Data Analysis Software
An endless amount of data is available to work with, as the world is becoming more data-driven. Organizations can make better business decisions with data analytics as they provide valuable insights, which in turn helps them make the right choices for their current and future business projects.
What is data analysis software?
Data analysis software is a system that enables users with the process of cleaning, inspecting, transforming, and modeling data to discover meaningful information for better decision making
How to select the best data analysis software?
Picking the best data analysis tool is a bit challenging. To ease this, it would be great to check the following list of best data analysis software formulated by GoodFirms. Every software listed below also comes with its core functionalities, prices, and reviews from its buyers.
List of Best Data Analysis ToolFilter
Keen.io is the complete event data streaming platform. Our platform can handle all aspects of your event data management, from collection to application. Stream, store, query, and present your data all in one place and get a competitive advantage over the competition. Keen allows you to get a true view of your product usage and interaction through event-based data. We make it easy to dive into the... read more about Keen
The fastest path to business value through data. With the only end-to-end solution for data integration and analytics, Qlik democratizes BI, creating a shared fabric of trusted, real-time data, collaborative analysis, and actionable insights. Get the technology and expertise you need to build a data-driven culture and transform your business.... read more about Qlik Sense
DQLabs has a decade of experience in providing data related solutions to fortune 100 clients around data integration, data governance, data analytics, data visualization, and data science-related solutions. The platform has all the inbuilt features to make autonomous execution without any manual or configuration. With this AI and ML-powered tool, scalability, governance, and automation from end to... read more about DQLabs
RapidMiner Studio is a visual data science workflow designer accelerating the prototyping & validation of models. Easy to use visual environment for building analytics processes: Graphical design environment makes it simple and fast to design better models.... read more about RapidMiner Studio
Mode is an analytics platform designed to help data analysts and data scientists analyze, visualize, and share data. Run fast, advanced analysis by seamlessly switching between a cloud-based SQL Editor, Python & R notebooks, and interactive visualizations. Focus on analysis, while stakeholders self-serve.... read more about Mode
GoodData powers the transformation of decision-making for frontline workers within their daily business processes. It's a platform that gathers data and user decisions and transforms them into actionable insights delivered at the point of work. This empowers enterprises and software companies to integrate insights into applications so that their customers, partners, and employees can make more... read more about GoodData
SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Our mission is to make it easier for more people to use powerful analytics every day, to shorten the path from data ... read more about SAS
KNIME Analytics Platform is the open source software for creating data science applications and services. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone. ... read more about KNIME Analytics Platform
Knowage offers FULL ANALYTICAL CAPABILITIES, with a special focus on big data analytics and comprehensive support to rich and multi-source data analysis. Knowage provides different modules, each one focused on a specific domain but mutually combinable (Big Data, Smart Intelligence, Enterprise Reporting, Location Intelligence, Performance Management, Predictive Analysis). It is available in two ve... read more about KNOWAGE
Parse.ly is the new measure of content value. Do you work in content marketing, digital media, audience development, corporate communications, or any other role in which your company's content is central to business goals and audience growth? If so, you want a way to win with your content. And Parse.ly can help. ... read more about Parse.ly
The best tool for product and marketing teams – accelerate website growth today with Kissmetrics. Move past unreliable session-based analytics like GA into advanced user-based insights. Great product, great service, great price. Let us show you how today! ... read more about Kissmetrics
Cluvio is a modern data analytics platform that lets you run SQL queries against your database, process data in R, visualize the results and create beautiful, interactive dashboards in minutes. With pricing optimized for startups and small companies, it is easy and cost effective to share insights with everyone in your company or your customers. Cluvio also supports powerful embedding that allows ... read more about Cluvio
Spider Impact is performance management software that powers data visualization, balanced scorecards, team alignment, and KPI and initiative management. Don't just define your strategy—live it. Get on-demand performance data from any perspective you desire. Allow employees to see and understand their roles in organizational success. Define and track your strategic initiatives at all levels of th... read more about Spider Impact
Answer dock is a data analytics company that uses Natural Language Processing (NLP) with Artificial Intelligence (AI) to empower business users to make smarter data-driven decisions. Our mission is to create an easy to use analytics product that speaks the business users’ language and enables them to leverage powerful analysis techniques and algorithms, without the need for data analysts or s... read more about AnswerDock
