houses. Is AI safe?AI is safe until how safe the technology on which it is built upon. But it is also a fact that any device using AI is connected to Internet and all Internet-connected devices cannot assure absolute security. This is the reason we see many news related to company data breaches and some AI vulnerabilities are also seen in the devices that are not properly secured. The future of AIIf you think AI is the future of technology, then you are wrong as it is already here. The voice assistants and other AI-enabled gadgets are already becoming more and more prominent in our lives. They have learnt more skills and companies are building out their connected device ecosystem. From a smart home assistant to e-commerce space, there is a lot of AI is affecting human life. For the cluttered marketplaces, online supplies are invented. For immediate payment, payment gateways are invented. There are many facts that show how AI is facilitating digital transformation across different industries. AI TakeoverAs much as the AI applications increase, people will start wondering if it is soon to erase the need of using human skills and experiences while performing different tasks. For example, an AI-enabled camera handled by a skilled and experienced photographer automatically points the snap and shot. Or AI-enabled software programs are trained to predict stocks to help people make decisions instead of the skills and intuitions of an experienced stockbroker.Some people will argue that AI trains someone faster to have years of experience without struggling hard for years to practice. But this also affects the fundamental human values and entirely remove the experience gaining process. If we will think in this way, there would nothing be much ridiculous than getting human beings replaced by AI. However, this thought cannot be confirmed or rejected yet. This discussion will need a long way to go until we really get deep into the visibly positive and negative effects of Artificial Intelligence on society. 
AI to knowAI makes a machine’s ability to stimulate human intelligence. Learning, reasoning, logic, perception, and creativity earlier considered as unique features of human intelligence are today totally replaced by technology. The chatbots, robots, smart cars, logistics, banking, healthcare, and other IoT devices have marked their presence to replace human mind and revolutionizing the human life to the next level of technology every now and then.How AI works?Undoubtedly, the intelligent machine goes through a number of components and methodologies to reach the final result. It is build by studying the way a human brain works, thinks and decides and then, those biological mechanisms are applied to computers. Generally, the classic coding requires putting the exact inputs, output, and logic by the coders, AI provides a machine the desires input, output and lets the machine develop its own path for achieving its objectives. This makes the machines optimize a situation better than the humans. Streamlining financial processes and optimizing supply chain logistics are some good examples in this regard. The voice assistants such as Siri, Alexa, Cortana, and Google’s Assistant are the ones who quickly found the way to most customer houses. Is AI safe?AI is safe until how safe the technology on which it is built upon. But it is also a fact that any device using AI is connected to Internet and all Internet-connected devices cannot assure absolute security. This is the reason we see many news related to company data breaches and some AI vulnerabilities are also seen in the devices that are not properly secured. The future of AIIf you think AI is the future of technology, then you are wrong as it is already here. The voice assistants and other AI-enabled gadgets are already becoming more and more prominent in our lives. They have learnt more skills and companies are building out their connected device ecosystem. From a smart home assistant to e-commerce space, there is a lot of AI is affecting human life. For the cluttered marketplaces, online supplies are invented. For immediate payment, payment gateways are invented. There are many facts that show how AI is facilitating digital transformation across different industries. AI TakeoverAs much as the AI applications increase, people will start wondering if it is soon to erase the need of using human skills and experiences while performing different tasks. For example, an AI-enabled camera handled by a skilled and experienced photographer automatically points the snap and shot. Or AI-enabled software programs are trained to predict stocks to help people make decisions instead of the skills and intuitions of an experienced stockbroker.Some people will argue that AI trains someone faster to have years of experience without struggling hard for years to practice. But this also affects the fundamental human values and entirely remove the experience gaining process. If we will think in this way, there would nothing be much ridiculous than getting human beings replaced by AI. However, this thought cannot be confirmed or rejected yet. This discussion will need a long way to go until we really get deep into the visibly positive and negative effects of Artificial Intelligence on society. 

AI to know

AI makes a machine’s ability to stimulate human intelligence. Learning, reasoning, logic, perception, and creativity earlier considered as unique features of human intelligence are today totally replaced by technology. The chatbots, robots, smart cars, logistics, banking, healthcare, and other IoT devices have marked their presence to replace human mind and revolutionizing the human life to the next level of technology every now and then.

How AI works?

Undoubtedly, the intelligent machine goes through a number of components and methodologies to reach the final result. It is build by studying the way a human brain works, thinks and decides and then, those biological mechanisms are applied to computers. Generally, the classic coding requires putting the exact inputs, output, and logic by the coders, AI provides a machine the desires input, output and lets the machine develop its own path for achieving its objectives. This makes the machines optimize a situation better than the humans. Streamlining financial processes and optimizing supply chain logistics are some good examples in this regard. The voice assistants such as Siri, Alexa, Cortana, and Google’s Assistant are the ones who quickly found the way to most customer houses. 

Is AI safe?

AI is safe until how safe the technology on which it is built upon. But it is also a fact that any device using AI is connected to Internet and all Internet-connected devices cannot assure absolute security. This is the reason we see many news related to company data breaches and some AI vulnerabilities are also seen in the devices that are not properly secured. 

The future of AI

If you think AI is the future of technology, then you are wrong as it is already here. The voice assistants and other AI-enabled gadgets are already becoming more and more prominent in our lives. They have learnt more skills and companies are building out their connected device ecosystem. From a smart home assistant to e-commerce space, there is a lot of AI is affecting human life. For the cluttered marketplaces, online supplies are invented. For immediate payment, payment gateways are invented. There are many facts that show how AI is facilitating digital transformation across different industries. 

AI Takeover

As much as the AI applications increase, people will start wondering if it is soon to erase the need of using human skills and experiences while performing different tasks. For example, an AI-enabled camera handled by a skilled and experienced photographer automatically points the snap and shot. Or AI-enabled software programs are trained to predict stocks to help people make decisions instead of the skills and intuitions of an experienced stockbroker.

Some people will argue that AI trains someone faster to have years of experience without struggling hard for years to practice. But this also affects the fundamental human values and entirely remove the experience gaining process. If we will think in this way, there would nothing be much ridiculous than getting human beings replaced by AI. 

However, this thought cannot be confirmed or rejected yet. This discussion will need a long way to go until we really get deep into the visibly positive and negative effects of Artificial Intelligence on society. 

Artificial Intelligence refers to the capacity of a machine or a computer program to perform human functions like learning, reasoning, problem-solving and thinking. It is a field of study where professionals learn to imbibe cognitive abilities in computers to make them smarter and more advanced. It is all about replicating human intelligence into machines. Whereas, BI refers to all the analytics tools that enterprises use to collect data points which aid them in making competent and informed decisions. It benefits businesses to understand their customer behaviours, which assist in shaping their products and services.The significant points of difference between Artificial Intelligence and Business Intelligence are as follows; Goals The goal of AI is to model human-like intelligence. AI programs can make rational decisions once they have a good understanding of behaviours and thought-processes of a human brain. If a chatbot can develop reliable intuition, then it can increase the efficiency of the business and drive profits. The goal of Business Intelligence is to facilitate collection and analysis of data, BI tools help translate the otherwise obscure data into a coherent picture. Applications BI enhances the functional capability and efficiency of an enterprise; it exists from time immemorial through spreadsheets such as Microsoft Excel. But now we can witness its advanced applications in digital dashboards, data mining, data warehouses, business analytics and many more. AI is used in a myriad of enterprises such as medical, gaming, language processing, intelligent systems, humanoids and speech recognition etc.Research areasNeural networks, robotics, fuzzy logic, expert systems are some of the areas where AI professionals do research and studies. In BI, the prime areas of research are data mining in social network platforms, big data, OLAP, Adhoc analytics, advanced business analytics and monitoring. How do they work?BI is established on statistical analysis – it studies the past data. It provides data-driven insights to businesses to reinforce their customer service, product designing while improving business processes and employee satisfaction. Owing to its top-notch machine learning, algorithms understanding and intuitive abilities, AI helps to predict the demand, trends and competitors’ standings in the marketplace. Final words Although BI came before AI in the hierarchy of analytics, they both provide predictive and descriptive analysis which are immensely crucial for the survival of the business. If used together, they can delve deeper into the data, solve complex problems and give actionable insights for decision-making.AI and BI are growing at a rapid rate individually; they are going to be an integral part of modern business operations to derive excellence.  
Artificial Intelligence refers to the capacity of a machine or a computer program to perform human functions like learning, reasoning, problem-solving and thinking. It is a field of study where professionals learn to imbibe cognitive abilities in computers to make them smarter and more advanced. It is all about replicating human intelligence into machines. Whereas, BI refers to all the analytics tools that enterprises use to collect data points which aid them in making competent and informed decisions. It benefits businesses to understand their customer behaviours, which assist in shaping their products and services.The significant points of difference between Artificial Intelligence and Business Intelligence are as follows; Goals The goal of AI is to model human-like intelligence. AI programs can make rational decisions once they have a good understanding of behaviours and thought-processes of a human brain. If a chatbot can develop reliable intuition, then it can increase the efficiency of the business and drive profits. The goal of Business Intelligence is to facilitate collection and analysis of data, BI tools help translate the otherwise obscure data into a coherent picture. Applications BI enhances the functional capability and efficiency of an enterprise; it exists from time immemorial through spreadsheets such as Microsoft Excel. But now we can witness its advanced applications in digital dashboards, data mining, data warehouses, business analytics and many more. AI is used in a myriad of enterprises such as medical, gaming, language processing, intelligent systems, humanoids and speech recognition etc.Research areasNeural networks, robotics, fuzzy logic, expert systems are some of the areas where AI professionals do research and studies. In BI, the prime areas of research are data mining in social network platforms, big data, OLAP, Adhoc analytics, advanced business analytics and monitoring. How do they work?BI is established on statistical analysis – it studies the past data. It provides data-driven insights to businesses to reinforce their customer service, product designing while improving business processes and employee satisfaction. Owing to its top-notch machine learning, algorithms understanding and intuitive abilities, AI helps to predict the demand, trends and competitors’ standings in the marketplace. Final words Although BI came before AI in the hierarchy of analytics, they both provide predictive and descriptive analysis which are immensely crucial for the survival of the business. If used together, they can delve deeper into the data, solve complex problems and give actionable insights for decision-making.AI and BI are growing at a rapid rate individually; they are going to be an integral part of modern business operations to derive excellence.  

Artificial Intelligence refers to the capacity of a machine or a computer program to perform human functions like learning, reasoning, problem-solving and thinking. It is a field of study where professionals learn to imbibe cognitive abilities in computers to make them smarter and more advanced. It is all about replicating human intelligence into machines. 

Whereas, BI refers to all the analytics tools that enterprises use to collect data points which aid them in making competent and informed decisions. It benefits businesses to understand their customer behaviours, which assist in shaping their products and services.

The significant points of difference between Artificial Intelligence and Business Intelligence are as follows; 

Goals 

The goal of AI is to model human-like intelligence. AI programs can make rational decisions once they have a good understanding of behaviours and thought-processes of a human brain. If a chatbot can develop reliable intuition, then it can increase the efficiency of the business and drive profits. 

The goal of Business Intelligence is to facilitate collection and analysis of data, BI tools help translate the otherwise obscure data into a coherent picture. 

Applications 

BI enhances the functional capability and efficiency of an enterprise; it exists from time immemorial through spreadsheets such as Microsoft Excel. But now we can witness its advanced applications in digital dashboards, data mining, data warehouses, business analytics and many more. 

AI is used in a myriad of enterprises such as medical, gaming, language processing, intelligent systems, humanoids and speech recognition etc.

Research areas

Neural networks, robotics, fuzzy logic, expert systems are some of the areas where AI professionals do research and studies. 

In BI, the prime areas of research are data mining in social network platforms, big data, OLAP, Adhoc analytics, advanced business analytics and monitoring. 

How do they work?

BI is established on statistical analysis – it studies the past data. It provides data-driven insights to businesses to reinforce their customer service, product designing while improving business processes and employee satisfaction. 

Owing to its top-notch machine learning, algorithms understanding and intuitive abilities, AI helps to predict the demand, trends and competitors’ standings in the marketplace. 

Final words 

Although BI came before AI in the hierarchy of analytics, they both provide predictive and descriptive analysis which are immensely crucial for the survival of the business. If used together, they can delve deeper into the data, solve complex problems and give actionable insights for decision-making.

AI and BI are growing at a rapid rate individually; they are going to be an integral part of modern business operations to derive excellence.  

Big data is a huge amount of data that has not been handled by the traditional data management systems. Business Intelligence(BI) is a technique, tool required to collect, store, analyse data into valuable information and benefit from analysing and making efficient business decisions.
Big data is a huge amount of data that has not been handled by the traditional data management systems. Business Intelligence(BI) is a technique, tool required to collect, store, analyse data into valuable information and benefit from analysing and making efficient business decisions.

Big data is a huge amount of data that has not been handled by the traditional data management systems.

Business Intelligence(BI) is a technique, tool required to collect, store, analyse data into valuable information and benefit from analysing and making efficient business decisions.

Business Intelligence (BI) is the use of technologies to transform raw data into useful information. It helps analyze business data like profits, incomes, sales, and revenue generation. These tools aid in turning these statistics into an action plan. You will find several reasons to leverage this trending technology. Many startups sprung up every year in market. Startup entrepreneurs need software that understands their needs and helps them work with new marketing tactics. Business intelligence tools can help them face these challenges. Common benefits achieved by SMBs while utilizing business intelligence tools are better decision making, improved data quality, quick reporting, faster analysis and planning, better operational efficiency, increase in profits, and reduction in costs. Leveraging these tools can thus make it easy for businesses to focus on customer needs rather than maintaining and decoding the collected data. The best three primary BI tools to choose from for small to medium e-commerce websites are: 1. DBxtra: It is mainly established for the non-technical employees struggling to decode and analyze the data to generate insights. The technology is connected with several databases in numerous locations and allows you to operate without being skilled in SQL. The data it collects is then transformed into a long five-hour presentation, charts, or tables with other pictures. 2. Sisense: The tools’ best features is to quickly drag and drop the data sets to make them one. For example, you got data of a particular customer in CMS and the sales database in accounting software. You can anytime open the software and connect the two databases with the help of visual connector system. 3. SAP Business Intelligence: This particular technology provides several analytics solutions to the firm. It offers machine learning, predictive analysis, and planning with analysis. It also includes office integration and mobile analytics, data visualization, and analytics applications. BI is the fastest developing technology, among others. The three mentioned are considered to be the best as per the experience of its users from the small E-Commerce companies. Connecting with the best business intelligence software will help you judge better.
Business Intelligence (BI) is the use of technologies to transform raw data into useful information. It helps analyze business data like profits, incomes, sales, and revenue generation. These tools aid in turning these statistics into an action plan. You will find several reasons to leverage this trending technology. Many startups sprung up every year in market. Startup entrepreneurs need software that understands their needs and helps them work with new marketing tactics. Business intelligence tools can help them face these challenges. Common benefits achieved by SMBs while utilizing business intelligence tools are better decision making, improved data quality, quick reporting, faster analysis and planning, better operational efficiency, increase in profits, and reduction in costs. Leveraging these tools can thus make it easy for businesses to focus on customer needs rather than maintaining and decoding the collected data. The best three primary BI tools to choose from for small to medium e-commerce websites are: 1. DBxtra: It is mainly established for the non-technical employees struggling to decode and analyze the data to generate insights. The technology is connected with several databases in numerous locations and allows you to operate without being skilled in SQL. The data it collects is then transformed into a long five-hour presentation, charts, or tables with other pictures. 2. Sisense: The tools’ best features is to quickly drag and drop the data sets to make them one. For example, you got data of a particular customer in CMS and the sales database in accounting software. You can anytime open the software and connect the two databases with the help of visual connector system. 3. SAP Business Intelligence: This particular technology provides several analytics solutions to the firm. It offers machine learning, predictive analysis, and planning with analysis. It also includes office integration and mobile analytics, data visualization, and analytics applications. BI is the fastest developing technology, among others. The three mentioned are considered to be the best as per the experience of its users from the small E-Commerce companies. Connecting with the best business intelligence software will help you judge better.

