Insight

We make the easiest to use fitness center software on the market!

0.0 (0 Reviews)
About Insight
Gym Insight is intuitive to learn, easy to use, and focused on the profitability of your business. It was created out of simple frustration for the products that existed at the time. Every one of them seems to have been cobbled together to solve the problem of billing. But w...
read more
Insight
We make the easiest to use fitness center software on the market!
0.0 (0 Reviews)
1 Question
  • Big Data
1 Answer
 Organizations can use big data systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.Data scientists, data analysts, predictive modellers, statisticians, and other professionals collect, clean, process, and analyze growing volumes of structured transaction data and other forms of data not used by conventional BI and analytics details.  Here is a review of the four steps of the data development process:Data experts gather data from a mixture of varied sources. Often, it is a mix of semi-organized and unorganized data. While each organization will use other data streams, some familiar sources include:internet clickstream data;webserver logs;mobile applications;cloud applications; social media content;text from customer and survey responses;mobile phone records; andmachine data obtained by sensors connected to the internet of things (IoT).Data is processed. After the information is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data correctly for analytical queries. Thorough data processing makes for higher performance from analytical questions.Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.The collected, processed, and cleaned data is analyzed with analytics software. This includes tools for:data mining, which sifts through data sets in search of patterns and relationshipspredictive analytics, which builds models to forecast customer behaviour and other future developmentsmachine learning, which taps algorithms to analyze large data setsdeep understanding, which is a more advanced offshoot of machine learningtext mining and statistical analysis softwareartificial intelligence (AI)mainstream business intelligence softwaredata visualization tools Big data applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming applications are becoming common in big data environments as users look to perform real-time on data fed into Hadoop systems through stream processing engines, such as Spark, Flink, and Storm.Earlier big data systems were mainly deployed on-premises, particularly in huge organizations collecting, organizing, and analyzing the cumbersome amount of data. But cloud platform vendors, such as Microsoft and Amazon Web Services (AWS), have made it easier to set up and manage Hadoop clusters in the cloud. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses.Big data has become increasingly advantageous in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources.Big data analytics uses and examplesHere are few examples of how big data analytics can be used to help organizations:Customer acquisition and retention. Consumer data can help companies' marketing efforts, which can act on trends to increase customer satisfaction. For example, personalization engines for Amazon, Netflix, and Spotify can provide improved customer experiences and create customer loyalty.Targeted ads. Personalization data from sources such as past purchases, interaction patterns, and product page viewing histories can help generate compelling, targeted ad campaigns for users on the individual level and on a larger scale.Product development. Big data analytics can provide insights to inform about product viability, development decisions, progress measurement, and steer improvements in the direction of what fits a business' customers.Price optimization. Retailers may opt for pricing models that use and model data from various data sources to maximize revenues.Supply chain and channel analytics. Predictive analytical models can help with preemptive replenishment, B2B supplier networks, inventory management, route optimizations, and the notification of potential delays to deliveries.Risk management. Big data analytics can identify new risks from data patterns for effective risk management strategies.Improved decision-making. Insights business users extract from relevant data can help organizations make quicker and better decisions.Big data analytics benefitsThe benefits of using big data analytics include:Quickly analyzing large amounts of data from different sources in many other formats and types.Rapidly making better-informed decisions for effective strategizing can benefit and improve the supply chain, operations, and other areas of strategic decision-making.Cost savings, which can result from new business process efficiencies and optimizations.A better understanding of customer needs, behaviour, and sentiment can lead to better marketing insights and provide information for product development.Improved, better-informed risk management strategies that draw from large sample sizes of data.
 Organizations can use big data systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.Data scientists, data analysts, predictive modellers, statisticians, and other professionals collect, clean, process, and analyze growing volumes of structured transaction data and other forms of data not used by conventional BI and analytics details.  Here is a review of the four steps of the data development process:Data experts gather data from a mixture of varied sources. Often, it is a mix of semi-organized and unorganized data. While each organization will use other data streams, some familiar sources include:internet clickstream data;webserver logs;mobile applications;cloud applications; social media content;text from customer and survey responses;mobile phone records; andmachine data obtained by sensors connected to the internet of things (IoT).Data is processed. After the information is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data correctly for analytical queries. Thorough data processing makes for higher performance from analytical questions.Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.The collected, processed, and cleaned data is analyzed with analytics software. This includes tools for:data mining, which sifts through data sets in search of patterns and relationshipspredictive analytics, which builds models to forecast customer behaviour and other future developmentsmachine learning, which taps algorithms to analyze large data setsdeep understanding, which is a more advanced offshoot of machine learningtext mining and statistical analysis softwareartificial intelligence (AI)mainstream business intelligence softwaredata visualization tools Big data applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming applications are becoming common in big data environments as users look to perform real-time on data fed into Hadoop systems through stream processing engines, such as Spark, Flink, and Storm.Earlier big data systems were mainly deployed on-premises, particularly in huge organizations collecting, organizing, and analyzing the cumbersome amount of data. But cloud platform vendors, such as Microsoft and Amazon Web Services (AWS), have made it easier to set up and manage Hadoop clusters in the cloud. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses.Big data has become increasingly advantageous in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources.Big data analytics uses and examplesHere are few examples of how big data analytics can be used to help organizations:Customer acquisition and retention. Consumer data can help companies' marketing efforts, which can act on trends to increase customer satisfaction. For example, personalization engines for Amazon, Netflix, and Spotify can provide improved customer experiences and create customer loyalty.Targeted ads. Personalization data from sources such as past purchases, interaction patterns, and product page viewing histories can help generate compelling, targeted ad campaigns for users on the individual level and on a larger scale.Product development. Big data analytics can provide insights to inform about product viability, development decisions, progress measurement, and steer improvements in the direction of what fits a business' customers.Price optimization. Retailers may opt for pricing models that use and model data from various data sources to maximize revenues.Supply chain and channel analytics. Predictive analytical models can help with preemptive replenishment, B2B supplier networks, inventory management, route optimizations, and the notification of potential delays to deliveries.Risk management. Big data analytics can identify new risks from data patterns for effective risk management strategies.Improved decision-making. Insights business users extract from relevant data can help organizations make quicker and better decisions.Big data analytics benefitsThe benefits of using big data analytics include:Quickly analyzing large amounts of data from different sources in many other formats and types.Rapidly making better-informed decisions for effective strategizing can benefit and improve the supply chain, operations, and other areas of strategic decision-making.Cost savings, which can result from new business process efficiencies and optimizations.A better understanding of customer needs, behaviour, and sentiment can lead to better marketing insights and provide information for product development.Improved, better-informed risk management strategies that draw from large sample sizes of data.