Helping organizations grow and gain complete insight into their business Solver, Inc. is redefining the category of cloud-based reporting and planning. The Solver solution is built to enable faster and better business decisions across the entire organization. Solver combines financial and other key data into a single tool, powered by the most flexible report and planning form designer on the mark... read more about Solver Suite
Whether you want to instantly deliver personalized reports to thousands of users, empower business users with self-service analytics, or boost analytics adoption to 100% across your enterprise, MicroStrategy provides the technology to make it happen. MicroStrategy's powerful analytical engine, comprehensive toolsets, variety of data connectors, and scalable, open architecture ensure you have every... read more about MicroStrategy Enterprise Analytics
Kyubit BI Server delivers comprehensive OLAP analysis for your Multidimensional and Tabular OLAP models within your browser with fast asynchronous responses and intuitive user interface. Quick product setup, OLAP standard analytic features, visual analysis, OLAP Reports, and OLAP Scheduled Reports subscriptions are key features of Kyubit Business Intelligence. Convert your analysis to PDF or Ex... read more about Kyubit Business Intelligence
Anaconda is the world’s most popular data science platform and the foundation of modern machine learning. We pioneered the use of Python for data science, champion its vibrant community, and continue to steward open-source projects that make tomorrow’s innovations possible. Our enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness the powe... read more about Anaconda
WebFOCUS business intelligence (BI) and analytics products help companies use data more strategically across and beyond the enterprise. It provides organizations with everything they need to turn every kind of data into actionable insights for real business outcomes. WebFOCUS offers the right user experience for every user. So easy to use, it helps satisfy all your information consumers needs in ... read more about WebFOCUS
Origin is the data analysis and graphing software of choice for over half a million scientists and engineers in commercial industries, academia, and government laboratories worldwide. Origin offers an easy-to-use interface for beginners, combined with the ability to perform advanced customization as you become more familiar with the application. Take your data analysis to the next level with Origi... read more about OriginPro
Neural Designer is a data science and machine learning platform that helps you build, train and deploy neural network models. Neural Designer contains the most advanced techniques for data preparation, machine learning and model deployment. Thanks to its intuitive graphical user interface, there is no need to write code or assembe blocks. It provides you with comprehensive and visual results. Neur... read more about Neural Designer
Widely admired for both its technical prowess and elegant ease of use, Mathematica provides a single integrated, continually expanding system that covers the breadth and depth of technical computing—and seamlessly available in the cloud through any web browser, as well as natively on all modern desktop systems.... read more about Wolfram Mathematica
Adaptive, ad-hoc reporting and analysis. From high-level dashboards to underlying details, Phocas delivers self-service capabilities that scale with business growth. Get instant, focused and visual data when you need it. Phocas solves the data problems of manufacturers, distributors and retailers. Our purpose is to make data analytics easy-to-use, so people feel good about their business data. Pho... read more about Phocas Software
MIPAR is a revolutionary image analysis software, capable of identifying and measuring features from nearly any image one can capture. Our users have shown it to be perhaps the most the efficient and flexible image processing software on the planet. We started in materials science, but realized it was so much more. Today, MIPAR is used by companies and universities, large and small, from anything ... read more about MIPAR
Informer is a game changer. You’re gathering data from more sources than ever before – enterprise applications, traditional databases, cloud data, unstructured data from across the web, even spreadsheets. These information streams hold valuable insights, and harnessed correctly, can lead to improved efficiency, productivity and competitiveness.... read more about Informer
MemSQL is The No Limits Database, powering modern applications and analytical systems with a cloud-native, massively scalable architecture for maximum ingest and query performance with the highest concurrency. MemSQL envisions a world where every business can make decisions in real time and every experience is optimized through data. Global enterprises use the MemSQL distributed database to easil... read more about MemSQL
FactoryTalk software is built for supporting an ecosystem of advanced industrial applications, including IoT. It all starts at the edge where manufacturing happens and scales from on-premise to cloud. Imagine supercharging your industrial environment with software that offers cutting edge design, maximizes operational efficiencies, and delivers predictive and augmented maintenance advantages. From... read more about FactoryTalk
The No. 1 Software for Qualitative and Mixed Methods Data Analysis. With over two decades of experience, ATLAS.ti is the only software that gives you all-in-one access to the entire range of platforms. Work with one of our desktop flagships for Windows and Mac, use our web-based Cloud version or our mobile iPad or Android versions for your fieldwork. All of the ATLAS.ti products are easy to learn ... read more about ATLAS.ti