Business Intelligence (BI) is the use of technologies to transform raw data into useful information. It helps analyze business data like profits, incomes, sales, and revenue generation. These tools aid in turning these statistics into an action plan. You will find several reasons to leverage this trending technology. Many startups sprung up every year in market. Startup entrepreneurs need software that understands their needs and helps them work with new marketing tactics. Business intelligence tools can help them face these challenges.

Common benefits achieved by SMBs while utilizing business intelligence tools are better decision making, improved data quality, quick reporting, faster analysis and planning, better operational efficiency, increase in profits, and reduction in costs. Leveraging these tools can thus make it easy for businesses to focus on customer needs rather than maintaining and decoding the collected data.

The best three primary BI tools to choose from for small to medium e-commerce websites are:

1. DBxtra: It is mainly established for the non-technical employees struggling to decode and analyze the data to generate insights. The technology is connected with several databases in numerous locations and allows you to operate without being skilled in SQL. The data it collects is then transformed into a long five-hour presentation, charts, or tables with other pictures.

2. Sisense: The tools’ best features is to quickly drag and drop the data sets to make them one. For example, you got data of a particular customer in CMS and the sales database in accounting software. You can anytime open the software and connect the two databases with the help of visual connector system.

3. SAP Business Intelligence: This particular technology provides several analytics solutions to the firm. It offers machine learning, predictive analysis, and planning with analysis. It also includes office integration and mobile analytics, data visualization, and analytics applications.

BI is the fastest developing technology, among others. The three mentioned are considered to be the best as per the experience of its users from the small E-Commerce companies. Connecting with the best business intelligence software will help you judge better.

Artificial intelligence is a simulation of the human brain in the best of its form. Their neural network is far quicker than human reflexes that could capture views even from a speeding train. Over the years, artificial intelligence has improved in its comprehensive skills and decision power by refining the accumulated data and analyzing them just as humans do.  It has been possible to wire electrical components to perform intellectual jobs. However, the data-scientist added one more tool to enhance AI capabilities called “Machine Learning.”  (christophm.github)   How is ML different from AI? - Machine learning is not separate from AI; it is a subset of artificial intelligence. In general, Machine Learning is the smarter version of AI that does not require to program over and again. Basically, in AI, the software behaves according to the specific instructions (spoon-feeding) or program. But in ML, you don’t prompt the machine how to behave; all you have to do is feed the data and rest work is done by ML. Unlike AI, ML does not need millions of codes, complex rules, and decision trees to perform any particular task but instead uses algorithms that adjust itself and improves. It trains itself to change the algorithm as per the data input and takes action accordingly.   You can resemble it with manual vs. automatic cars. In a manual car, you have to shift the gears manually whenever you want to increase/decrease the speed of the car, in automatic cars the system takes care of the gear-box, and all you need to do is to control the acceleration padel.   Example of Machine Learning- One of the best examples of Machine learning is “product recommendation,” you too may have experienced this while accessing a shopping app or website when a message pops out recommending the product based on your past buying history. To see what is the difference between them, see the image below.   ( Image source: quora)  There are a few more details that set them apart from each other.  Machine learning vs. Artificial Intelligence  Machine learning has equipped the business owner to achieve pinpoint accuracy in reading customer’s demand and design products accordingly. The ML model can be used to segment customers, spot anomalies, or forecast sales. With AutoML kicking in, these processes will get even better, and companies that were reluctant to adopting ML application due to lack of expertise will now embrace it with open hands. AutoML will eliminate the labor-intensive job of choosing and tuning the machine-learning models.
Artificial intelligence is a simulation of the human brain in the best of its form. Their neural network is far quicker than human reflexes that could capture views even from a speeding train. Over the years, artificial intelligence has improved in its comprehensive skills and decision power by refining the accumulated data and analyzing them just as humans do.  It has been possible to wire electrical components to perform intellectual jobs. However, the data-scientist added one more tool to enhance AI capabilities called “Machine Learning.”  (christophm.github)   How is ML different from AI? - Machine learning is not separate from AI; it is a subset of artificial intelligence. In general, Machine Learning is the smarter version of AI that does not require to program over and again. Basically, in AI, the software behaves according to the specific instructions (spoon-feeding) or program. But in ML, you don’t prompt the machine how to behave; all you have to do is feed the data and rest work is done by ML. Unlike AI, ML does not need millions of codes, complex rules, and decision trees to perform any particular task but instead uses algorithms that adjust itself and improves. It trains itself to change the algorithm as per the data input and takes action accordingly.   You can resemble it with manual vs. automatic cars. In a manual car, you have to shift the gears manually whenever you want to increase/decrease the speed of the car, in automatic cars the system takes care of the gear-box, and all you need to do is to control the acceleration padel.   Example of Machine Learning- One of the best examples of Machine learning is “product recommendation,” you too may have experienced this while accessing a shopping app or website when a message pops out recommending the product based on your past buying history. To see what is the difference between them, see the image below.   ( Image source: quora)  There are a few more details that set them apart from each other.  Machine learning vs. Artificial Intelligence  Machine learning has equipped the business owner to achieve pinpoint accuracy in reading customer’s demand and design products accordingly. The ML model can be used to segment customers, spot anomalies, or forecast sales. With AutoML kicking in, these processes will get even better, and companies that were reluctant to adopting ML application due to lack of expertise will now embrace it with open hands. AutoML will eliminate the labor-intensive job of choosing and tuning the machine-learning models.

Artificial intelligence is a simulation of the human brain in the best of its form. Their neural network is far quicker than human reflexes that could capture views even from a speeding train. Over the years, artificial intelligence has improved in its comprehensive skills and decision power by refining the accumulated data and analyzing them just as humans do. 

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It has been possible to wire electrical components to perform intellectual jobs. However, the data-scientist added one more tool to enhance AI capabilities called “Machine Learning.” 

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(christophm.github)  

  • How is ML different from AI? - Machine learning is not separate from AI; it is a subset of artificial intelligence. In general, Machine Learning is the smarter version of AI that does not require to program over and again. Basically, in AI, the software behaves according to the specific instructions (spoon-feeding) or program. But in ML, you don’t prompt the machine how to behave; all you have to do is feed the data and rest work is done by ML. Unlike AI, ML does not need millions of codes, complex rules, and decision trees to perform any particular task but instead uses algorithms that adjust itself and improves. It trains itself to change the algorithm as per the data input and takes action accordingly.  

You can resemble it with manual vs. automatic cars. In a manual car, you have to shift the gears manually whenever you want to increase/decrease the speed of the car, in automatic cars the system takes care of the gear-box, and all you need to do is to control the acceleration padel.  

  • Example of Machine Learning- One of the best examples of Machine learning is “product recommendation,” you too may have experienced this while accessing a shopping app or website when a message pops out recommending the product based on your past buying history. To see what is the difference between them, see the image below.  

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( Image source: quora) 

There are a few more details that set them apart from each other. 

Machine learning vs. Artificial Intelligence 

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Machine learning has equipped the business owner to achieve pinpoint accuracy in reading customer’s demand and design products accordingly. The ML model can be used to segment customers, spot anomalies, or forecast sales. With AutoML kicking in, these processes will get even better, and companies that were reluctant to adopting ML application due to lack of expertise will now embrace it with open hands. AutoML will eliminate the labor-intensive job of choosing and tuning the machine-learning models.

Companies today, small, medium, and large are using business intelligence solutions in their businesses to make effective data-driven decisions. Large companies are bombarded with information overload, and through the tools provided by business intelligence solution company they can control, understand, analyze their data.  Business intelligence solutions are essential because they help managers and business owners make smart decisions and meet their sales and marketing targets. The tools provide a means of interacting with their customers giving them a deeper understanding of their client’s needs and fulfilling them effectively. Business intelligence solutions enhance data security. They also help eliminate the time-consuming task of compiling data manually, thus, saving time.  When choosing a company that provides you with business intelligence services in India, you have to pay attention to data modeling, data visualization, reporting, support, big data integration, deployment environment, and native security.  If you are looking to improve productivity in your company and save time when going through vast piles of data, adopts any of the top 10 business intelligence solutions discussed below.   Birst   Birst provides its users with a cloud-based analytics solution that helps them discover insights without the use of analytical input. It allows users to pinpoint patterns and understand their company’s key performance indicators.  The Birst tool features an automated data refinement that merges data from multiple sources into one user-ready data. It also allows real-time access to data. Birst also has an adaptive UX, interactive dashboards, multi-tenant cloud architecture, machine learning, and one-click data connectivity. It also has a mobile application for androids and iOS through which users can access their reports and dashboards.  Cons  Not suitable for complex analysis, does not support non-English languages, and lacks timely support.   Board   Board is a data discovery platform that is used by small, medium, and large companies. It provides users with business intelligence, business analytics, and enterprise performance management under a single platform. Board has customizable and interactive dashboards that enable the users to see a complete overview of their business. They can also analyze their business's KPIs to asses their performance objectives.  Board features an in-memory technology known as Hybrid Bitwise Memory Pattern that offers the high-performance capability to read and write, quick data visualizations, simulations that and planning processes. It allows users to export data in several useful formats such as HTML and CSV. Board is multilingual such that it provides different languages.  Cons   It is only operable with windows  It is extremely configurable  It takes a lot of time to set up the security profiles  It involves in-depth learning; thus, not easy to use    Dundas BI Software   Dunda BI is a product od Dunda Data Visualization. It allows the users to gather data from multiple sources and then generate interactive dashboards, customize their visualizations, and build reports in the form of charts and graphs. These charts and graphs enable them to identify and predict pattern trends in their organizations. This allows users to make informed decisions and implement best practices in their business.  With the use of the Dundas BI interface, users can easily create a wide range of reports such as automated or customized, and the recycle bin features make it easy to restore documents once deleted.  Dundas BI features API support and HTML 5 foundation.  Cons   Does not support 3-D charts  In-memory processing requires a larger RAM  Does not support text analytics   Sisense   Small, medium, and large companies can use Sisense. It focuses on data discovery, analysis, and intelligence, and every user can access the data through embeddable, scalable, accessible architecture. Sisense has a back end powered in chip technology that allows users to combine data from multiple sources into a single database. This allows front end users to create visuals, reports, and dashboards. It also enables the sharing of information between the users.  Sisense features In-chip analytics, Prism10X that allows users to analyze data 10 times faster than with any other in-memory solution. The single-stack system enables users to perform a range of tasks on the same platform. Machine learning and integration.  Cons   It does work well with sophisticated and vast data sets.  It is not user-friendly  Does not support predictive modeling and analysis  It does not support 3-D graphics.    QlikView   QlikView Platform focuses on data discovery and customer insight. The tool is from Qlik, which is a leader in the idea and intelligence space. It is easy to access and offers self-service data that help users make decisions and generate personalized reports and custom dashboards.  QlikView is affordable and can be used in any business. It designed in a way that can connect to any source of data such as big data streams, cloud data, or file-based data. Its patented in-memory data processing feature processes the data into as little as 10% of its initial size.  QlikView allows collaboration across a user’s interface such that two people can share the same dashboard. The patented in-memory application enables users to conduct quick searches and eliminates the issue of slow, on-disk applications.  Cons  It does not allow users to identify patterns within the data, and users can not predict project profitability.  Looker   It is a great collaboration tool as it allows users from different departments to pull together data from sales, support, logistics, and e-commerce. It easy to use and permits the sharing of information from one department to another. You do not need to be a tech guru to use Looker.  Looker provides users with self-service tools, such as filtering, pivoting, and visualizations. Looker’s dashboard gives the user a quick and clear view of insights.  Looker is not limited to any web browser, and it can also be installed in a mobile device, giving users mobility in collaboration.it also allows real-time updates.  Looker can be used in the healthcare, financial, technology industry.   Microstrategy   Microstrategy is used by both small and medium-sized companies. It is a popular business intelligence solution because it is easy to use. Besides, it has scalable and sophisticated analytics functions.  Microstrategy has unique features that help it stand out, such as social intelligence, app integration, mobile productivity, data discovery, and real-time telemetry. It allows users to combine data from more than 200 sources and then visualize them.  Microstrategy can be downloaded on the phone, making it easy to read and share data on the go. It is also designed for use by any business size since it can be operated from a desktop, mobile. It offers users of all levels with business intelligence services. Major clients include Merck, AIG, Coca-Cola, and Vibes.  Cons  It does not support portal integration, and training modules are costly. It does not point out the issues in the analyzed data.  Zoho Analytics   Zoho is a self-service BI and data analytics platform. Zoho allows the user to incorporate data from several sources and blend it, to generate reports and dashboards. The BI solution features a drag and drop, eliminating the need to download and upload data. It also has various visualization tools that allow the user to drill down to specifics. It provides machine learning natural language processing.  Final Thoughts  Before investing in any of the following business intelligence solutions also consider the following, subscription, maintenance, installation, customization, data migration, and renewal costs. You should also look at the features and choose according to the needs of your business.
Companies today, small, medium, and large are using business intelligence solutions in their businesses to make effective data-driven decisions. Large companies are bombarded with information overload, and through the tools provided by business intelligence solution company they can control, understand, analyze their data.  Business intelligence solutions are essential because they help managers and business owners make smart decisions and meet their sales and marketing targets. The tools provide a means of interacting with their customers giving them a deeper understanding of their client’s needs and fulfilling them effectively. Business intelligence solutions enhance data security. They also help eliminate the time-consuming task of compiling data manually, thus, saving time.  When choosing a company that provides you with business intelligence services in India, you have to pay attention to data modeling, data visualization, reporting, support, big data integration, deployment environment, and native security.  If you are looking to improve productivity in your company and save time when going through vast piles of data, adopts any of the top 10 business intelligence solutions discussed below.   Birst   Birst provides its users with a cloud-based analytics solution that helps them discover insights without the use of analytical input. It allows users to pinpoint patterns and understand their company’s key performance indicators.  The Birst tool features an automated data refinement that merges data from multiple sources into one user-ready data. It also allows real-time access to data. Birst also has an adaptive UX, interactive dashboards, multi-tenant cloud architecture, machine learning, and one-click data connectivity. It also has a mobile application for androids and iOS through which users can access their reports and dashboards.  Cons  Not suitable for complex analysis, does not support non-English languages, and lacks timely support.   Board   Board is a data discovery platform that is used by small, medium, and large companies. It provides users with business intelligence, business analytics, and enterprise performance management under a single platform. Board has customizable and interactive dashboards that enable the users to see a complete overview of their business. They can also analyze their business's KPIs to asses their performance objectives.  Board features an in-memory technology known as Hybrid Bitwise Memory Pattern that offers the high-performance capability to read and write, quick data visualizations, simulations that and planning processes. It allows users to export data in several useful formats such as HTML and CSV. Board is multilingual such that it provides different languages.  Cons   It is only operable with windows  It is extremely configurable  It takes a lot of time to set up the security profiles  It involves in-depth learning; thus, not easy to use    Dundas BI Software   Dunda BI is a product od Dunda Data Visualization. It allows the users to gather data from multiple sources and then generate interactive dashboards, customize their visualizations, and build reports in the form of charts and graphs. These charts and graphs enable them to identify and predict pattern trends in their organizations. This allows users to make informed decisions and implement best practices in their business.  With the use of the Dundas BI interface, users can easily create a wide range of reports such as automated or customized, and the recycle bin features make it easy to restore documents once deleted.  Dundas BI features API support and HTML 5 foundation.  Cons   Does not support 3-D charts  In-memory processing requires a larger RAM  Does not support text analytics   Sisense   Small, medium, and large companies can use Sisense. It focuses on data discovery, analysis, and intelligence, and every user can access the data through embeddable, scalable, accessible architecture. Sisense has a back end powered in chip technology that allows users to combine data from multiple sources into a single database. This allows front end users to create visuals, reports, and dashboards. It also enables the sharing of information between the users.  Sisense features In-chip analytics, Prism10X that allows users to analyze data 10 times faster than with any other in-memory solution. The single-stack system enables users to perform a range of tasks on the same platform. Machine learning and integration.  Cons   It does work well with sophisticated and vast data sets.  It is not user-friendly  Does not support predictive modeling and analysis  It does not support 3-D graphics.    QlikView   QlikView Platform focuses on data discovery and customer insight. The tool is from Qlik, which is a leader in the idea and intelligence space. It is easy to access and offers self-service data that help users make decisions and generate personalized reports and custom dashboards.  QlikView is affordable and can be used in any business. It designed in a way that can connect to any source of data such as big data streams, cloud data, or file-based data. Its patented in-memory data processing feature processes the data into as little as 10% of its initial size.  QlikView allows collaboration across a user’s interface such that two people can share the same dashboard. The patented in-memory application enables users to conduct quick searches and eliminates the issue of slow, on-disk applications.  Cons  It does not allow users to identify patterns within the data, and users can not predict project profitability.  Looker   It is a great collaboration tool as it allows users from different departments to pull together data from sales, support, logistics, and e-commerce. It easy to use and permits the sharing of information from one department to another. You do not need to be a tech guru to use Looker.  Looker provides users with self-service tools, such as filtering, pivoting, and visualizations. Looker’s dashboard gives the user a quick and clear view of insights.  Looker is not limited to any web browser, and it can also be installed in a mobile device, giving users mobility in collaboration.it also allows real-time updates.  Looker can be used in the healthcare, financial, technology industry.   Microstrategy   Microstrategy is used by both small and medium-sized companies. It is a popular business intelligence solution because it is easy to use. Besides, it has scalable and sophisticated analytics functions.  Microstrategy has unique features that help it stand out, such as social intelligence, app integration, mobile productivity, data discovery, and real-time telemetry. It allows users to combine data from more than 200 sources and then visualize them.  Microstrategy can be downloaded on the phone, making it easy to read and share data on the go. It is also designed for use by any business size since it can be operated from a desktop, mobile. It offers users of all levels with business intelligence services. Major clients include Merck, AIG, Coca-Cola, and Vibes.  Cons  It does not support portal integration, and training modules are costly. It does not point out the issues in the analyzed data.  Zoho Analytics   Zoho is a self-service BI and data analytics platform. Zoho allows the user to incorporate data from several sources and blend it, to generate reports and dashboards. The BI solution features a drag and drop, eliminating the need to download and upload data. It also has various visualization tools that allow the user to drill down to specifics. It provides machine learning natural language processing.  Final Thoughts  Before investing in any of the following business intelligence solutions also consider the following, subscription, maintenance, installation, customization, data migration, and renewal costs. You should also look at the features and choose according to the needs of your business.