 

Organizations can use big data systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization, and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals.

Data scientists, data analysts, predictive modellers, statisticians, and other professionals collect, clean, process, and analyze growing volumes of structured transaction data and other forms of data not used by conventional BI and analytics details. 

 

Here is a review of the four steps of the data development process:

Data experts gather data from a mixture of varied sources. Often, it is a mix of semi-organized and unorganized data. While each organization will use other data streams, some familiar sources include:

internet clickstream data;

webserver logs;

mobile applications;

cloud applications; 

social media content;

text from customer and survey responses;

mobile phone records; and

machine data obtained by sensors connected to the internet of things (IoT).

Data is processed. After the information is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data correctly for analytical queries. Thorough data processing makes for higher performance from analytical questions.

Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.

The collected, processed, and cleaned data is analyzed with analytics software. This includes tools for:

data mining, which sifts through data sets in search of patterns and relationships

predictive analytics, which builds models to forecast customer behaviour and other future developments

machine learning, which taps algorithms to analyze large data sets

deep understanding, which is a more advanced offshoot of machine learning

text mining and statistical analysis software

artificial intelligence (AI)

mainstream business intelligence software

data visualization tools

 

Big data applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming applications are becoming common in big data environments as users look to perform real-time on data fed into Hadoop systems through stream processing engines, such as Spark, Flink, and Storm.

Earlier big data systems were mainly deployed on-premises, particularly in huge organizations collecting, organizing, and analyzing the cumbersome amount of data. But cloud platform vendors, such as Microsoft and Amazon Web Services (AWS), have made it easier to set up and manage Hadoop clusters in the cloud. Users can now spin up clusters in the cloud, run them for as long as they need and then take them offline with usage-based pricing that doesn't require ongoing software licenses.

Big data has become increasingly advantageous in supply chain analytics. Big supply chain analytics utilizes big data and quantitative methods to enhance decision-making processes across the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources.

Big data analytics uses and examples

Here are few examples of how big data analytics can be used to help organizations:

Customer acquisition and retention. Consumer data can help companies' marketing efforts, which can act on trends to increase customer satisfaction. For example, personalization engines for Amazon, Netflix, and Spotify can provide improved customer experiences and create customer loyalty.

Targeted ads. Personalization data from sources such as past purchases, interaction patterns, and product page viewing histories can help generate compelling, targeted ad campaigns for users on the individual level and on a larger scale.

Product development. Big data analytics can provide insights to inform about product viability, development decisions, progress measurement, and steer improvements in the direction of what fits a business' customers.

Price optimization. Retailers may opt for pricing models that use and model data from various data sources to maximize revenues.

Supply chain and channel analytics. Predictive analytical models can help with preemptive replenishment, B2B supplier networks, inventory management, route optimizations, and the notification of potential delays to deliveries.

Risk management. Big data analytics can identify new risks from data patterns for effective risk management strategies.