Igor Pro 8 is an interactive software environment for experimentation with scientific and engineering data and for the production of publication-quality graphs and page layouts. Igor has been used by tens of thousands of technical professionals since its introduction in 1989. Igor Pro combines powerful tools with an easy to use, point and click interface for the casual user together and a programm... read more about Igor Pro
Putler takes care of tedious data collection and processing work, so you can get to sales analysis and business growth. Putler has ready integrations with most popular payment gateways. We also have open source connectors for popular WordPress eCommerce plugins. Putler gives you accurate analytics, business insights & takes care of routine tasks. Putler analyzes billions of dollars for people who ... read more about Putler
Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like t... read more about Databricks
MarketSight is dedicated to providing powerful and intuitive software solutions for Market Researchers. Our mission is to provide easy-to-use tools that enable researchers and analysts to focus on finding and presenting critical new insights about their business. MarketSight offers a cloud-based data analysis and visualization platform and professional design services that empower researchers and ... read more about MarketSight
QueryStorm is a development and data processing plugin for Excel. It offers SQL and C# support in Excel, making it much easier for tech people to interact with data in spreadsheets. It sports an advanced IDE with all the features you might expect from a modern code editor: auto-complete, syntax and error highlighting, documentation tooltips, code formatting etc...... read more about QueryStorm
Qubole is a simple, open, and secure Data Lake Platform for machine learning, streaming, and ad-hoc analytics. Our platform provides end-to-end services that reduce the time and effort required to run Data pipelines, Streaming Analytics, and Machine Learning workloads on any cloud. No other platform offers the openness and data workload flexibility of Qubole while lowering cloud data lake costs by... read more about Qubole
Tercept Unified Analytics automatically aggregates, normalizes & organizes all monetization data and all marketing data into a single dashboard with powerful visualization & charting capabilities. Analytics teams can setup data transformations & create custom reports that combine/map data across data sources. They can also automatically send data into visualization dashboards (Tableau, Looker, Goo... read more about Tercept Unified Analytics
First collaborative SaaS software for cost estimating and data mining. Improve design-to-cost and time-to-market performance, create operating synergy between the various jobs and functions, streamline and professionalize the costing and quotation activities. easyKost makes it possible to determine the cost of new products or services in a matter of seconds by exploiting the richness of your data ... read more about EasyKost
Striim moves data continuously and in real time across on-premises and Cloud environments via streaming data integration – including change data capture – and stream processing. Striim specializes in integration from a wide variety of data sources – transaction/change data, events, log files, application and IoT sensor data – and real-time correlation across multiple streams. Striim is the... read more about Striim
MonkeyLearn is an AI platform that allows you analyze text with Machine Learning to automate business workflows and save hours of manual data processing. Customers like Clearbit, Segment and Drift use MonkeyLearn to classify and extract actionable data from raw texts like emails, chats, web pages, documents, tweets and more! MonkeyLearn can be easily integrated via integrations like Google Sheets,... read more about MonkeyLearn
quantilope's Agile Insights platform offers multiple solutions to answer questions across your entire organization from business strategy to product planning, product launch, and performance monitoring. Our tools are customizable, powered by automated methodologies to deliver advanced analyses at speed. quantilope provides a variety of pre-templated surveys to answers questions that affect your ov... read more about quantilope
Appsilon delivers the world’s most advanced R Shiny apps, and we have the expertise to scale these apps up to hundreds of concurrent users. We provide data science consulting, managed services, and custom solutions with R Shiny and Python Dash technologies. We have a wealth of experience with machine learning in image recognition and predictive analytics. If you have a difficult data problem tha... read more about Appsilon Data Science
Arcadia Data provides the first visual analytics and BI platform native to big data that delivers the scale, performance, and agility business users need to discover and productionize real-time insights. Its flagship product, Arcadia Enterprise, was built from inception to run natively within big data platforms, in the cloud and/or on-premises, to streamline the self-service analytics process on d... read more about Arcadia Enterprise
datacadabra allows you to save time on the most common analyses of your business while guaranteeing an exceptional level of performance. datacadabra allows you to gain autonomy on customer knowledge, while remaining very easy to use for profiles with no knowledge of statistics.... read more about datacadabra
GADS/OS Data Entry, GADS/OS Analysis & Reporting, and GADS/OS Wind™ give generating companies tools to take control of their mandatory GADS reporting to NERC and the various ISOs. GADS/OS is a set of free and open-source software tools and is licensed under the GNU General Public License Version 2 (GNU GPL or simply GPL) which is the most widely used free software license. GADS/OS is in use at t... read more about GADS
Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections.... read more about Orange
- Introduction to the buyer’s guide for data analysis software:
- What is data analysis?
- Why do businesses need data analysis?
- Key terminology associated with the data analysis process:
- Data Analysis Process: Then and Now. How has the data analysis process changed over time?
- What are the challenges faced in data analysis?
- What is data analysis software?
- What are the different types of data analysis software?
- What are the benefits of data analysis software?
- What are the key features of data analysis software?
- What are the current trends in the data analysis field?
- What is the role of data analysis software in various industries?
- How to choose the best data analysis software for your business?
- What is the average cost of buying data analysis software?
- Why refer to GoodFirms’ list of data analysis software?
Introduction to the buyer’s guide for data analysis software:
Do you remember Michael Burry? The critically acclaimed film on financial markets- The Big Short is based on this legendary hedge fund manager's life. He successfully predicted the US housing loan bubble of 2007-2008 that triggered the economic depression of 2008. How was he able to do that? By analyzing the mortgage lending data from 2003 to 2004. When the whole S&P index rose only 3% between 2000 and 2008, the funds managed by Michael Burry returned a whopping 489.34% during the same period. That's the power of data analysis. For the hundreds of decisions organizations make every day, data analysis provides a mechanism to identify patterns, anticipate results, and respond proactively.
Today's businesses are luckier than Michael, as what he did manually can be done through a click of the button using a Data Analysis Software. The data analysis technology has advanced dramatically in the past decade and has taken a leap forward with more sophisticated algorithms and massive data processing capabilities. However, deploying the right technology that provides businesses with functional expertise to use data is the biggest challenge that companies face.
This buyer's guide attempts to guide businesses in making the right decision to invest in the best data analysis software by providing them a comprehensive view of all aspects of a data analysis software such as features, use, pricing, and types.
What is data analysis?
Data analysis is the process and technique of analyzing data to draw conclusions, get insights, and highlight the most useful part of the raw data. Data analysis involves cleaning and modeling data to unearth critical information for business decision-making. Today, mechanical processes, preset algorithms, and software applications are used to process the data for analysis.
Types of Data Analysis Methods:
Descriptive Data Analysis:
Descriptive data analysis, a part of statistical data analysis, focuses on past data such as profits, revenues, sales, KPIs, etc. to reveal meaningful insights about the business's performance over time. Companies often visualize their earnings and revenues over the past years (Quarterly, Yearly Reports) with trendlines formed with descriptive data analysis methods.
Diagnostic Data Analysis:
Diagnostic data analysis is the process of identifying behavior patterns and relating them with a recent business phenomenon. For example, if sales increased dramatically in a particular month, then a business may use diagnostic data analysis to determine the factors that caused the sudden surge. If a business finds out that sales data corresponded with the increased marketing spend prior to the sales surge month, then they can decide if they should increase the marketing budget for the coming months too.
Predictive Data Analysis:
Predictive data analysis predicts future trends, events, revenues, etc. based on the current and past data analysis. For example, a company studies past ten-year sales data and finds that sales are generally down in April-May months. Based on such data, the company can revise their inventory needs for next year's May and June.
Prescriptive Data Analysis:
Prescriptive data analysis utilizes the results of above all analysis to form a conclusive strategy to mitigate a challenge or decide on the future course of action.
Why do businesses need data analysis?
Consider the following business scenarios:
- A manufacturer to launch a new product in the overseas market
- A pharma company wants to apply for new drug approval with the FDA.