Companies today, small, medium, and large are using business intelligence solutions in their businesses to make effective data-driven decisions. Large companies are bombarded with information overload, and through the tools provided by business intelligence solution company they can control, understand, analyze their data. 

Business intelligence solutions are essential because they help managers and business owners make smart decisions and meet their sales and marketing targets. The tools provide a means of interacting with their customers giving them a deeper understanding of their client’s needs and fulfilling them effectively. Business intelligence solutions enhance data security. They also help eliminate the time-consuming task of compiling data manually, thus, saving time. 

When choosing a company that provides you with business intelligence services in India, you have to pay attention to data modeling, data visualization, reporting, support, big data integration, deployment environment, and native security. 

If you are looking to improve productivity in your company and save time when going through vast piles of data, adopts any of the top 10 business intelligence solutions discussed below.  

  1. Birst  

Birst provides its users with a cloud-based analytics solution that helps them discover insights without the use of analytical input. It allows users to pinpoint patterns and understand their company’s key performance indicators. 

The Birst tool features an automated data refinement that merges data from multiple sources into one user-ready data. It also allows real-time access to data. Birst also has an adaptive UX, interactive dashboards, multi-tenant cloud architecture, machine learning, and one-click data connectivity. It also has a mobile application for androids and iOS through which users can access their reports and dashboards. 

Cons 

Not suitable for complex analysis, does not support non-English languages, and lacks timely support.  

  1. Board  

Board is a data discovery platform that is used by small, medium, and large companies. It provides users with business intelligence, business analytics, and enterprise performance management under a single platform. Board has customizable and interactive dashboards that enable the users to see a complete overview of their business. They can also analyze their business's KPIs to asses their performance objectives. 

Board features an in-memory technology known as Hybrid Bitwise Memory Pattern that offers the high-performance capability to read and write, quick data visualizations, simulations that and planning processes. It allows users to export data in several useful formats such as HTML and CSV. Board is multilingual such that it provides different languages. 

Cons  

  • It is only operable with windows 
  • It is extremely configurable 
  • It takes a lot of time to set up the security profiles 
  • It involves in-depth learning; thus, not easy to use   
  1. Dundas BI Software  

Dunda BI is a product od Dunda Data Visualization. It allows the users to gather data from multiple sources and then generate interactive dashboards, customize their visualizations, and build reports in the form of charts and graphs. These charts and graphs enable them to identify and predict pattern trends in their organizations. This allows users to make informed decisions and implement best practices in their business. 

With the use of the Dundas BI interface, users can easily create a wide range of reports such as automated or customized, and the recycle bin features make it easy to restore documents once deleted. 

Dundas BI features API support and HTML 5 foundation. 

Cons  

  • Does not support 3-D charts 
  • In-memory processing requires a larger RAM 
  • Does not support text analytics  
  1. Sisense  

Small, medium, and large companies can use Sisense. It focuses on data discovery, analysis, and intelligence, and every user can access the data through embeddable, scalable, accessible architecture. Sisense has a back end powered in chip technology that allows users to combine data from multiple sources into a single database. This allows front end users to create visuals, reports, and dashboards. It also enables the sharing of information between the users. 

Sisense features In-chip analytics, Prism10X that allows users to analyze data 10 times faster than with any other in-memory solution. The single-stack system enables users to perform a range of tasks on the same platform. Machine learning and integration. 

Cons  

  • It does work well with sophisticated and vast data sets. 
  • It is not user-friendly 
  • Does not support predictive modeling and analysis 
  • It does not support 3-D graphics.   
  1. QlikView  

QlikView Platform focuses on data discovery and customer insight. The tool is from Qlik, which is a leader in the idea and intelligence space. It is easy to access and offers self-service data that help users make decisions and generate personalized reports and custom dashboards. 

QlikView is affordable and can be used in any business. It designed in a way that can connect to any source of data such as big data streams, cloud data, or file-based data. Its patented in-memory data processing feature processes the data into as little as 10% of its initial size. 

QlikView allows collaboration across a user’s interface such that two people can share the same dashboard. The patented in-memory application enables users to conduct quick searches and eliminates the issue of slow, on-disk applications. 

Cons 

It does not allow users to identify patterns within the data, and users can not predict project profitability. 

  1. Looker  

It is a great collaboration tool as it allows users from different departments to pull together data from sales, support, logistics, and e-commerce. It easy to use and permits the sharing of information from one department to another. You do not need to be a tech guru to use Looker. 

Looker provides users with self-service tools, such as filtering, pivoting, and visualizations. Looker’s dashboard gives the user a quick and clear view of insights. 

Looker is not limited to any web browser, and it can also be installed in a mobile device, giving users mobility in collaboration.it also allows real-time updates. 

Looker can be used in the healthcare, financial, technology industry.  

  1. Microstrategy  

Microstrategy is used by both small and medium-sized companies. It is a popular business intelligence solution because it is easy to use. Besides, it has scalable and sophisticated analytics functions. 

Microstrategy has unique features that help it stand out, such as social intelligence, app integration, mobile productivity, data discovery, and real-time telemetry. It allows users to combine data from more than 200 sources and then visualize them. 

Microstrategy can be downloaded on the phone, making it easy to read and share data on the go. It is also designed for use by any business size since it can be operated from a desktop, mobile. It offers users of all levels with business intelligence services. Major clients include Merck, AIG, Coca-Cola, and Vibes. 

Cons 

It does not support portal integration, and training modules are costly. It does not point out the issues in the analyzed data. 

  1. Zoho Analytics  

Zoho is a self-service BI and data analytics platform. Zoho allows the user to incorporate data from several sources and blend it, to generate reports and dashboards. The BI solution features a drag and drop, eliminating the need to download and upload data. It also has various visualization tools that allow the user to drill down to specifics. It provides machine learning natural language processing. 

Final Thoughts 

Before investing in any of the following business intelligence solutions also consider the following, subscription, maintenance, installation, customization, data migration, and renewal costs. You should also look at the features and choose according to the needs of your business.

Artificial Intelligence and Machine Learning technologies have found their way to our everyday lives. The recent COVID-19 pandemic has encouraged digital transactions and transformations of our official and personal activities. AI-enabled sources have become the important components of our household applications, electronic goods, smartphones, cars, etc. The best thing is they can recognize our voice, touch, face, and monitor our property and health, warn us during emergencies, and work as personal assistants in multiple situations. AI and HumansThe multiple uses of AI are constantly benefitting humans. From autonomous cars, medical AI devices to robots, technology is taking over human jobs. Strong AI masters the tasks in the way human beings do use certain logical operations. It exists as a theoretical concept, but soon it goes beyond one’s imagination with ‘superintelligence.’ Researches on Artificial IntelligenceAI regards the relationship between man and the intellectual machine. As per the researchers of the project - “AI 100: One Hundred Years Study in Artificial Intelligence” from American universities and research institutions, AI is expected to make great process over the next 15 years in the field of business, medicine, transport, education, and everyday life. The AI advances can be seen as:The self-driving test vehicle of Google drove successfully drove more than 500 kilometers without having an accident.Robots helping in the household work are now providing security, clean buildings, acting as caretakers, and babysitters. AI-based latest medical devices are helping patients with successfully diagnosing diseases and supporting doctors during operations. AI has improved education processes. The speech recognition technologies help to develop language skills during complex simulation systems, especially for scientific courses and enrich the process of learning. AI Identifies Dangers and Encourages ProgressHuman history displays unstoppable progress. In the coming future, we will be more and more surrounded by machines. The only thing needed would be efficient cooperation between science and the economy to promote AI development. If we have realized this, sooner or later, we will definitely get the chance to resolve the problems like world hunger, poverty, and overpopulation. Hence, AI is definitely ruling the world and will do so in the future as well.
Artificial Intelligence and Machine Learning technologies have found their way to our everyday lives. The recent COVID-19 pandemic has encouraged digital transactions and transformations of our official and personal activities. AI-enabled sources have become the important components of our household applications, electronic goods, smartphones, cars, etc. The best thing is they can recognize our voice, touch, face, and monitor our property and health, warn us during emergencies, and work as personal assistants in multiple situations. AI and HumansThe multiple uses of AI are constantly benefitting humans. From autonomous cars, medical AI devices to robots, technology is taking over human jobs. Strong AI masters the tasks in the way human beings do use certain logical operations. It exists as a theoretical concept, but soon it goes beyond one’s imagination with ‘superintelligence.’ Researches on Artificial IntelligenceAI regards the relationship between man and the intellectual machine. As per the researchers of the project - “AI 100: One Hundred Years Study in Artificial Intelligence” from American universities and research institutions, AI is expected to make great process over the next 15 years in the field of business, medicine, transport, education, and everyday life. The AI advances can be seen as:The self-driving test vehicle of Google drove successfully drove more than 500 kilometers without having an accident.Robots helping in the household work are now providing security, clean buildings, acting as caretakers, and babysitters. AI-based latest medical devices are helping patients with successfully diagnosing diseases and supporting doctors during operations. AI has improved education processes. The speech recognition technologies help to develop language skills during complex simulation systems, especially for scientific courses and enrich the process of learning. AI Identifies Dangers and Encourages ProgressHuman history displays unstoppable progress. In the coming future, we will be more and more surrounded by machines. The only thing needed would be efficient cooperation between science and the economy to promote AI development. If we have realized this, sooner or later, we will definitely get the chance to resolve the problems like world hunger, poverty, and overpopulation. Hence, AI is definitely ruling the world and will do so in the future as well.