Improved decision-making. Insights business users extract from relevant data can help organizations make quicker and better decisions.

Big data analytics benefits

The benefits of using big data analytics include:

Quickly analyzing large amounts of data from different sources in many other formats and types.

Rapidly making better-informed decisions for effective strategizing can benefit and improve the supply chain, operations, and other areas of strategic decision-making.

Cost savings, which can result from new business process efficiencies and optimizations.

A better understanding of customer needs, behaviour, and sentiment can lead to better marketing insights and provide information for product development.

Improved, better-informed risk management strategies that draw from large sample sizes of data.

  • Business Services
  • Digital Marketing
  • Stock market
2 Answers
Zomato Limited (earlier known as Zomato Private Limited and Zomato Media Private Limited) has now come to terms with opening its public offering on July 14, 2021, i.e., today. The cost of this IPO has been priced at INR 72 to INR 76/equity share. Bids can be made for at least 195 equity shares and in multiples of 195 equity shares later.The IPO comprises the latest issue collecting up to INR 90,000 million and an offer for sale by Info Edge (India) Limited aggregating up to INR 3,750 million. This offer also comes with a reservation of up to 6,500,000 equity shares for purchase by eligible employees on a proportionate basis—the quantity not more than 5 percent of the after that offered equity share capital of the company.Last week Info Edge, initiated exchanges that it had minimized its offer for sale size by half to INR 375 (earlier 750) crore in Zomato's initial public offering. Moreover, Sebi recently cleared Zomato's Application for fundraising via IPO.In February, Zomato raised $250 million in its pre-IPO primary fundraise for $5.4 billion from investors like Kora Management, Tiger Global, Fidelity, Dragoneer, and Bow Wave.Zomato had filed the DRHP (Draft Red Herring Prospectus) with Sebi this April for its INR 8,250-crore IPO including the latest issue of INR 7,500 crore and an offer for sale of INR 750 crore by its existing investor Info Edge.At the end of March 31, 2020, zomato reported a loss of 2,385.6 crores against a loss of 1,010.2 crores last year, but revenue almost multiplied by two to 2,604.7 crores from 1,312.58 crores in the same time frame. This loss for the nine months ended December 31, 2020, stood at 682.2 crores on revenue of INR 1,301.35 crore, which looks revolutionary.It is mandatory for all potential Bidders, except Anchor Investors, to utilize the Application Supported by Blocked Amount (ASBA) process by offering details of their respective bank account in case of Retail Individual Bidders.Global coordinators such as Kotak Mahindra Capital Company Limited, Morgan Stanley India Company Private Limited, and Credit Suisse Securities (India) Private Limited are looking forward to booking running lead managers (GCBRLMs) to this issue.
Zomato Limited (earlier known as Zomato Private Limited and Zomato Media Private Limited) has now come to terms with opening its public offering on July 14, 2021, i.e., today. The cost of this IPO has been priced at INR 72 to INR 76/equity share. Bids can be made for at least 195 equity shares and in multiples of 195 equity shares later.The IPO comprises the latest issue collecting up to INR 90,000 million and an offer for sale by Info Edge (India) Limited aggregating up to INR 3,750 million. This offer also comes with a reservation of up to 6,500,000 equity shares for purchase by eligible employees on a proportionate basis—the quantity not more than 5 percent of the after that offered equity share capital of the company.Last week Info Edge, initiated exchanges that it had minimized its offer for sale size by half to INR 375 (earlier 750) crore in Zomato's initial public offering. Moreover, Sebi recently cleared Zomato's Application for fundraising via IPO.In February, Zomato raised $250 million in its pre-IPO primary fundraise for $5.4 billion from investors like Kora Management, Tiger Global, Fidelity, Dragoneer, and Bow Wave.Zomato had filed the DRHP (Draft Red Herring Prospectus) with Sebi this April for its INR 8,250-crore IPO including the latest issue of INR 7,500 crore and an offer for sale of INR 750 crore by its existing investor Info Edge.At the end of March 31, 2020, zomato reported a loss of 2,385.6 crores against a loss of 1,010.2 crores last year, but revenue almost multiplied by two to 2,604.7 crores from 1,312.58 crores in the same time frame. This loss for the nine months ended December 31, 2020, stood at 682.2 crores on revenue of INR 1,301.35 crore, which looks revolutionary.It is mandatory for all potential Bidders, except Anchor Investors, to utilize the Application Supported by Blocked Amount (ASBA) process by offering details of their respective bank account in case of Retail Individual Bidders.Global coordinators such as Kotak Mahindra Capital Company Limited, Morgan Stanley India Company Private Limited, and Credit Suisse Securities (India) Private Limited are looking forward to booking running lead managers (GCBRLMs) to this issue.