- A sales manager wants to know the reason for the product's poor performance in a particular location.
In all the above situations, data analysis is essential. The company will have to analyze the market data for a response to such products in the overseas market, the pharma company will have to rely on trial test results and its analysis before applying, and the sales manager will have to carry out a detailed analysis of the customer preference and buying behavior survey to find the reason for poor performance. Data analysis is the lifeline of businesses.
As businesses around the world get smarter, data-driven insights become the key to competitive advantage. Web, sensors, phone transactions, cameras, POS systems, etc. create a huge array of data sets for businesses that can be used for valuable analytics. Data-driven insights enable companies to better target the market compared to their competitors.
Key terminology associated with the data analysis process:
The data set is the collection of data represented in rows and columns (Units of observation). Data itself is a plural form of datum. For example, you can observe toys and infer a set of characteristics such as size, color, price, shape, etc. If such patterns and differences are put into rows and columns to present the information about those toys, it will be called a data set.
In the technological sense, data source refers to the location from which data originates. For example, a company may store the names, email addresses, purchasing history, contact details, preferences, etc. of customers in a CRM database. In this case, the database containing customer data is the data source. Data can be retrieved whenever required from its source through web accessibility or local server connection.
It is the collection of structured data. It usually consists of organized sets of information that can be retrieved and updated as required. There are many types of databases based on their operation. For example, Relational databases such as MySQL, Oracle, Sybase, and more use the standard language SQL (Structured Query Language) for searching, inserting and updating databases. Another type, NoSQL database, is used to store unstructured data. Nowadays, advanced Cloud databases with massive storage capacities and support to various software applications are deployed by businesses. According to Gartner, by 2022, cloud services will be essential for 90% of data and analytics innovation.
In the data blending process, data analysts collect data from various sources and combine them to form a useful functioning data set. Data blending is done to improve the quality, functionality, and utility of data. The blended data can reveal more profound insights into the data and drive better analysis. Businesses that imbibe predictive analytics into their core functions often rely on data blending for higher intelligence.
Qualitative data refers to data collected in a descriptive or categorical form. It is the data that cannot be computed by can be put in a verbal or narrative form. It is not measurable and exploratory in nature. For example, opinion interviews, case studies, expert opinions, open-ended questions, etc. can get you qualitative data.
Quantitative data refers to data collected in numerical units. It is measurable and conclusive. In short, this data can be analyzed statistically and often provides information about the level of occurrence of some entity. For instance, responses from the Likert scale can get you quantitative data.
Data alerts are email notifications or message alerts that one gets when data changes or data levels reach a pre-specified condition. Data alerts are helpful to businesses when a huge amount of data is monitored on a day to day basis, and businesses don't want to miss out on vital data changes.
Data visualization is the technique to represent data in visual graphics form by using elements such as charts, graphs, and maps, etc. Data visualization helps to see the trends and patterns in any data set clearly and in an easily comprehensible manner.
Datapoint is a unit of information, a single fact, an individual item, in the data that is being analyzed. Multiple data points create charts or trend lines.
Metadata is the data that summarizes information about other data. Metadata is a useful tool for finding and extracting relevant information from a huge dataset. Metadata usually provides information such as title, keywords, themes, context, and utility of the data.
Big data analytics
Big data analysis is the method of harnessing and analyzing a large amount of data for unearthing patterns, correlations, and hidden value in the massive data. Businesses use big data analytics for faster decision making, gauging ideas for rolling out new products and services, and gain competitive advantage by data-fuelled insights. Most of the Big data is generated from sensors, devices, transactions, web, and social media.
Granular data is the data that is derived from dividing large data into sub-divisions. For obtaining granular data, data is divided into the lowest possible extent. It helps data analysts to be more specific in confirming data facts.
Data mining is done to discover patterns in large data sets using artificial intelligence and machine learning.
Data Analysis Process: Then and Now. How has the data analysis process changed over time?
Data analysis has played a crucial role in the progress that human civilizations have made so far. The inscriptions on walls and caves were analyzed to learn about our history and ancient languages. Whatever we know about the world today, the solar system, the galaxies, etc. are possible due to data analysis. When Henry Ford measured the speed of assembly lines to increase the production of cars, he was using data analysis. W.D Gann, the legendary trader, studied data of the past 1000 years of wheat and accurately predicted that wheat would sell at $1.20 in September 1909. He also used data analysis. (Source-Ticker and Investment book by Richard D. Wykoff).
Data is everywhere, and humans have relied on data for many of their accomplishments. The data process evolved with time, and today most of it is automated and carried out by software applications.
Let us discuss the data analysis process in detail. The process involved in data analysis involves several different steps:
Step 1: Defining data prerequisites:
Before one goes for data analysis, one has to define the prerequisites for the data analysis process clearly:
- The questions for the data analysis: One must know why the data needs analysis? Clearly define the questions for which the answers are required.
- Data segregation points: One needs to identify how data will be grouped? List down the parameters for data grouping such as age, demographics, categories, colors, size, etc.
- Method: Deciding which method will be used for analysis is also critical.
- Time Frame: One needs to decide the time frame for which the data will be analyzed.
Step 2: Data collection:
Once the prerequisites are met, one needs to collect data using data collection methods such as:
- Getting data from already existing databases such as CRM database, inventory database, sales data, ledgers, bank statements, etc.
- One can also gather data through interviews, case studies, questionnaires, surveys, polls, expert opinions, website analytics, customer reviews, etc.
Step 3: Data Cleansing
Data cleansing or data cleaning method is the process to get rid of irrelevant, duplicate, inaccurate and incomplete data that is not required for data analysis. Data cleaning makes the data error-free and brings accuracy and precision in the final results.
Step 4: Data Processing
This step processes the data and analyzes it for various correlations, trends, and combinations to deliver valuable insights about the data. This is a very crucial step in the data analysis process, and generally, businesses use advanced data analytics tools to complete this step. Data visualization feature is used in this step to make the data analysis easy to understand.
Step 5: Data Interpretation
This is the final step of data analysis. The analyzed data is set for meaningful conclusions, findings, and interpretations. Data is interpreted for valuable analytics and solutions that can be applied for future action based on the data analysis results.
What are the challenges faced in data analysis?
- Collecting real-time data
- Getting rid of outdated data
- Presenting data in an understandable format
- Managing data from multiple sources
- Dealing with poor quality data
- Conducting data analysis accurately
- Shortage of skilled data analysts
- Maintaining the security and privacy of the organizational data
- Sharing the analytics in real-time with team members
- Another challenge is to incorporate data-driven insights into daily workflows
What is data analysis software?
Data analysis software is an application that helps in collecting, merging, cleaning, analyzing, and interpreting data. It also has features to present data in a visually appealing format. Data analysis software is used by managers, data scientists, statistical analysts, data analysts, etc. for reliable data analysis and insights.
What are the different types of data analysis software?
These types of data analysis software solutions are licensed, and businesses can install them within their IT infrastructure. On-Premise, data analytics systems are highly customizable in nature.
These types of online data analysis software are hosted, and third-party data analysis solution providers offer these tools. Web-based online data analysis tools are very popular applications as they don’t require any hardware installation and can run online with a login ID and internet connectivity.
These types of data analysis software solutions are hosted through third-party cloud service providers. These are popular with large organizations that require big data analytics, massive data storage capacity, and extreme flexibility.
These types of data analysis software allow users to customize the source code, alter inbuilt settings, and modify functions by adding extra plugins. When organizations need tailored solutions that are unavailable with any vendor, then open source data analysis software can be opted.
What are the benefits of data analysis software?
Data analysis software has the features to carry all the steps of the data analysis process. Apart from this, the other benefits are:
Timely decision making:
Unlocking the potential value of data requires analytics. Businesses can improve their knowledge and understanding of business processes, customers, campaigns, products, finances, employees, etc. through data analysis processes. Businesses can unveil the value hidden in data with the help of a data analysis software that removes the guesswork and provides key metrics required for better decision making. Data analysis software helps organizations make reliable decisions to meet customer expectations, mitigate global challenges, and stay competitive.
Provides valuable insights:
Without insights, data is of no use. Organizations that know what to do with data can capitalize it for long-term success. Continuous data analysis with real-time data provides businesses space and time to tailor services, alter campaigns, modify strategies, and optimize current processes to improve results.
Storing and retrieving data easily:
With quintillions Gigabytes of data produced every year in the world, storing and retrieving data from multiple sources and databases is a challenging task. Pre-planned data analysis programs with the help of analytics software make it easy to store and retrieve data. Data analytics applications support all types of databases and integrate with them smoothly to import data whenever required.
Optimizes various businesses activities:
Using a business analysis tool, various critical business activities such as budgeting, hiring, scheduling, advertising, inventory management, customer service, etc. can be optimized and improved.
Proper planning and execution:
Data software helps managers plan their course of action realistically and more accurately by providing essential information about the resources and time. Managers can forecast future actions from multiple perspectives and create a flexible but effective strategy that is easy to execute.
The amount of data that current-day businesses generate daily is too huge to manage manually. It is humanly not possible to study the data manually for insights. Therefore, data analysis software rescues managers from menial responsibilities and empowers them to use the data analysis application's insights directly.
Solves problems and eliminates errors:
Many times, businesses face issues of data mismatch, fraudulent transactions, errors in processes, etc. A robust data analysis software can help businesses to identify the leakages by analyzing the granular data.
What are the key features of data analysis software?
Data analysis software allows users to import data from various file formats such as CSV, excel sheets, HTML, etc. to the software for analysis. You can also import data from these sources to tables of the data analysis software. The data analysis software supports all types of databases, including NoSQL and SQL databases. Data stored in cloud servers or local databases can also be imported.
For regular data analysis, users can set periodic or real-time synchronization of the data between the data analysis software and the database of their choice. By enabling real-time synchronization, data will get uploaded to analytics software automatically.
Importing data from popular applications
Most advanced data analysis software supports popular business applications such as CRM, Sales, Marketing, Project Management, etc. for direct database collaboration and faster analysis.
Data analysis software allows users to merge data from two applications or sources to create meaningful results.
Data Visualization tools
Data analysis software has inbuilt data visualization tools to make a variety of charts, pivot tables widgets, graphs, tables, etc. It also creates insightful reports and custom dashboards with the intuitive drag and drop interface. Other options, such as insert images, format text, create a design layout, drill-downs, contextual filters, and many more, help in visualizing data effectively. Some data analysis software come with interactive map integration to visualize your data geographically.
Various analytics and reporting tools
Data analysis software is capable of in-depth analysis of various metrics and key performance indicators. It comes with extensive analytical capabilities and core functions such as forecasting, projection, and reporting.
Sharing and collaboration
Data analysis software enables managers to share real-time data analytics with their team for informed decision making and teamwork.
Data security tools
Data analysis software comes with inbuilt encryption, firewalls, regular backups, etc. features for security and data usage privacy.
- Set Data alerts
- Set access control
- Inbuilt business intelligence
What are the current trends in the data analysis field?
Augmented analytics uses machine learning, NLP (Neurolinguistics Programming), and artificial intelligence to gather business analytics. Augmented analytics produces faster results, deeper insights, and speedy visualization.
Conversational analytics uses processes such as textual data mining, sentiment analysis, and social listening to derive insights about consumer behavior and satisfaction.
Incorporating data-driven culture into organizations is an important trend in the data analytics world. Business Intelligence professionals and managers are prioritizing the implementation of a culture where decisions are based on data-driven insights.
Data Analysis Automation
Organizations are implementing automated processes to handle massive data sets. Automated data analysis and automated decision making are popular trends in data analytics
Graph analytics is the process of analyzing data in a graph format. It requires managers to analyze data with a database platform that supports graphs format. Graph analytics is getting traction for solving complex problems. Graph analytics is used for revealing relationships between logical concepts and people, organizations, and related entities.
Continuous intelligence (CI) uses real-time data to enable automated decision-making. This technique generates continuous insights and real-time analytics for decision-making. In June 2020, when oil prices went below zero levels, most of the data analysis systems struggled to make trading decisions as data analysts never thought that oil could go below zero levels. In such scenarios, a continuous intelligence method has a greater advantage over other data feeding algorithms as it continuously updates its data feeds to automatically make real-time decisions.
Big data analytics
With billions of connected devices, the data created by these is massive and overwhelming. Big data analytics attempts to map this data for meaningful insights. There is a growing trend among organizations to utilize big data for providing personalized services to their customers.