Artificial Intelligence and Machine Learning technologies have found their way to our everyday lives. The recent COVID-19 pandemic has encouraged digital transactions and transformations of our official and personal activities. AI-enabled sources have become the important components of our household applications, electronic goods, smartphones, cars, etc. The best thing is they can recognize our voice, touch, face, and monitor our property and health, warn us during emergencies, and work as personal assistants in multiple situations. 

AI and Humans

The multiple uses of AI are constantly benefitting humans. From autonomous cars, medical AI devices to robots, technology is taking over human jobs. Strong AI masters the tasks in the way human beings do use certain logical operations. It exists as a theoretical concept, but soon it goes beyond one’s imagination with ‘superintelligence.’ 

Researches on Artificial Intelligence

AI regards the relationship between man and the intellectual machine. As per the researchers of the project - “AI 100: One Hundred Years Study in Artificial Intelligence” from American universities and research institutions, AI is expected to make great process over the next 15 years in the field of business, medicine, transport, education, and everyday life. The AI advances can be seen as:

  • The self-driving test vehicle of Google drove successfully drove more than 500 kilometers without having an accident.
  • Robots helping in the household work are now providing security, clean buildings, acting as caretakers, and babysitters. 
  • AI-based latest medical devices are helping patients with successfully diagnosing diseases and supporting doctors during operations. 
  • AI has improved education processes. The speech recognition technologies help to develop language skills during complex simulation systems, especially for scientific courses and enrich the process of learning. 

AI Identifies Dangers and Encourages Progress

Human history displays unstoppable progress. In the coming future, we will be more and more surrounded by machines. The only thing needed would be efficient cooperation between science and the economy to promote AI development. If we have realized this, sooner or later, we will definitely get the chance to resolve the problems like world hunger, poverty, and overpopulation. Hence, AI is definitely ruling the world and will do so in the future as well.

In the programming world, there is a well-known phrase - "choosing the right IDE can make you or break you as a coder." So what is an IDE?    What is an IDE   The full form of IDE is Integrated Development Environment. Programmers use IDE to write, compile, and test code. In simple words, you can think of IDE as a Word document or Photoshop tool designed for developers to write code. Just as word document provides a set of tools to write and format documents, IDE also comes with features that help programmers write code seamlessly and correct the errors.    Programmers can write code in regular notepad editor, but it has a limitation. As application development progresses, the code becomes complex and it is difficult to detect an error in notepad. This is where IDE is helpful.     IDE does all the heavy-lifting in terms of compiling code and identifying the errors. The IDE can be a standalone application, or it could be a part of one or more existing and compatible applications.    Some IDE’s are language-specific, while few supports multiple languages. Example of language-specific IDE includes IntelliJ IDEA for JAVA, PyCharm for Python, Zend Studio for PHP and so on, while Eclipse, NetBeans, Aptana supports multiple languages. There are plugins available for language-specific IDE to extend their support for multiple languages,    Whereas, the web-based IDEs like Shiftedit and CodeRun runs on any browser.    These IDE’s are useful if the programmer is working remotely or need a last-minute fix. Also, one cannot ignore the fact that more and more monolithic application is shifting towards cloud-native application. So the rise of cloud-based IDE is inevitable. As each IDE provider competes hard to bring cloud benefits, it is not at all surprising to see Eclipse and IntelliJ sitting on the top of the Grid.    ( Image source: i-programmer.info)    Let’s compare these two popular IDEs, Eclipse vs. IntelliJ   List of other popular IDEs that developers would like to consider for their projects:       Netbeans   Visual Studio   Webstorm   Pycharm   Jdeveloper   Rubymine   Clion    Wrapping up: Both IDEs are great and mature products; the choice is solely based on user requirements and preferences. However, there are few advantages that IntelliJ gives like it allows you to quickly and easily write and change the code, suggests appropriate names, finds the appropriate methods. Eclipse, on the other hand, has richer project support and memory-efficient compared to IntelliJ Idea.    The experts are inclined towards long-time veteran Eclipse but also acknowledges the rising popularity of IntelliJ. The mixed opinions on IDEs leave nobody as the clear winner here and it seems the debate might not end soon.
In the programming world, there is a well-known phrase - "choosing the right IDE can make you or break you as a coder." So what is an IDE?    What is an IDE   The full form of IDE is Integrated Development Environment. Programmers use IDE to write, compile, and test code. In simple words, you can think of IDE as a Word document or Photoshop tool designed for developers to write code. Just as word document provides a set of tools to write and format documents, IDE also comes with features that help programmers write code seamlessly and correct the errors.    Programmers can write code in regular notepad editor, but it has a limitation. As application development progresses, the code becomes complex and it is difficult to detect an error in notepad. This is where IDE is helpful.     IDE does all the heavy-lifting in terms of compiling code and identifying the errors. The IDE can be a standalone application, or it could be a part of one or more existing and compatible applications.    Some IDE’s are language-specific, while few supports multiple languages. Example of language-specific IDE includes IntelliJ IDEA for JAVA, PyCharm for Python, Zend Studio for PHP and so on, while Eclipse, NetBeans, Aptana supports multiple languages. There are plugins available for language-specific IDE to extend their support for multiple languages,    Whereas, the web-based IDEs like Shiftedit and CodeRun runs on any browser.    These IDE’s are useful if the programmer is working remotely or need a last-minute fix. Also, one cannot ignore the fact that more and more monolithic application is shifting towards cloud-native application. So the rise of cloud-based IDE is inevitable. As each IDE provider competes hard to bring cloud benefits, it is not at all surprising to see Eclipse and IntelliJ sitting on the top of the Grid.    ( Image source: i-programmer.info)    Let’s compare these two popular IDEs, Eclipse vs. IntelliJ   List of other popular IDEs that developers would like to consider for their projects:       Netbeans   Visual Studio   Webstorm   Pycharm   Jdeveloper   Rubymine   Clion    Wrapping up: Both IDEs are great and mature products; the choice is solely based on user requirements and preferences. However, there are few advantages that IntelliJ gives like it allows you to quickly and easily write and change the code, suggests appropriate names, finds the appropriate methods. Eclipse, on the other hand, has richer project support and memory-efficient compared to IntelliJ Idea.    The experts are inclined towards long-time veteran Eclipse but also acknowledges the rising popularity of IntelliJ. The mixed opinions on IDEs leave nobody as the clear winner here and it seems the debate might not end soon.

In the programming world, there is a well-known phrase - "choosing the right IDE can make you or break you as a coder." So what is an IDE?   

What is an IDE  

The full form of IDE is Integrated Development Environment. Programmers use IDE to write, compile, and test code. In simple words, you can think of IDE as a Word document or Photoshop tool designed for developers to write code. Just as word document provides a set of tools to write and format documents, IDE also comes with features that help programmers write code seamlessly and correct the errors.   

Programmers can write code in regular notepad editor, but it has a limitation. As application development progresses, the code becomes complex and it is difficult to detect an error in notepad. This is where IDE is helpful.    

IDE does all the heavy-lifting in terms of compiling code and identifying the errors. The IDE can be a standalone application, or it could be a part of one or more existing and compatible applications.   

Some IDE’s are language-specific, while few supports multiple languages. Example of language-specific IDE includes IntelliJ IDEA for JAVA, PyCharm for Python, Zend Studio for PHP and so on, while Eclipse, NetBeans, Aptana supports multiple languages. There are plugins available for language-specific IDE to extend their support for multiple languages,   

Whereas, the web-based IDEs like Shiftedit and CodeRun runs on any browser.   

These IDE’s are useful if the programmer is working remotely or need a last-minute fix. Also, one cannot ignore the fact that more and more monolithic application is shifting towards cloud-native application. So the rise of cloud-based IDE is inevitable. As each IDE provider competes hard to bring cloud benefits, it is not at all surprising to see Eclipse and IntelliJ sitting on the top of the Grid.   

( Image source: i-programmer.info)   

Let’s compare these two popular IDEs, Eclipse vs. IntelliJ  

List of other popular IDEs that developers would like to consider for their projects:      

Wrapping up: Both IDEs are great and mature products; the choice is solely based on user requirements and preferences. However, there are few advantages that IntelliJ gives like it allows you to quickly and easily write and change the code, suggests appropriate names, finds the appropriate methods. Eclipse, on the other hand, has richer project support and memory-efficient compared to IntelliJ Idea.   

The experts are inclined towards long-time veteran Eclipse but also acknowledges the rising popularity of IntelliJ. The mixed opinions on IDEs leave nobody as the clear winner here and it seems the debate might not end soon.

The telecommunication landscape in our generation is vast and demanding. The service providers need to deal with many connectivity points to provide fast and reliable services to their customers. With the increase in IoT and excessive use of wireless sensor networks, telecom firms find it challenging to manage boatloads data.There are various ways in which BI technology can help the telecommunication industry listed below:Visibility into Network Issues and Location DataTelecom providers have to work on streaming high-quality video and particular time frames. As they have to work with substantial data usage, getting an exact historical scope of network errors and the details about the service interruptions to help telecom providers to strengthen particular locales. Moreover, the much-anticipated rollout of 5G demands telecom providers to monitor their networks. Business Intelligence platforms like ThoughtSpot analyses display the most common types of service interruption in the company. Additionally, it provides information about the areas that experience interruption most often or even the devices most likely to encounter issues.Proactive Customer CareThe amount of digital content created every other day demands more devices to consume, which ultimately means a greater reliance on quality service. But customer complaints have always been constant irrespective of the technology evolution. This leads to issues that need to be resolved time and ahead to reduce customer waiting times and unsatisfactory support. When a large amount of data is presented so clearly, customer behavior also becomes easier to grasp. Customer behavior can also be easily analyzed by comparing service outages on a particular day by customer segment or devices. Customer care executives can also enhance their services by searching for the most common complaints, finding stats on the allegations that waited on the phone longest about, or even identifying support questions by email that took the longest to respond.Understanding of Subscriber BaseBI technology provides a platform to the telecom companies that have a robust subscriber base. This platform helps the customer service reps to understand better customers, opportunities for cross/upselling, and more personalized customer service experiences. A search and AI-powered telecom BI tool can provide all the information from granular subscription-by-subscription insights to long-term trends by region or subscriber growth rates. These tools also contain data such as by zip code while pointing out data anomalies and drawing causal relationships that are moving a business forward.IoT Data MonetizationThe IoT industry continues to expand and accelerate. By 2020, the IoT industry is estimated to expand upto 30 billion objects. The telecom companies are also making up for diminishing data revenue by developing IoT partnerships. For telecom companies to subscribe to IoT financially, vast amounts of data collected must be accessible, on-time, and easily acceptable.Hence, if utilized accurately, business intelligence can help the telecommunication industry by the ways mentioned above and many more.
The telecommunication landscape in our generation is vast and demanding. The service providers need to deal with many connectivity points to provide fast and reliable services to their customers. With the increase in IoT and excessive use of wireless sensor networks, telecom firms find it challenging to manage boatloads data.There are various ways in which BI technology can help the telecommunication industry listed below:Visibility into Network Issues and Location DataTelecom providers have to work on streaming high-quality video and particular time frames. As they have to work with substantial data usage, getting an exact historical scope of network errors and the details about the service interruptions to help telecom providers to strengthen particular locales. Moreover, the much-anticipated rollout of 5G demands telecom providers to monitor their networks. Business Intelligence platforms like ThoughtSpot analyses display the most common types of service interruption in the company. Additionally, it provides information about the areas that experience interruption most often or even the devices most likely to encounter issues.Proactive Customer CareThe amount of digital content created every other day demands more devices to consume, which ultimately means a greater reliance on quality service. But customer complaints have always been constant irrespective of the technology evolution. This leads to issues that need to be resolved time and ahead to reduce customer waiting times and unsatisfactory support. When a large amount of data is presented so clearly, customer behavior also becomes easier to grasp. Customer behavior can also be easily analyzed by comparing service outages on a particular day by customer segment or devices. Customer care executives can also enhance their services by searching for the most common complaints, finding stats on the allegations that waited on the phone longest about, or even identifying support questions by email that took the longest to respond.Understanding of Subscriber BaseBI technology provides a platform to the telecom companies that have a robust subscriber base. This platform helps the customer service reps to understand better customers, opportunities for cross/upselling, and more personalized customer service experiences. A search and AI-powered telecom BI tool can provide all the information from granular subscription-by-subscription insights to long-term trends by region or subscriber growth rates. These tools also contain data such as by zip code while pointing out data anomalies and drawing causal relationships that are moving a business forward.IoT Data MonetizationThe IoT industry continues to expand and accelerate. By 2020, the IoT industry is estimated to expand upto 30 billion objects. The telecom companies are also making up for diminishing data revenue by developing IoT partnerships. For telecom companies to subscribe to IoT financially, vast amounts of data collected must be accessible, on-time, and easily acceptable.Hence, if utilized accurately, business intelligence can help the telecommunication industry by the ways mentioned above and many more.

The telecommunication landscape in our generation is vast and demanding. The service providers need to deal with many connectivity points to provide fast and reliable services to their customers. With the increase in IoT and excessive use of wireless sensor networks, telecom firms find it challenging to manage boatloads data.

There are various ways in which BI technology can help the telecommunication industry listed below:

Visibility into Network Issues and Location Data

Telecom providers have to work on streaming high-quality video and particular time frames. As they have to work with substantial data usage, getting an exact historical scope of network errors and the details about the service interruptions to help telecom providers to strengthen particular locales. Moreover, the much-anticipated rollout of 5G demands telecom providers to monitor their networks. Business Intelligence platforms like ThoughtSpot analyses display the most common types of service interruption in the company. Additionally, it provides information about the areas that experience interruption most often or even the devices most likely to encounter issues.

Proactive Customer Care

The amount of digital content created every other day demands more devices to consume, which ultimately means a greater reliance on quality service. But customer complaints have always been constant irrespective of the technology evolution. This leads to issues that need to be resolved time and ahead to reduce customer waiting times and unsatisfactory support. When a large amount of data is presented so clearly, customer behavior also becomes easier to grasp. Customer behavior can also be easily analyzed by comparing service outages on a particular day by customer segment or devices. Customer care executives can also enhance their services by searching for the most common complaints, finding stats on the allegations that waited on the phone longest about, or even identifying support questions by email that took the longest to respond.

Understanding of Subscriber Base

BI technology provides a platform to the telecom companies that have a robust subscriber base. This platform helps the customer service reps to understand better customers, opportunities for cross/upselling, and more personalized customer service experiences. A search and AI-powered telecom BI tool can provide all the information from granular subscription-by-subscription insights to long-term trends by region or subscriber growth rates. These tools also contain data such as by zip code while pointing out data anomalies and drawing causal relationships that are moving a business forward.