Zomato Limited (earlier known as Zomato Private Limited and Zomato Media Private Limited) has now come to terms with opening its public offering on July 14, 2021, i.e., today. The cost of this IPO has been priced at INR 72 to INR 76/equity share. Bids can be made for at least 195 equity shares and in multiples of 195 equity shares later.

The IPO comprises the latest issue collecting up to INR 90,000 million and an offer for sale by Info Edge (India) Limited aggregating up to INR 3,750 million. This offer also comes with a reservation of up to 6,500,000 equity shares for purchase by eligible employees on a proportionate basis—the quantity not more than 5 percent of the after that offered equity share capital of the company.

Last week Info Edge, initiated exchanges that it had minimized its offer for sale size by half to INR 375 (earlier 750) crore in Zomato's initial public offering. Moreover, Sebi recently cleared Zomato's Application for fundraising via IPO.

In February, Zomato raised $250 million in its pre-IPO primary fundraise for $5.4 billion from investors like Kora Management, Tiger Global, Fidelity, Dragoneer, and Bow Wave.

Zomato had filed the DRHP (Draft Red Herring Prospectus) with Sebi this April for its INR 8,250-crore IPO including the latest issue of INR 7,500 crore and an offer for sale of INR 750 crore by its existing investor Info Edge.

At the end of March 31, 2020, zomato reported a loss of 2,385.6 crores against a loss of 1,010.2 crores last year, but revenue almost multiplied by two to 2,604.7 crores from 1,312.58 crores in the same time frame. This loss for the nine months ended December 31, 2020, stood at 682.2 crores on revenue of INR 1,301.35 crore, which looks revolutionary.

It is mandatory for all potential Bidders, except Anchor Investors, to utilize the Application Supported by Blocked Amount (ASBA) process by offering details of their respective bank account in case of Retail Individual Bidders.

Global coordinators such as Kotak Mahindra Capital Company Limited, Morgan Stanley India Company Private Limited, and Credit Suisse Securities (India) Private Limited are looking forward to booking running lead managers (GCBRLMs) to this issue.

  • UI-UX
  • User Experience UX
  • Blogs
1 Answer
I regularly read Entrepreneur.com and TED Talks for tech news & trends, product designs, hacks, and everything related to technology. Technorati, I found, has become more business-oriented these days, so it provides a better read to the businesses and start-ups. If you want to have an insight over design thinking, manufacturing, material, inspiration, design sketching, and entrepreneurship, you can check out some of the trending blogs online. TheInspirationGrid, Behance, DesignInspiration, and DesignYourWay are some of the popular blogs that will help you to get what is going on in the world of technology today and tomorrow. You should definitely try visiting SpeakerHub to listen to the world’s most popular technology speakers. Alexis Ohanian, Brian Cox, and Buzz Aldrin rank at the top of this list. They are fantastic speakers and can solve all of your tech-queries and issues. Being a reader, I personally like advices and recommendations from Luisa Isbell on Quora. Her product management guidelines are awesome.
I regularly read Entrepreneur.com and TED Talks for tech news & trends, product designs, hacks, and everything related to technology. Technorati, I found, has become more business-oriented these days, so it provides a better read to the businesses and start-ups. If you want to have an insight over design thinking, manufacturing, material, inspiration, design sketching, and entrepreneurship, you can check out some of the trending blogs online. TheInspirationGrid, Behance, DesignInspiration, and DesignYourWay are some of the popular blogs that will help you to get what is going on in the world of technology today and tomorrow. You should definitely try visiting SpeakerHub to listen to the world’s most popular technology speakers. Alexis Ohanian, Brian Cox, and Buzz Aldrin rank at the top of this list. They are fantastic speakers and can solve all of your tech-queries and issues. Being a reader, I personally like advices and recommendations from Luisa Isbell on Quora. Her product management guidelines are awesome.

I regularly read Entrepreneur.com and TED Talks for tech news & trends, product designs, hacks, and everything related to technology.

Technorati, I found, has become more business-oriented these days, so it provides a better read to the businesses and start-ups.

If you want to have an insight over design thinking, manufacturing, material, inspiration, design sketching, and entrepreneurship, you can check out some of the trending blogs online.

TheInspirationGrid, Behance, DesignInspiration, and DesignYourWay are some of the popular blogs that will help you to get what is going on in the world of technology today and tomorrow.

You should definitely try visiting SpeakerHub to listen to the world’s most popular technology speakers. Alexis Ohanian, Brian Cox, and Buzz Aldrin rank at the top of this list. They are fantastic speakers and can solve all of your tech-queries and issues.

Being a reader, I personally like advices and recommendations from Luisa Isbell on Quora. Her product management guidelines are awesome.

Loading interface...
Key Details
Software trial:

N/A

Starting Price:

$49/Month

Check vendor pricing
Platforms: