With the growth of new data science technologies and business intelligence (BI) goals increasing at enterprises, the market for data analytics software is growing at a rapid rate.
See below to learn about some of the top players in the data analytics market and how these data tools can be used for a range of internal and external business functions.
A Closer Look at the Data Analytics Market
- Data analytics
- Data analytics market
- Data analytics features
- Benefits of data analytics
- Data analytics use cases
- Data analytics software providers
Data analytics is one of the most important data science practices that involves everything from collecting and storing data to processing data and using tools like data visualizations and models to make meaning out of data sets.
These are some of the most commonly used approaches to data analytics:
- Text analytics
- Descriptive or statistical analytics
- Inferential analytics
- Diagnostic analytics
- Exploratory analytics
- Predictive analytics
- Prescriptive analytics
More on this topic: Best Data Analysis Methods 2021
Data analytics market
The data analytics market is expected to reach $132.9 billion by 2026, compared to $23 billion in 2019, according to Market Research Future.
Growth is coming from many different industries, but it is primarily driven by internal business intelligence goals. The North American market is predicted to remain dominant, but the Asia-Pacific region is rising in its data analytics use and capabilities more quickly than other regions, with China at the front of the pack.
Data analytics features
Strong data analytics platforms include the following set of key features:
- Data analysis: Data analysis is the first step in data analytics where the data is cleaned up and analyzed at a higher, big-picture level.
- Dashboards: Dashboards are the interfaces that make it easier for data professionals and other relevant business users to see and launch different analytical projects.
- Custom and templated reports: Because not all relevant employees and stakeholders will regularly access the dashboard, reports are a necessary part of digesting and presenting data analytics results in a company.
- Data visualization and modeling: Many analytical processes and results are difficult to understand in their initial forms. Visualization and modeling are data illustrations that make it easier to understand results and how they connect to other business data.
Benefits of data analytics
The data analytics software market benefits businesses that would otherwise have trouble making sense of operational data.
Some key benefits that data analytics tools can provide include:
- Learning more about current data and using current and past information for predictive analytics and future planning.
- Predictive analytics can cover employee and customer metrics, such as churn analysis and customer segmentation, and future revenue.
- A fuller understanding of structured, unstructured, qualitative, and quantitative data across the business. Even if this wide variety of data is all in one database, different data analytics tools can organize and elucidate meaning from the data.
- The ability to enable machine learning (ML) and artificial intelligence (AI) tools via data annotation means that data analytics powers automated processes and new business and consumer technologies.
- Additional information for network troubleshooting and security needs, which is ushering in an AIOps future.
Data analytics use cases
Here’s how a couple of users are using data analytics tools to optimize their business data:
“We use SAS for predictive modeling, ad hoc statistical analysis, and automated report[ing], all of which were absent before the software arrived here. This tool has allowed us to get to answers more quickly, while utilizing our data the best we can.” -user product review of SAS Analytics at TechnologyAdvice
“[Birst’s] end-to-end BI on the cloud, including storage, database, modeling and GUI development. One-stop shop for all our analytical BI. The most important consideration is that I do not have to maintain DR and HA procedures. I do not have to wait for DBA, storage or system guys.” -user product review of Birst at TechnologyAdvice
Data analytics software providers
The data analytics market offers a wide variety of tools for different data analysis goals:
- Salesforce Tableau: This generalized tool for data analytics is a top competitor with strong data visualizations, user-friendly dashboards, and visual data mining for users of varying data science experience.
- Microsoft Power BI: One of the most widely used data analytics tools on the market, this Microsoft software offers ML/AI-powered analytics with specialty features like image analytics.
- Google Analytics: Perhaps one of the best-known data software options on this list, Google Analytics is a “freemium” web analytics software that helps users track web traffic and visitor metrics.
- HubSpot Analytics: This customer relationship management (CRM) data analytics tool makes it possible for sales and marketing teams to see how their teams’ data intersects with each other and customer outcomes.
- KNIME: This free, open-source solution allows users to pull data from other sources and makes operations like visual programming and tool blending easier.
- RapidMiner: An excellent mathematical analytics tool, RapidMiner combines statistical data analysis, mathematical inputs, and traditional business intelligence strategies in a predictive analytics approach.
- Birst: A tool that is immersed in an enterprise resource planning (ERP) environment, Birst is a top option on the market for automating data warehouse schemas.
- TIBCO: TIBCO’s solutions use ML/AI operations and data mining to analyze streaming data from sources like IoT and mobile devices.
- MicroStrategy: MicroStrategy focuses on combining the strengths of other data analytics software, using API connectors to link different competitors’ resources.
- Sisense: Sisense is a top choice for cloud-based data analytics, but its user interface and modules are designed for the more advanced data science expert.
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