IoT Data Monetization

The IoT industry continues to expand and accelerate. By 2020, the IoT industry is estimated to expand upto 30 billion objects. The telecom companies are also making up for diminishing data revenue by developing IoT partnerships. For telecom companies to subscribe to IoT financially, vast amounts of data collected must be accessible, on-time, and easily acceptable.

Hence, if utilized accurately, business intelligence can help the telecommunication industry by the ways mentioned above and many more.

I would have phrased the question better. Will try to provide short answers to all questions part by part from my perspective:Where is BI going? Short answer: BI is going places, and gong there fast. In my 2010 BI model, we valued the BI software market at about $7.2 billion, growing at a global rate of 6.5% on an average, which is significant compared to more established enterprise technologies. I expect BI to increasingly make an entry into iel smith emerging markets, especially China, India, and Latin America. @Unnati Chauhan BI is going to every single enterprise application and delivering more value than the sum of parts. With Ent. Search, it is helping answer casual user queries. With BPM, it is providing perspective to CEP. What are the biggest problems with the existing established players, and how are startups trying to disrupt them?As with all establishments, the problems are of sustainable growth, keeping BI relevant to the needs of a changing demographic of end-users, being on the forefront of business issues, and noticing trends. Some of the common issues are:    Dealing with semi-structured data, and how to include the same in analysis  How to best use user-generated social content  How to deal with the sheer growth in the volume of enterprise and social data  How to better integrate into other information management technologies and enterprise applications  How to come as close to real-time (right-time, if you will) as required  How to deal with Big Data   This is obviously not an exhaustive list.Startups: One very successful startup that came into prominence in the last few years is Qliktech, which defined a radical approach to data analysis doing away with OLAP cubes. Some other companies are trying to come up with newer ways of data visualization. Some such as Jasper and Pentaho are open-source representations of BI. Newer players have BIRT as a starting point, so building a solution becomes less cumbersome. Still others are innovating with in-memory, in-database, MPP driven architectures and analytical databases.
I would have phrased the question better. Will try to provide short answers to all questions part by part from my perspective:Where is BI going? Short answer: BI is going places, and gong there fast. In my 2010 BI model, we valued the BI software market at about $7.2 billion, growing at a global rate of 6.5% on an average, which is significant compared to more established enterprise technologies. I expect BI to increasingly make an entry into iel smith emerging markets, especially China, India, and Latin America. @Unnati Chauhan BI is going to every single enterprise application and delivering more value than the sum of parts. With Ent. Search, it is helping answer casual user queries. With BPM, it is providing perspective to CEP. What are the biggest problems with the existing established players, and how are startups trying to disrupt them?As with all establishments, the problems are of sustainable growth, keeping BI relevant to the needs of a changing demographic of end-users, being on the forefront of business issues, and noticing trends. Some of the common issues are:    Dealing with semi-structured data, and how to include the same in analysis  How to best use user-generated social content  How to deal with the sheer growth in the volume of enterprise and social data  How to better integrate into other information management technologies and enterprise applications  How to come as close to real-time (right-time, if you will) as required  How to deal with Big Data   This is obviously not an exhaustive list.Startups: One very successful startup that came into prominence in the last few years is Qliktech, which defined a radical approach to data analysis doing away with OLAP cubes. Some other companies are trying to come up with newer ways of data visualization. Some such as Jasper and Pentaho are open-source representations of BI. Newer players have BIRT as a starting point, so building a solution becomes less cumbersome. Still others are innovating with in-memory, in-database, MPP driven architectures and analytical databases.

I would have phrased the question better. Will try to provide short answers to all questions part by part from my perspective:

Where is BI going? Short answer: BI is going places, and gong there fast. In my 2010 BI model, we valued the BI software market at about $7.2 billion, growing at a global rate of 6.5% on an average, which is significant compared to more established enterprise technologies. I expect BI to increasingly make an entry into iel smith emerging markets, especially China, India, and Latin America. @Unnati Chauhan
BI is going to every single enterprise application and delivering more value than the sum of parts. With Ent. Search, it is helping answer casual user queries. With BPM, it is providing perspective to CEP.

What are the biggest problems with the existing established players, and how are startups trying to disrupt them?
As with all establishments, the problems are of sustainable growth, keeping BI relevant to the needs of a changing demographic of end-users, being on the forefront of business issues, and noticing trends. Some of the common issues are:  

  1.  Dealing with semi-structured data, and how to include the same in analysis 
  2. How to best use user-generated social content 
  3. How to deal with the sheer growth in the volume of enterprise and social data 
  4. How to better integrate into other information management technologies and enterprise applications 
  5. How to come as close to real-time (right-time, if you will) as required 
  6. How to deal with Big Data  

This is obviously not an exhaustive list.

Startups: One very successful startup that came into prominence in the last few years is Qliktech, which defined a radical approach to data analysis doing away with OLAP cubes. Some other companies are trying to come up with newer ways of data visualization. Some such as Jasper and Pentaho are open-source representations of BI. Newer players have BIRT as a starting point, so building a solution becomes less cumbersome. Still others are innovating with in-memory, in-database, MPP driven architectures and analytical databases.

If you are looking for best AI software, then make sure you consider following points in mind: More user-friendly Open-source software Available for Windows, Linux, Mac platforms Free software Contains libraries in python that makes it handy to develop machine learning and artificial intelligent systems To learn Python for free, huge resources should be available on the internet
If you are looking for best AI software, then make sure you consider following points in mind: More user-friendly Open-source software Available for Windows, Linux, Mac platforms Free software Contains libraries in python that makes it handy to develop machine learning and artificial intelligent systems To learn Python for free, huge resources should be available on the internet

If you are looking for best AI software, then make sure you consider following points in mind:

More user-friendly

Open-source software

Available for Windows, Linux, Mac platforms

Free software

Contains libraries in python that makes it handy to develop machine learning and artificial intelligent systems

To learn Python for free, huge resources should be available on the internet

Business Intelligence is the technology that includes a constant and detailed analysis of the factors responsible for organizational success. For instance, consider a situation where your marketing team may produce reports that provide the in-depth individual ROI of every social media campaign they pursue. Most effective BI evaluations break down these reports even further. By selecting the right performance indicators, or KPIs, you can derive clear and detailed insights from general operating data.In simple terms, the team of social media experts in your company mayindividually track KPIs to keep track of the number of times people comment on posts or follow links to your homepage. These bearings result in various rates of conversions and purchases, to provide you a more accurate picture of what's worth your effort. Business intelligence works efficiently to provide you all the minor and major details about these marketing strategies and implement effective controller services.The relevance of Business Intelligence with Accounting Technology and MethodologyBusiness intelligence can positively influence accounting processes. Tracking factors responsible for business processes can reveal trends that impact your business. For instance, an industrial manufacturer that maintains detailed logs of their production lines every month may gain insights into operating expenses such as utility payments. Now, if they continue this process further and plot these numbers against the frequency and cost of maintenance actions, they might take charge of expenditures by redefining their factory and service-call schedules.BI technology can also provide business leaders with more significant accounting and translational practices. Business Intelligence software tools accumulate and organize large volumes of data, so they rely on reporting, dashboards, and layout elements to make sense of the processes taking place in the organization. Digital Marketing teams implementing these tools customize and modify these accounting processes to enhance better understanding. As your business expands, having a firm knowledge of BI technology helps manage complex data in bookkeeping tasks.How To Implement BI techniques for AccountingKey performance indicators work well with production line managers, retail sales staff, and internet marketing teams. Accounting departments implement key performance indicators to measure performance, maintain trends-insights, and find reasonable solutions to current problems.BI technology helps in accumulating data and provides information about the time taken to generate an invoice from the time of its receipt. Moreover, it can also provide information about the total number of errors in the invoice. Hence, BI technology can help your accounting services in identifying critical snags in existing processes.Accounts departments can use KPI tracking to forecast cash flows more precisely by gathering open accounts by people’s age. With the help of BI technology, it becomes easy to track disputes with credit account holders, collection-agency resolutions, and litigation. The reports and accounts thus generated, can provide accurate information on the amount spent on administrative costs. Final Words:Accounting services can use Business Intelligence to quantify their performance. It can also provide information such as time is taken to correct errors and organize various cost-controlling measures work. Hence, it is essential to implement the use of BI in accounting to increase efficiency in your business.
Business Intelligence is the technology that includes a constant and detailed analysis of the factors responsible for organizational success. For instance, consider a situation where your marketing team may produce reports that provide the in-depth individual ROI of every social media campaign they pursue. Most effective BI evaluations break down these reports even further. By selecting the right performance indicators, or KPIs, you can derive clear and detailed insights from general operating data.In simple terms, the team of social media experts in your company mayindividually track KPIs to keep track of the number of times people comment on posts or follow links to your homepage. These bearings result in various rates of conversions and purchases, to provide you a more accurate picture of what's worth your effort. Business intelligence works efficiently to provide you all the minor and major details about these marketing strategies and implement effective controller services.The relevance of Business Intelligence with Accounting Technology and MethodologyBusiness intelligence can positively influence accounting processes. Tracking factors responsible for business processes can reveal trends that impact your business. For instance, an industrial manufacturer that maintains detailed logs of their production lines every month may gain insights into operating expenses such as utility payments. Now, if they continue this process further and plot these numbers against the frequency and cost of maintenance actions, they might take charge of expenditures by redefining their factory and service-call schedules.BI technology can also provide business leaders with more significant accounting and translational practices. Business Intelligence software tools accumulate and organize large volumes of data, so they rely on reporting, dashboards, and layout elements to make sense of the processes taking place in the organization. Digital Marketing teams implementing these tools customize and modify these accounting processes to enhance better understanding. As your business expands, having a firm knowledge of BI technology helps manage complex data in bookkeeping tasks.How To Implement BI techniques for AccountingKey performance indicators work well with production line managers, retail sales staff, and internet marketing teams. Accounting departments implement key performance indicators to measure performance, maintain trends-insights, and find reasonable solutions to current problems.BI technology helps in accumulating data and provides information about the time taken to generate an invoice from the time of its receipt. Moreover, it can also provide information about the total number of errors in the invoice. Hence, BI technology can help your accounting services in identifying critical snags in existing processes.Accounts departments can use KPI tracking to forecast cash flows more precisely by gathering open accounts by people’s age. With the help of BI technology, it becomes easy to track disputes with credit account holders, collection-agency resolutions, and litigation. The reports and accounts thus generated, can provide accurate information on the amount spent on administrative costs. Final Words:Accounting services can use Business Intelligence to quantify their performance. It can also provide information such as time is taken to correct errors and organize various cost-controlling measures work. Hence, it is essential to implement the use of BI in accounting to increase efficiency in your business.

Business Intelligence is the technology that includes a constant and detailed analysis of the factors responsible for organizational success. For instance, consider a situation where your marketing team may produce reports that provide the in-depth individual ROI of every social media campaign they pursue. Most effective BI evaluations break down these reports even further. By selecting the right performance indicators, or KPIs, you can derive clear and detailed insights from general operating data.

In simple terms, the team of social media experts in your company may

individually track KPIs to keep track of the number of times people comment on posts or follow links to your homepage. These bearings result in various rates of conversions and purchases, to provide you a more accurate picture of what's worth your effort. Business intelligence works efficiently to provide you all the minor and major details about these marketing strategies and implement effective controller services.

The relevance of Business Intelligence with Accounting Technology and Methodology

Business intelligence can positively influence accounting processes. Tracking factors responsible for business processes can reveal trends that impact your business. For instance, an industrial manufacturer that maintains detailed logs of their production lines every month may gain insights into operating expenses such as utility payments. Now, if they continue this process further and plot these numbers against the frequency and cost of maintenance actions, they might take charge of expenditures by redefining their factory and service-call schedules.

BI technology can also provide business leaders with more significant accounting and translational practices. Business Intelligence software tools accumulate and organize large volumes of data, so they rely on reporting, dashboards, and layout elements to make sense of the processes taking place in the organization. Digital Marketing teams implementing these tools customize and modify these accounting processes to enhance better understanding. As your business expands, having a firm knowledge of BI technology helps manage complex data in bookkeeping tasks.

How To Implement BI techniques for Accounting

Key performance indicators work well with production line managers, retail sales staff, and internet marketing teams. Accounting departments implement key performance indicators to measure performance, maintain trends-insights, and find reasonable solutions to current problems.

BI technology helps in accumulating data and provides information about the time taken to generate an invoice from the time of its receipt. Moreover, it can also provide information about the total number of errors in the invoice. Hence, BI technology can help your accounting services in identifying critical snags in existing processes.

Accounts departments can use KPI tracking to forecast cash flows more precisely by gathering open accounts by people’s age. With the help of BI technology, it becomes easy to track disputes with credit account holders, collection-agency resolutions, and litigation. The reports and accounts thus generated, can provide accurate information on the amount spent on administrative costs. 

Final Words:

Accounting services can use Business Intelligence to quantify their performance. It can also provide information such as time is taken to correct errors and organize various cost-controlling measures work. Hence, it is essential to implement the use of BI in accounting to increase efficiency in your business.

As per one of the reports, in 2020, the enterprise-level companies would be shelling out between $6000 to $300,000 for AI solutions. The hotel industry will match the spending, more or less.  For third party software like a pre-built chatbot, it could cost you around $40,000 per year.  ( Image Source: webfx.com)  IBM Watson for Hotel Sector  As far as IBM Watson is concerned, many factors determine the AI cost. You may consider the following AI solutions to include in your hotel business-like:   Chatbots  Analysis systems  Virtual assistants for customers  Text-based personal assistant  IoT integration for automated room services   However, there is no accurate estimation for AI solution because it all boils down to your requirements and preferences. The best way is to reach out to IBM customer support services.  If you are wondering why there is no clear-cut cost estimation of IBM Watson for the hotel sector and why I am telling you to reach IBM customer support services, here are a few things to note.  IBM Watson is quite a broad term to estimate the cost of the hotel services. You can build models from scratch, or use their APIs and pre-trained business solutions. It could include IoT-enabled connections of motion sensors, smart voice control, facial recognition, and myriad other things. That may require you to pick either one of the below technology stack or even all of them. The services listed are just half of them; there are a few more.  ( Image source: ibm.com)  Once you have the stack of technology, you want to implement, the next step is to approach the developer to integrate them into your business model. If you prefer an in-house custom solution then remember there is more cost to add. The data scientist’s salary is around $90,000/year.  Besides AI solution development, the developers may have to see like what number of API calls you will make, and your data storage needs. In the end, this will add to the AI solution cost. It will become easier to estimate cost if you know the architecture and scale of the AI solution you are building.  Few examples for IBM Watson AI development stack  Watson Studio  The IBM Watson Studio provides a suite of tools for data scientists and developers to collaboratively connect to data, wrangle that data, and use it to build, train and deploy advanced business models. For example, an accident repair car company used facial recognition technology to its website’s booking page. It visually analyzes the customer photos of vehicle damage and automatically recommends a repair price. The hotel industry can explore Watson Studio (facial recognition, machine learning, etc.) in many ways.  To develop your AI solution, IBM Watson gives the option of free trial for their desktop version or cloud service for free. Below is the table for the Watson studio price plan for the cloud version.  ( Image source: ibm.com)  Watson’s Assistant  For your hotel business, whether you want a room temperature manager that cuts the utility cost or an AI-based robot-like Connie to improve concierge services you just need an AI assistant. It can turn any space – lobby, guest, or conference room -- into a smart space. Here is the price plan for Watson’s Assistant. Here we have just listed down two of the Watson AI function(Watson Studio & Watson Assistant) to get a fair idea about IBM Watson.  As we mentioned above, it is compiled of many such products and services. The cost of your hotel AI largely depends on what services you are choosing from the list. By selecting them wisely, you can transform your hotel business or any business into a smart AI-based model without exceeding the budget.  For hoteliers, some of the above details may sound technical as they are less inclined to the development side. But if they are serious about integrating AI into the hotel sector, this post can help them to narrow down their budget for AI solutions and get their work done.
As per one of the reports, in 2020, the enterprise-level companies would be shelling out between $6000 to $300,000 for AI solutions. The hotel industry will match the spending, more or less.  For third party software like a pre-built chatbot, it could cost you around $40,000 per year.  ( Image Source: webfx.com)  IBM Watson for Hotel Sector  As far as IBM Watson is concerned, many factors determine the AI cost. You may consider the following AI solutions to include in your hotel business-like:   Chatbots  Analysis systems  Virtual assistants for customers  Text-based personal assistant  IoT integration for automated room services   However, there is no accurate estimation for AI solution because it all boils down to your requirements and preferences. The best way is to reach out to IBM customer support services.  If you are wondering why there is no clear-cut cost estimation of IBM Watson for the hotel sector and why I am telling you to reach IBM customer support services, here are a few things to note.  IBM Watson is quite a broad term to estimate the cost of the hotel services. You can build models from scratch, or use their APIs and pre-trained business solutions. It could include IoT-enabled connections of motion sensors, smart voice control, facial recognition, and myriad other things. That may require you to pick either one of the below technology stack or even all of them. The services listed are just half of them; there are a few more.  ( Image source: ibm.com)  Once you have the stack of technology, you want to implement, the next step is to approach the developer to integrate them into your business model. If you prefer an in-house custom solution then remember there is more cost to add. The data scientist’s salary is around $90,000/year.  Besides AI solution development, the developers may have to see like what number of API calls you will make, and your data storage needs. In the end, this will add to the AI solution cost. It will become easier to estimate cost if you know the architecture and scale of the AI solution you are building.  Few examples for IBM Watson AI development stack  Watson Studio  The IBM Watson Studio provides a suite of tools for data scientists and developers to collaboratively connect to data, wrangle that data, and use it to build, train and deploy advanced business models. For example, an accident repair car company used facial recognition technology to its website’s booking page. It visually analyzes the customer photos of vehicle damage and automatically recommends a repair price. The hotel industry can explore Watson Studio (facial recognition, machine learning, etc.) in many ways.  To develop your AI solution, IBM Watson gives the option of free trial for their desktop version or cloud service for free. Below is the table for the Watson studio price plan for the cloud version.  ( Image source: ibm.com)  Watson’s Assistant  For your hotel business, whether you want a room temperature manager that cuts the utility cost or an AI-based robot-like Connie to improve concierge services you just need an AI assistant. It can turn any space – lobby, guest, or conference room -- into a smart space. Here is the price plan for Watson’s Assistant. Here we have just listed down two of the Watson AI function(Watson Studio & Watson Assistant) to get a fair idea about IBM Watson.  As we mentioned above, it is compiled of many such products and services. The cost of your hotel AI largely depends on what services you are choosing from the list. By selecting them wisely, you can transform your hotel business or any business into a smart AI-based model without exceeding the budget.  For hoteliers, some of the above details may sound technical as they are less inclined to the development side. But if they are serious about integrating AI into the hotel sector, this post can help them to narrow down their budget for AI solutions and get their work done.

As per one of the reports, in 2020, the enterprise-level companies would be shelling out between $6000 to $300,000 for AI solutions. The hotel industry will match the spending, more or less. 

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For third party software like a pre-built chatbot, it could cost you around $40,000 per year. 

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( Image Source: webfx.com) 

IBM Watson for Hotel Sector 

As far as IBM Watson is concerned, many factors determine the AI cost. You may consider the following AI solutions to include in your hotel business-like:  

  • Chatbots 
  • Analysis systems 
  • Virtual assistants for customers 
  • Text-based personal assistant 
  • IoT integration for automated room services  

However, there is no accurate estimation for AI solution because it all boils down to your requirements and preferences. The best way is to reach out to IBM customer support services

If you are wondering why there is no clear-cut cost estimation of IBM Watson for the hotel sector and why I am telling you to reach IBM customer support services, here are a few things to note. 

IBM Watson is quite a broad term to estimate the cost of the hotel services. You can build models from scratch, or use their APIs and pre-trained business solutions. It could include IoT-enabled connections of motion sensors, smart voice control, facial recognition, and myriad other things. That may require you to pick either one of the below technology stack or even all of them. The services listed are just half of them; there are a few more. 

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( Image source: ibm.com) 

Once you have the stack of technology, you want to implement, the next step is to approach the developer to integrate them into your business model. If you prefer an in-house custom solution then remember there is more cost to add. The data scientist’s salary is around $90,000/year

Besides AI solution development, the developers may have to see like what number of API calls you will make, and your data storage needs. In the end, this will add to the AI solution cost. It will become easier to estimate cost if you know the architecture and scale of the AI solution you are building. 

Few examples for IBM Watson AI development stack 

Watson Studio 

The IBM Watson Studio provides a suite of tools for data scientists and developers to collaboratively connect to data, wrangle that data, and use it to build, train and deploy advanced business models. For example, an accident repair car company used facial recognition technology to its website’s booking page. It visually analyzes the customer photos of vehicle damage and automatically recommends a repair price. The hotel industry can explore Watson Studio (facial recognition, machine learning, etc.) in many ways. 

To develop your AI solution, IBM Watson gives the option of free trial for their desktop version or cloud service for free. Below is the table for the Watson studio price plan for the cloud version. 

undefined

( Image source: ibm.com) 

Watson’s Assistant 

For your hotel business, whether you want a room temperature manager that cuts the utility cost or an AI-based robot-like Connie to improve concierge services you just need an AI assistant. It can turn any space – lobby, guest, or conference room -- into a smart space. Here is the price plan for Watson’s Assistant.

undefined

Here we have just listed down two of the Watson AI function(Watson Studio & Watson Assistant) to get a fair idea about IBM Watson. 

As we mentioned above, it is compiled of many such products and services. The cost of your hotel AI largely depends on what services you are choosing from the list. By selecting them wisely, you can transform your hotel business or any business into a smart AI-based model without exceeding the budget. 

For hoteliers, some of the above details may sound technical as they are less inclined to the development side. But if they are serious about integrating AI into the hotel sector, this post can help them to narrow down their budget for AI solutions and get their work done.

Artificial intelligence indeed has ample of career options and opportunities for a serious aspirant! It is the rising demand for Artificial Intelligence that has given a steep rise to more diverse AI career paths. (AI) has come to define society today in ways we never anticipated. In recent years, careers in artificial intelligence (AI) have grown exponentially to fulfill the demands of digitally transformed industries. Artificial intelligence (AI) promises to deliver some of the most significant and disruptive innovations of this century.Self-driving cars, robotic assistants, and automated disease diagnosis are all products of an emerging AI revolution that will reshape how we live and work. Jobs in the artificial intelligence industry are expected to increase by 2.3 million positions by the year 2030 and 83% of companies that implement AI technologies confirm that AI is already contributing to the creation of new jobs. Artificial intelligence is being used for a variety of applications to make computers smarter. With the demand for talented engineers more than doubling in the last few years, there are limitless opportunities for professionals who want to work on the cutting edge of AI research and development. If careers in artificial intelligence appeal to you and you want a piece of the AI pie, what kind of jobs should you start looking for? What skills would you need to get hired? Let’s take a closer look.Machine Learning ResearcherThis position entails researching to improve robotics and machine learning and working to make algorithms more effective. It will sometimes be required to obtain a master’s degree or a Ph.D. in a field associated with STEAM, including a degree or certification in AI.Data AnalystData analysts need to possess a sound understanding of the data itself, including the practices of managing, analyzing, and storing it as well as the skills needed to communicate findings through visualization. Those looking to excel in the field of analytics as it relates to AI and machine learning should consider either a master’s degree in analytics or a master’s degree in computer science with a specialization in data science.Natural Language Processing DeveloperFrom chatbots to virtual assistants to predictive texting on smartphones, AI tools have been utilized to replicate human speech in a variety of formats. To do this effectively, developers call upon the knowledge of natural language processors—individuals who have both the language and technology skills needed to assist in the creation of these tools. National defense servicesAn artificial intelligence degree and an AI certification can also help you attain a position in the armed forces, helping to develop their intelligent technology. You can serve the nation and secure a more decent future with a government Job.Software EngineeringThe AI field also relies on traditional computer science roles such as software engineers to develop the programs on which artificial intelligence tools function.With the vast possibilities available in the arena of AI, one can even become a video game programmer. Artificial intelligence represents a lucrative field with above-average job growth, but the industry remains competitive. Roles in this discipline are extremely niche, requiring both an advanced technical background and extensive practical experience
Artificial intelligence indeed has ample of career options and opportunities for a serious aspirant! It is the rising demand for Artificial Intelligence that has given a steep rise to more diverse AI career paths. (AI) has come to define society today in ways we never anticipated. In recent years, careers in artificial intelligence (AI) have grown exponentially to fulfill the demands of digitally transformed industries. Artificial intelligence (AI) promises to deliver some of the most significant and disruptive innovations of this century.Self-driving cars, robotic assistants, and automated disease diagnosis are all products of an emerging AI revolution that will reshape how we live and work. Jobs in the artificial intelligence industry are expected to increase by 2.3 million positions by the year 2030 and 83% of companies that implement AI technologies confirm that AI is already contributing to the creation of new jobs. Artificial intelligence is being used for a variety of applications to make computers smarter. With the demand for talented engineers more than doubling in the last few years, there are limitless opportunities for professionals who want to work on the cutting edge of AI research and development. If careers in artificial intelligence appeal to you and you want a piece of the AI pie, what kind of jobs should you start looking for? What skills would you need to get hired? Let’s take a closer look.Machine Learning ResearcherThis position entails researching to improve robotics and machine learning and working to make algorithms more effective. It will sometimes be required to obtain a master’s degree or a Ph.D. in a field associated with STEAM, including a degree or certification in AI.Data AnalystData analysts need to possess a sound understanding of the data itself, including the practices of managing, analyzing, and storing it as well as the skills needed to communicate findings through visualization. Those looking to excel in the field of analytics as it relates to AI and machine learning should consider either a master’s degree in analytics or a master’s degree in computer science with a specialization in data science.Natural Language Processing DeveloperFrom chatbots to virtual assistants to predictive texting on smartphones, AI tools have been utilized to replicate human speech in a variety of formats. To do this effectively, developers call upon the knowledge of natural language processors—individuals who have both the language and technology skills needed to assist in the creation of these tools. National defense servicesAn artificial intelligence degree and an AI certification can also help you attain a position in the armed forces, helping to develop their intelligent technology. You can serve the nation and secure a more decent future with a government Job.Software EngineeringThe AI field also relies on traditional computer science roles such as software engineers to develop the programs on which artificial intelligence tools function.With the vast possibilities available in the arena of AI, one can even become a video game programmer. Artificial intelligence represents a lucrative field with above-average job growth, but the industry remains competitive. Roles in this discipline are extremely niche, requiring both an advanced technical background and extensive practical experience

Artificial intelligence indeed has ample of career options and opportunities for a serious aspirant! It is the rising demand for Artificial Intelligence that has given a steep rise to more diverse AI career paths. (AI) has come to define society today in ways we never anticipated. In recent years, careers in artificial intelligence (AI) have grown exponentially to fulfill the demands of digitally transformed industries. Artificial intelligence (AI) promises to deliver some of the most significant and disruptive innovations of this century.

Self-driving cars, robotic assistants, and automated disease diagnosis are all products of an emerging AI revolution that will reshape how we live and work. Jobs in the artificial intelligence industry are expected to increase by 2.3 million positions by the year 2030 and 83% of companies that implement AI technologies confirm that AI is already contributing to the creation of new jobs. Artificial intelligence is being used for a variety of applications to make computers smarter. With the demand for talented engineers more than doubling in the last few years, there are limitless opportunities for professionals who want to work on the cutting edge of AI research and development.

 If careers in artificial intelligence appeal to you and you want a piece of the AI pie, what kind of jobs should you start looking for? What skills would you need to get hired? Let’s take a closer look.

Machine Learning Researcher

This position entails researching to improve robotics and machine learning and working to make algorithms more effective. It will sometimes be required to obtain a master’s degree or a Ph.D. in a field associated with STEAM, including a degree or certification in AI.

Data Analyst

Data analysts need to possess a sound understanding of the data itself, including the practices of managing, analyzing, and storing it as well as the skills needed to communicate findings through visualization. Those looking to excel in the field of analytics as it relates to AI and machine learning should consider either a master’s degree in analytics or a master’s degree in computer science with a specialization in data science.

Natural Language Processing Developer

From chatbots to virtual assistants to predictive texting on smartphones, AI tools have been utilized to replicate human speech in a variety of formats. To do this effectively, developers call upon the knowledge of natural language processors—individuals who have both the language and technology skills needed to assist in the creation of these tools.

 National defense services

An artificial intelligence degree and an AI certification can also help you attain a position in the armed forces, helping to develop their intelligent technology. You can serve the nation and secure a more decent future with a government Job.

Software Engineering

The AI field also relies on traditional computer science roles such as software engineers to develop the programs on which artificial intelligence tools function.