What is the role of data analysis software in various industries?
Banks and NBFCs can deploy data analytics software to become hyper-intelligent organizations that provide personalized customer experiences and complete regulatory compliance for its services. Data analysis helps financial organizations in the early detection of frauds, risk management, operational excellence, and increasing engagement with customers through personalized offers. Even Insurance and Mutual funds companies can provide personalized offers to their clients based on their preferences. The financial sector can use big data analytics to predict future market trends.
Data integration and systematic analysis of data with the help of data analytics software enable improved clinical decisions. It also helps prevent healthcare financial frauds and improves patient satisfaction. Data analysis of various medical, and wearables devices such as Fitbits helps physicians to monitor their patient's health remotely based on the analysis of data received from these devices.
Manufacturing data analytics solutions enable managers to balance production operations for cost reduction and risk management. In manufacturing, advanced analytics helps operations managers gain insights from historical processes to optimize the current ones for better yields. Companies can also capitalize on big data to optimize their manufacturing cycles.
Data analysis software can be used to optimize retail operations, merchandise planning, controlling the supply chain, and making the customer journey a pleasant experience. Using past data, big data analytics, and AI, retail businesses can predict customers' future behavior, develop personalized marketing campaigns, and improve employee training.
Educational institutes can improve learning environments, teaching techniques, and learning outcomes by deploying data analytics techniques. Data received from students' feedback, parents' feedback, evaluation, results, etc. can be analyzed to improve the learning process and reduce dropout rates.
Logistics and Transportation
Data analysis enables Transportation and Logistics Companies to understand the reasons behind shipping delays and performance across various shipping routes. Using such insights, logistics companies can optimize delivery times and reduce costs.
How to choose the best data analysis software for your business?
- Does it provide all features, functionality, integrations, and settings for your analysis needs?
- Is the data analysis software user-friendly?
- Can you customize the data analysis software or request to add more functions over time?
- Does the data analytics application deploy advanced security features such as encryption, firewall, access controls, backup plans, etc. ?
- Can it work with all types of databases to retrieve your data?
- Is your data analysis software vendor well-experienced in the data analysis field?
- Does the software provider offer a free trial/free demo of its services?
- Are you going to get free live training/demo or customer support services from the software provider?
If the answer is 'yes' to above all questions, then you have all green ticks to go ahead and purchase the software.
What is the average cost of buying data analysis software?
The costs of the data analysis system depend on a large number of factors such as:
- The number of users
- Features and functions it offers
- The amount of data it can process in a given time frame
- The maximum data analyzing capacity
- Quality of service offered
- Integrations it offers
- User interface and experience
These variations can change the cost factor and estimations of the data analysis software significantly.
The enterprise version of Qlik Sense will cost you $70 a month per user, while the basic version is available for $30 per user per month. Parse.ly data analysis software will cost $1500 per month for its Growth plan and $500 per month for the Starter plan. Freemium data analysis software such as Cluvio comes with a free version for single users with a maximum of three dashboards. Zoho Analytics also offers a freemium version with a free plan for two users, while enterprise plans for 50 users will cost $495. If you need more free data analysis software, try Anaconda, Kyubit, AnswerDock, and MeMSQL.
For other online data analysis tools such as SAS, Stata, Solver Suite, WebFocus, you need to contact the vendor for detailed pricing. You can get the list and all information from the GoodFirms data analysis software Page.
Why refer to GoodFirms’ list of data analysis software?
Goodfirms is a leading IT research company renowned in the IT field for its database of thousands of IT services and products. GoodFirms has an impressive track record of providing buying support and assistance to B2B IT service seekers. GoodFirms researches, creates, organizes, and regularly updates the huge database of software services and products. It lists companies and their products based on their performance, features, functionalities, updates, pricings, and user reviews. Currently, GoodFirms houses a massive list of 60000+ software products and services. GoodFirms has also received 30000+ reviews of these services and products from verified users. You are advised to go through the extensively researched best data analysis software list to find the best suitable one for yourself. You can filter the data analysis software solutions based on various criteria such as features, deployment type, pricings, and many more. You can also read reviews to have better insights about software services.