With the vast possibilities available in the arena of AI, one can even become a video game programmer. Artificial intelligence represents a lucrative field with above-average job growth, but the industry remains competitive. Roles in this discipline are extremely niche, requiring both an advanced technical background and extensive practical experience

Learners in 21Gen can call themselves fortunate enough, as they do not have to take years of their life studying at university to become familiar with this extremely complex technology i.e. Artificial Intelligence. A massive sprung up of numerous online courses in recent years covers everything from the basics to advanced implementation. Some people aim at diving straight into coding artificial neural networks assuming they have a certain level of technical ability. Others may want to learn how this technology can be applied, regardless of prior technical expertise to solve real-world problems. Here I am going to tell you how you can learn AI better.Initially, if you are good in mathematics like algebra, calculus, probability, and distributions, things will become easier for you to learn ML and AI. You should have knowledge of programming languages like C++, Python, Java, and R programming language. Being familiar with Machine Learning makes it much better for you to learn AI. You can learn with Google AI as well. It is a newly launched resource and a part of Google’s plan to spread understanding about Artificial Intelligence among the public. Though, Google is slowly adding material to let people learn AI in this regard, some contents related to Machine Learning TensorFlow crash course is already added there.Artificial Intelligence has been driving billions of dollars and rupees in business over the years. It is enabling enormous job opportunities as well. So, now it is the perfect time to learn AI and start implementing AI-based ethics in your career. If you have already started with the basics i.e. from books and blogs, it will be your first step towards orientation. Enrolling into webinars will help you see the world through the eyes of AI. Nowadays, you can also learn AI course on Udemy considering the pandemic scenario when online education seems much better than joining a brick and mortar classroom. The combination of the power of Data Science, Machine Learning, and Deep Learning helps you create powerful AI for real-world applications.  
Learners in 21Gen can call themselves fortunate enough, as they do not have to take years of their life studying at university to become familiar with this extremely complex technology i.e. Artificial Intelligence. A massive sprung up of numerous online courses in recent years covers everything from the basics to advanced implementation. Some people aim at diving straight into coding artificial neural networks assuming they have a certain level of technical ability. Others may want to learn how this technology can be applied, regardless of prior technical expertise to solve real-world problems. Here I am going to tell you how you can learn AI better.Initially, if you are good in mathematics like algebra, calculus, probability, and distributions, things will become easier for you to learn ML and AI. You should have knowledge of programming languages like C++, Python, Java, and R programming language. Being familiar with Machine Learning makes it much better for you to learn AI. You can learn with Google AI as well. It is a newly launched resource and a part of Google’s plan to spread understanding about Artificial Intelligence among the public. Though, Google is slowly adding material to let people learn AI in this regard, some contents related to Machine Learning TensorFlow crash course is already added there.Artificial Intelligence has been driving billions of dollars and rupees in business over the years. It is enabling enormous job opportunities as well. So, now it is the perfect time to learn AI and start implementing AI-based ethics in your career. If you have already started with the basics i.e. from books and blogs, it will be your first step towards orientation. Enrolling into webinars will help you see the world through the eyes of AI. Nowadays, you can also learn AI course on Udemy considering the pandemic scenario when online education seems much better than joining a brick and mortar classroom. The combination of the power of Data Science, Machine Learning, and Deep Learning helps you create powerful AI for real-world applications.  

Learners in 21Gen can call themselves fortunate enough, as they do not have to take years of their life studying at university to become familiar with this extremely complex technology i.e. Artificial Intelligence. A massive sprung up of numerous online courses in recent years covers everything from the basics to advanced implementation. 

Some people aim at diving straight into coding artificial neural networks assuming they have a certain level of technical ability. Others may want to learn how this technology can be applied, regardless of prior technical expertise to solve real-world problems. 

Here I am going to tell you how you can learn AI better.

Initially, if you are good in mathematics like algebra, calculus, probability, and distributions, things will become easier for you to learn ML and AI. You should have knowledge of programming languages like C++, Python, Java, and R programming language. Being familiar with Machine Learning makes it much better for you to learn AI. 

You can learn with Google AI as well. It is a newly launched resource and a part of Google’s plan to spread understanding about Artificial Intelligence among the public. Though, Google is slowly adding material to let people learn AI in this regard, some contents related to Machine Learning TensorFlow crash course is already added there.

Artificial Intelligence has been driving billions of dollars and rupees in business over the years. It is enabling enormous job opportunities as well. So, now it is the perfect time to learn AI and start implementing AI-based ethics in your career. If you have already started with the basics i.e. from books and blogs, it will be your first step towards orientation. Enrolling into webinars will help you see the world through the eyes of AI. 

Nowadays, you can also learn AI course on Udemy considering the pandemic scenario when online education seems much better than joining a brick and mortar classroom. The combination of the power of Data Science, Machine Learning, and Deep Learning helps you create powerful AI for real-world applications.  

Artificial Intelligence or AI is no longer a ‘sci-fi thing’ as it is already adopted by various industries. It is a set of technologies that makes machines smarter over time. It basically makes the computers think intelligently, especially when you are working on a new artificial intelligence project that needs a critical branch of engineering to accomplish. Surprising is to see different kinds of languages, tools and methods used for developing classy AI programs and interesting it is to know about them, indeed. So, let’s have a glance over them to know how they contribute to the exceptional AI programs and what their role is to the world of artificial intelligence. 1. Python:Python contains simple syntaxes and easy to implement AI. It does not take much of your development time in comparison to C++, Ruby, or Java. The best thing of using Python is that it supports functional, object-oriented, and procedure-oriented styles of programming. It facilitates plenty of libraries to make our tasks more manageable. 2. Java: One of the right choices for AI development, Java has to do a lot with artificial neural networks, algorithms, and genetic programming. It offers many benefits like easy usage, package services, debugging ease, simplified work with large-scale projects, user interaction, and better graphical representation of data. It also incorporates Swing and SWT (the Standard Widget Toolkit) to make graphics and interfaces look appealing and sophisticated. 3. R: R offers the most effective production to analyze and manipulate data for statistical usages. It lets you create a well-designed and quality-based publication inclusive of formulas and mathematical symbols. Other than its general usage, R language also offers numerous packages like Gmodels, RODBC, Class, and Tm that are basically the part of machine learning. 4. Lisp: Lisp is regarded as the most suitable language for developing AI-based programs like DART, CYC, and Macsyma. It processes the symbolic information effectively and is also known best for the easy dynamic creation of the new objects and excellent prototyping capabilities with automatic garbage collection. The excellent features this language contains make it most useful for the AI programs. It contains the macro system that enables the developers to develop a domain-specific level of abstraction. 5. Prolog: Alongside Lisp, Prolog is another most used language to build AI projects, especially medical-related projects. Automatic backtracking, tree-based data structuring, and efficient pattern-matching are some of the important features of Prolog that makes the AI programming framework extremely powerful and flexible. Conclusion: If you go into details, you will get to know that all languages have different features to create diversified programs. As per my personal opinion, the cost of Lisp-based development is higher and so the demand for Lisp experts is limited in the market. But you cannot ignore Python as well when you are developing the Machine Learning projects, as it is extensively used in the ML sphere. Similarly, Java is better than Python to develop games, and desktop & mobile applications. So, all languages have their own importance and usage depending upon the type of AI project wherein they are going to be used.
Artificial Intelligence or AI is no longer a ‘sci-fi thing’ as it is already adopted by various industries. It is a set of technologies that makes machines smarter over time. It basically makes the computers think intelligently, especially when you are working on a new artificial intelligence project that needs a critical branch of engineering to accomplish. Surprising is to see different kinds of languages, tools and methods used for developing classy AI programs and interesting it is to know about them, indeed. So, let’s have a glance over them to know how they contribute to the exceptional AI programs and what their role is to the world of artificial intelligence. 1. Python:Python contains simple syntaxes and easy to implement AI. It does not take much of your development time in comparison to C++, Ruby, or Java. The best thing of using Python is that it supports functional, object-oriented, and procedure-oriented styles of programming. It facilitates plenty of libraries to make our tasks more manageable. 2. Java: One of the right choices for AI development, Java has to do a lot with artificial neural networks, algorithms, and genetic programming. It offers many benefits like easy usage, package services, debugging ease, simplified work with large-scale projects, user interaction, and better graphical representation of data. It also incorporates Swing and SWT (the Standard Widget Toolkit) to make graphics and interfaces look appealing and sophisticated. 3. R: R offers the most effective production to analyze and manipulate data for statistical usages. It lets you create a well-designed and quality-based publication inclusive of formulas and mathematical symbols. Other than its general usage, R language also offers numerous packages like Gmodels, RODBC, Class, and Tm that are basically the part of machine learning. 4. Lisp: Lisp is regarded as the most suitable language for developing AI-based programs like DART, CYC, and Macsyma. It processes the symbolic information effectively and is also known best for the easy dynamic creation of the new objects and excellent prototyping capabilities with automatic garbage collection. The excellent features this language contains make it most useful for the AI programs. It contains the macro system that enables the developers to develop a domain-specific level of abstraction. 5. Prolog: Alongside Lisp, Prolog is another most used language to build AI projects, especially medical-related projects. Automatic backtracking, tree-based data structuring, and efficient pattern-matching are some of the important features of Prolog that makes the AI programming framework extremely powerful and flexible. Conclusion: If you go into details, you will get to know that all languages have different features to create diversified programs. As per my personal opinion, the cost of Lisp-based development is higher and so the demand for Lisp experts is limited in the market. But you cannot ignore Python as well when you are developing the Machine Learning projects, as it is extensively used in the ML sphere. Similarly, Java is better than Python to develop games, and desktop & mobile applications. So, all languages have their own importance and usage depending upon the type of AI project wherein they are going to be used.

Artificial Intelligence or AI is no longer a ‘sci-fi thing’ as it is already adopted by various industries. It is a set of technologies that makes machines smarter over time. It basically makes the computers think intelligently, especially when you are working on a new artificial intelligence project that needs a critical branch of engineering to accomplish.

Surprising is to see different kinds of languages, tools and methods used for developing classy AI programs and interesting it is to know about them, indeed. So, let’s have a glance over them to know how they contribute to the exceptional AI programs and what their role is to the world of artificial intelligence.

1. Python:Python contains simple syntaxes and easy to implement AI. It does not take much of your development time in comparison to C++, Ruby, or Java. The best thing of using Python is that it supports functional, object-oriented, and procedure-oriented styles of programming. It facilitates plenty of libraries to make our tasks more manageable.

2. Java: One of the right choices for AI development, Java has to do a lot with artificial neural networks, algorithms, and genetic programming. It offers many benefits like easy usage, package services, debugging ease, simplified work with large-scale projects, user interaction, and better graphical representation of data. It also incorporates Swing and SWT (the Standard Widget Toolkit) to make graphics and interfaces look appealing and sophisticated.

3. R: R offers the most effective production to analyze and manipulate data for statistical usages. It lets you create a well-designed and quality-based publication inclusive of formulas and mathematical symbols. Other than its general usage, R language also offers numerous packages like Gmodels, RODBC, Class, and Tm that are basically the part of machine learning.

4. Lisp: Lisp is regarded as the most suitable language for developing AI-based programs like DART, CYC, and Macsyma. It processes the symbolic information effectively and is also known best for the easy dynamic creation of the new objects and excellent prototyping capabilities with automatic garbage collection. The excellent features this language contains make it most useful for the AI programs. It contains the macro system that enables the developers to develop a domain-specific level of abstraction.

5. Prolog: Alongside Lisp, Prolog is another most used language to build AI projects, especially medical-related projects. Automatic backtracking, tree-based data structuring, and efficient pattern-matching are some of the important features of Prolog that makes the AI programming framework extremely powerful and flexible.

Conclusion:

If you go into details, you will get to know that all languages have different features to create diversified programs. As per my personal opinion, the cost of Lisp-based development is higher and so the demand for Lisp experts is limited in the market. But you cannot ignore Python as well when you are developing the Machine Learning projects, as it is extensively used in the ML sphere. Similarly, Java is better than Python to develop games, and desktop & mobile applications.

So, all languages have their own importance and usage depending upon the type of AI project wherein they are going to be used.

Being the well reckoned terms of computer science, machine learning and artificial intelligence both are the most commonly discussed points by the next generation of IT professionals. Machine learning and Artificial intelligence both are a boon not just for the IT sector but also for various other sectors like robotics and mechatronics. Artificial intelligence and machine learning are two most popular buzzwords making headlines in the market. The two coined terms are many times used interchangeably. They have become a part of mundane life, but that does not mean that we apprehend them well. A lot of confusion still exists between what is machine learning and what is AI! In most of the companies; marketing overlooks this distinction for the sake of advertising and sales.What is Machine Learning?ML stands for Machine Learning which is defined as the acquisition of knowledge or skill. In Machine learning, algorithms acquire knowledge or skill through experience. It relies on big data sets to remind the data to identify conventional patterns. A Machine learning algorithm aims to increase accuracy, where the success of that accuracy is not a primary concern. For example, contemplate a situation where a logically rational decision is less likely to work in a practical environment. In that condition, the machine learning algorithm will still focus on taking the same logically right decision no matter what the results are. The main reason behind such behavior of a Machine Learning algorithm is that it is designed to learn from past data and act on the basis of only logic and nothing else. What is Artificial intelligence?Artificial intelligence, on the other hand, is vast and broader in scope. According to Andrew Moore Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of developing computers that behave in ways that, until recently, we thought required human intelligence.”That is a remarkable way to define AI in a single sentence; however, it still shows how broad the field is. AI stands for Artificial intelligence, where intelligence is defined as the ability to acquire and utilize knowledge. Unlike Machine Learning, an Artificial Intelligence program aims to increase the chance of success and not accuracy. An artificial intelligence program is designed to replicate a human brain in every possible manner. It is no doubt that AI is a broader concept while ML remains as only its subset.Both AI & ML can demonstrate valuable business applications. But ML has got a lot more adoption for solving the critical business problems in many organizations and companies.
Being the well reckoned terms of computer science, machine learning and artificial intelligence both are the most commonly discussed points by the next generation of IT professionals. Machine learning and Artificial intelligence both are a boon not just for the IT sector but also for various other sectors like robotics and mechatronics. Artificial intelligence and machine learning are two most popular buzzwords making headlines in the market. The two coined terms are many times used interchangeably. They have become a part of mundane life, but that does not mean that we apprehend them well. A lot of confusion still exists between what is machine learning and what is AI! In most of the companies; marketing overlooks this distinction for the sake of advertising and sales.What is Machine Learning?ML stands for Machine Learning which is defined as the acquisition of knowledge or skill. In Machine learning, algorithms acquire knowledge or skill through experience. It relies on big data sets to remind the data to identify conventional patterns. A Machine learning algorithm aims to increase accuracy, where the success of that accuracy is not a primary concern. For example, contemplate a situation where a logically rational decision is less likely to work in a practical environment. In that condition, the machine learning algorithm will still focus on taking the same logically right decision no matter what the results are. The main reason behind such behavior of a Machine Learning algorithm is that it is designed to learn from past data and act on the basis of only logic and nothing else. What is Artificial intelligence?Artificial intelligence, on the other hand, is vast and broader in scope. According to Andrew Moore Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of developing computers that behave in ways that, until recently, we thought required human intelligence.”That is a remarkable way to define AI in a single sentence; however, it still shows how broad the field is. AI stands for Artificial intelligence, where intelligence is defined as the ability to acquire and utilize knowledge. Unlike Machine Learning, an Artificial Intelligence program aims to increase the chance of success and not accuracy. An artificial intelligence program is designed to replicate a human brain in every possible manner. It is no doubt that AI is a broader concept while ML remains as only its subset.Both AI & ML can demonstrate valuable business applications. But ML has got a lot more adoption for solving the critical business problems in many organizations and companies.

Being the well reckoned terms of computer science, machine learning and artificial intelligence both are the most commonly discussed points by the next generation of IT professionals. Machine learning and Artificial intelligence both are a boon not just for the IT sector but also for various other sectors like robotics and mechatronics. Artificial intelligence and machine learning are two most popular buzzwords making headlines in the market. The two coined terms are many times used interchangeably. They have become a part of mundane life, but that does not mean that we apprehend them well. A lot of confusion still exists between what is machine learning and what is AI! In most of the companies; marketing overlooks this distinction for the sake of advertising and sales.

What is Machine Learning?

ML stands for Machine Learning which is defined as the acquisition of knowledge or skill. In Machine learning, algorithms acquire knowledge or skill through experience. It relies on big data sets to remind the data to identify conventional patterns. A Machine learning algorithm aims to increase accuracy, where the success of that accuracy is not a primary concern. For example, contemplate a situation where a logically rational decision is less likely to work in a practical environment. In that condition, the machine learning algorithm will still focus on taking the same logically right decision no matter what the results are. The main reason behind such behavior of a Machine Learning algorithm is that it is designed to learn from past data and act on the basis of only logic and nothing else.

 What is Artificial intelligence?

Artificial intelligence, on the other hand, is vast and broader in scope. According to Andrew Moore Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of developing computers that behave in ways that, until recently, we thought required human intelligence.”

That is a remarkable way to define AI in a single sentence; however, it still shows how broad the field is. AI stands for Artificial intelligence, where intelligence is defined as the ability to acquire and utilize knowledge. Unlike Machine Learning, an Artificial Intelligence program aims to increase the chance of success and not accuracy. An artificial intelligence program is designed to replicate a human brain in every possible manner. It is no doubt that AI is a broader concept while ML remains as only its subset.

Both AI & ML can demonstrate valuable business applications. But ML has got a lot more adoption for solving the critical business problems in many organizations and companies.

In today’s world, intelligence is essential for gaining the information needed to make key decisions. The world has a need for intelligence, and business intelligence serves much the same function in each and every community. First, let us know what is Business Intelligence (BI)? Business intelligence (BI) is a technology-based process, in which the data is analyzed and presented in actionable information format to help executives, managers, and other corporate end-users make informed business decisions. It is composed of different strategies and technologies used by enterprises for the data analysis of business information. BI has become so important and popular because of its integrated system. We can improve efficiency within our organization which as a result increases productivity. Some important reasons why BI is important: Turning raw data into useful data: Raw data doesn’t tell us what to do in business all on its own. BI systems allow for a comprehensive analysis of data to identify important trends, which can be used further to modify and understand the interconnections between different functions in business. Increase in efficiency: As we get a greater insight into the data recorded and analyzed by BI, we also expect an increase in the efficiency of the business, products, and services offered. As there is an increment in the efficiency of our business, products, and services, chances of getting attracted to more leads increase which uplift our market value. Improves visibility of business components OR Manage & Reduce Risk: With the help of BI, we can easily identify the core business components and see whether they are in need of improvement. It enables us to get access to detailed insights and analytics about the business. Hence, with all of these, we can identify risks and set the elements back. By this, it becomes easy for the management to effectively manage and eliminate these risks. Identifying risks can help the business grow in a better and pre-planned way. Understanding of consumer behavior: BI analysis allows us to track consumption patterns at global, regional, and local levels to better understand current trends. This, in turn, allows us to develop, deliver and serve according to the current market needs. Better ROI: By various BI analysis tools, we can understand our resources allocated to us and achieve the stated goal as per the target. The more businesses get an insight into the workings, trends, and analytics of their business processes, the more aware they are. This strategic awareness leads to faster reporting, lowering the operating costs etc and can help produce products that match the requirements of the consumers. BI helps more in an effective way which in turn directly affects the growth in revenue generation. So it's said that BI brings a better ROI. Improvement in Marketing and Sales Intelligence: To achieve the target of increment in sales, BI helps us to understand the track of data about our clients and customers. By this, we can know how they interact with our organization at a deeper level. In this way, we can identify consumer issues and better opportunities for both - the client and the organization. Improves productivity: The process of analyzing and interpreting data is done faster and efficiently with the help of BI. It gives us the power to understand business data and generate reports as per the analysis. A step ahead from the competition: BI keeps us updated on the current market trends along with being an important contributor in the decision-making process. BI ensures that you stay on top and ahead of your competitors. For years, firms have relied on basic desktop programs to keep track of their data with basic spreadsheets, but now dedicated BI platforms and BI service giving companies are affordable and many times powerful for analyzing and understanding business data.
In today’s world, intelligence is essential for gaining the information needed to make key decisions. The world has a need for intelligence, and business intelligence serves much the same function in each and every community. First, let us know what is Business Intelligence (BI)? Business intelligence (BI) is a technology-based process, in which the data is analyzed and presented in actionable information format to help executives, managers, and other corporate end-users make informed business decisions. It is composed of different strategies and technologies used by enterprises for the data analysis of business information. BI has become so important and popular because of its integrated system. We can improve efficiency within our organization which as a result increases productivity. Some important reasons why BI is important: Turning raw data into useful data: Raw data doesn’t tell us what to do in business all on its own. BI systems allow for a comprehensive analysis of data to identify important trends, which can be used further to modify and understand the interconnections between different functions in business. Increase in efficiency: As we get a greater insight into the data recorded and analyzed by BI, we also expect an increase in the efficiency of the business, products, and services offered. As there is an increment in the efficiency of our business, products, and services, chances of getting attracted to more leads increase which uplift our market value. Improves visibility of business components OR Manage & Reduce Risk: With the help of BI, we can easily identify the core business components and see whether they are in need of improvement. It enables us to get access to detailed insights and analytics about the business. Hence, with all of these, we can identify risks and set the elements back. By this, it becomes easy for the management to effectively manage and eliminate these risks. Identifying risks can help the business grow in a better and pre-planned way. Understanding of consumer behavior: BI analysis allows us to track consumption patterns at global, regional, and local levels to better understand current trends. This, in turn, allows us to develop, deliver and serve according to the current market needs. Better ROI: By various BI analysis tools, we can understand our resources allocated to us and achieve the stated goal as per the target. The more businesses get an insight into the workings, trends, and analytics of their business processes, the more aware they are. This strategic awareness leads to faster reporting, lowering the operating costs etc and can help produce products that match the requirements of the consumers. BI helps more in an effective way which in turn directly affects the growth in revenue generation. So it's said that BI brings a better ROI. Improvement in Marketing and Sales Intelligence: To achieve the target of increment in sales, BI helps us to understand the track of data about our clients and customers. By this, we can know how they interact with our organization at a deeper level. In this way, we can identify consumer issues and better opportunities for both - the client and the organization. Improves productivity: The process of analyzing and interpreting data is done faster and efficiently with the help of BI. It gives us the power to understand business data and generate reports as per the analysis. A step ahead from the competition: BI keeps us updated on the current market trends along with being an important contributor in the decision-making process. BI ensures that you stay on top and ahead of your competitors. For years, firms have relied on basic desktop programs to keep track of their data with basic spreadsheets, but now dedicated BI platforms and BI service giving companies are affordable and many times powerful for analyzing and understanding business data.

In today’s world, intelligence is essential for gaining the information needed to make key decisions. The world has a need for intelligence, and business intelligence serves much the same function in each and every community.

First, let us know what is Business Intelligence (BI)? Business intelligence (BI) is a technology-based process, in which the data is analyzed and presented in actionable information format to help executives, managers, and other corporate end-users make informed business decisions. It is composed of different strategies and technologies used by enterprises for the data analysis of business information.

BI has become so important and popular because of its integrated system. We can improve efficiency within our organization which as a result increases productivity.

Some important reasons why BI is important:

  • Turning raw data into useful data: Raw data doesn’t tell us what to do in business all on its own. BI systems allow for a comprehensive analysis of data to identify important trends, which can be used further to modify and understand the interconnections between different functions in business.
  • Increase in efficiency: As we get a greater insight into the data recorded and analyzed by BI, we also expect an increase in the efficiency of the business, products, and services offered. As there is an increment in the efficiency of our business, products, and services, chances of getting attracted to more leads increase which uplift our market value.
  • Improves visibility of business components OR Manage & Reduce Risk: With the help of BI, we can easily identify the core business components and see whether they are in need of improvement. It enables us to get access to detailed insights and analytics about the business. Hence, with all of these, we can identify risks and set the elements back. By this, it becomes easy for the management to effectively manage and eliminate these risks. Identifying risks can help the business grow in a better and pre-planned way.
  • Understanding of consumer behavior: BI analysis allows us to track consumption patterns at global, regional, and local levels to better understand current trends. This, in turn, allows us to develop, deliver and serve according to the current market needs.
  • Better ROI: By various BI analysis tools, we can understand our resources allocated to us and achieve the stated goal as per the target. The more businesses get an insight into the workings, trends, and analytics of their business processes, the more aware they are. This strategic awareness leads to faster reporting, lowering the operating costs etc and can help produce products that match the requirements of the consumers. BI helps more in an effective way which in turn directly affects the growth in revenue generation. So it's said that BI brings a better ROI.
  • Improvement in Marketing and Sales Intelligence: To achieve the target of increment in sales, BI helps us to understand the track of data about our clients and customers. By this, we can know how they interact with our organization at a deeper level. In this way, we can identify consumer issues and better opportunities for both - the client and the organization.
  • Improves productivity: The process of analyzing and interpreting data is done faster and efficiently with the help of BI. It gives us the power to understand business data and generate reports as per the analysis.
  • A step ahead from the competition: BI keeps us updated on the current market trends along with being an important contributor in the decision-making process. BI ensures that you stay on top and ahead of your competitors.

For years, firms have relied on basic desktop programs to keep track of their data with basic spreadsheets, but now dedicated BI platforms and BI service giving companies are affordable and many times powerful for analyzing and understanding business data.

Data Analytics: As the name suggests, it is the analysis and subsequent organizing of the enormous data available online. Businesses employ different types of analytics tools offered by the top data analytics companies to predict the effectiveness of the business processes. Data analytics aid companies gain information to identify potential gaps across the industry providing them an edge over their competitors. Analytics is also implied to anticipate the need of the business while mitigating any risky and fraudulent activities. Business Intelligence: It is the next step that processes the data compiled by the business analysts and utilizes the vast amount of data available to aid organizations to make informed decisions. Business intelligence allows brands to gain insight into the behavior of the customers to help improve their overall profitability. Moreover, the best big data analytics companies utilize the forecasts generated by data analytics and turn them into actionable information for the success of the businesses. Certain technologies that are commonly implemented by BI are: Data Warehousing Dashboards Ad Hoc Reporting Data Discovery Cloud Data Services There are four key types of Data Analytics techniques that are employed based on the kind of analysis needed. Descriptive analytics Diagnostic analytics Predictive analytics Prescriptive analytics TechGenix discusses the key characteristics of Business Intelligence and Data Analytics. The BI analysts focus on delivering Reports, KPIs, and analyzing trends, while the Data scientists analyze the patterns, correlations, and models. In short, the two, however different, need to work simultaneously and in synchronization to improve the efficiency and ROI of the business by properly analyzing and utilizing data.
Data Analytics: As the name suggests, it is the analysis and subsequent organizing of the enormous data available online. Businesses employ different types of analytics tools offered by the top data analytics companies to predict the effectiveness of the business processes. Data analytics aid companies gain information to identify potential gaps across the industry providing them an edge over their competitors. Analytics is also implied to anticipate the need of the business while mitigating any risky and fraudulent activities. Business Intelligence: It is the next step that processes the data compiled by the business analysts and utilizes the vast amount of data available to aid organizations to make informed decisions. Business intelligence allows brands to gain insight into the behavior of the customers to help improve their overall profitability. Moreover, the best big data analytics companies utilize the forecasts generated by data analytics and turn them into actionable information for the success of the businesses. Certain technologies that are commonly implemented by BI are: Data Warehousing Dashboards Ad Hoc Reporting Data Discovery Cloud Data Services There are four key types of Data Analytics techniques that are employed based on the kind of analysis needed. Descriptive analytics Diagnostic analytics Predictive analytics Prescriptive analytics TechGenix discusses the key characteristics of Business Intelligence and Data Analytics. The BI analysts focus on delivering Reports, KPIs, and analyzing trends, while the Data scientists analyze the patterns, correlations, and models. In short, the two, however different, need to work simultaneously and in synchronization to improve the efficiency and ROI of the business by properly analyzing and utilizing data.

Data Analytics: As the name suggests, it is the analysis and subsequent organizing of the enormous data available online. Businesses employ different types of analytics tools offered by the top data analytics companies to predict the effectiveness of the business processes. Data analytics aid companies gain information to identify potential gaps across the industry providing them an edge over their competitors. Analytics is also implied to anticipate the need of the business while mitigating any risky and fraudulent activities.

Business Intelligence: It is the next step that processes the data compiled by the business analysts and utilizes the vast amount of data available to aid organizations to make informed decisions. Business intelligence allows brands to gain insight into the behavior of the customers to help improve their overall profitability. Moreover, the best big data analytics companies utilize the forecasts generated by data analytics and turn them into actionable information for the success of the businesses.

Certain technologies that are commonly implemented by BI are:

  • Data Warehousing
  • Dashboards
  • Ad Hoc Reporting
  • Data Discovery
  • Cloud Data Services

There are four key types of Data Analytics techniques that are employed based on the kind of analysis needed.

  • Descriptive analytics
  • Diagnostic analytics
  • Predictive analytics
  • Prescriptive analytics

TechGenix discusses the key characteristics of Business Intelligence and Data Analytics.

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The BI analysts focus on delivering Reports, KPIs, and analyzing trends, while the Data scientists analyze the patterns, correlations, and models.

In short, the two, however different, need to work simultaneously and in synchronization to improve the efficiency and ROI of the business by properly analyzing and utilizing data.

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