Top 24 Tools For Data Analysis And How To Decide Between Them

Modern businesses are reliant on data analysis. The choice of the right tool to analyze data is hard as not every business needs are covered. Let’s look at the key factors to help you choose the right data analytics tool for your company. Next, we will examine some of the most widely used options available today.

Before you can evaluate the tools available, there are some things that you need to remember. Before you can evaluate the available tools, it is important to first identify the data types your company needs to analyze. To create an analytics source, you will need to identify data sources, tables, and columns, and then replicate them to a database warehouse. Data governance and security will be important. Data sharing between departments, such as among departments, should be protected by access control and permissions.

How to select a data analysis software

Once you have your data, you are able to try different tools to analyze it. How do I find the right tool for my company? Consider your company’s business requirements and determine who will use your analytics tool. The tool should be suitable for all users, not just data scientists or data analysts. Some platforms allow you to interact with code and create iterations using SQL. Others focus on more point-and-click analysis, which is better for non-technical users. Your enterprise should have the ability to view the tool’s visualizations.

Take into account the data modeling capabilities of a tool. Some tools have a data modeling capability or support a specific layer. You can use a tool like SQL to model your data, but not one that supports it.

Last, think about licensing and price. Some products are completely free while others require subscription or licensing fees. Users should not overlook the many strong, free options available.

Let’s now look at the criteria for data analysis tools. Before moving on to the more popular options, we’ll begin by discussing the eight platforms within Gartner’s Magic quadrant for Analytics.

1. Microsoft PowerBI
Microsoft Power BI provides a powerful business intelligence platform that supports dozens data sources. You can create, share, and collaborate on reports, visualizations, dashboards, and other data. Power BI makes it easy to share a set of reports and dashboards. Power BI can also be used to create machine learning models that integrate with Azure Machine Learning.

2. SAP BusinessObjects is a business intelligence software solution.
SAP BusinessObjects is a suite business intelligence applications designed to help you discover, analyze, and report on data. Although these tools are intended for non-technical users, they can be used to perform complex analysis. BusinessObjects is integrated with Microsoft Office products. Business Analysts can quickly switch between Excel reports and BusinessObjects spreadsheets. You can also use it to perform self-service predictive analysis.

3. Sisense
Sisense, a data-analytics platform, is designed for both technical and business developers. It helps them visualize and process all of their business information. It offers a wide range of drag-and drop tools as well as interactive dashboards that allow for collaboration. Sisense’s unique In-Chip technology optimizes computations to use CPU caching instead of slower RAM. For certain workflows, this could result in 10-100x faster calculation.

4. TIBCO Spotfire is a business intelligence platform that provides users with powerful data analytics and visualizations. It enables people to easily uncover insights in their data and make informed decisions.
TIBCO Spotfire offers data analytics that combines natural language searches with AI-powered data insights. It can create reports for both desktop and mobile devices. Spotfire provides tools that can be used to create predictive analytics models.

5. Thoughtspot
Thoughtspot, an analytics platform, allows users to examine data from different sources via reports and natural-language searches. SpotIQ is an artificial intelligence system that automatically finds patterns to aid users in discovering them. To help reduce data silos, the platform allows users to automatically join tables derived from multiple data sources.

6. Qlik
Qlik offers self-service data analytics, business intelligence and cloud deployment support. The tool offers both technical and untechnical users strong support for data discovery and exploration.
Qlik can create many different types of charts using embedded SQL or drag-and-drop module customizations.

7. SAS Business Intelligence
SAS Business Intelligence offers a range of self-service analytical applications. You can push reports to mobile apps with many of its built-in collaboration capabilities. SAS Business Intelligence can be expensive, but it is more flexible and comprehensive than other platforms. Its versatility may be worth it for larger enterprises.

8. Tableau
Tableau allows users to create and share reports across desktop and mobile platforms. It can also be embedded in applications. It is available on-premises or in the cloud. VizQL, the core query language of Tableau, is used for much of Tableau’s platform. This allows users to drag-and drop visualization components and dashboards into powerful back-end query languages that reduce the need for optimizations. However, Tableau lacks support for advanced SQL queries.

9. Google Data Studio
Google Data Studio allows you to create dashboards and visualize data. It integrates with Google Analytics, Google Ads, Google BigQuery, and Google Analytics. Data Studio can be used to analyze Google data because it integrates with other Google services. Marketers can create dashboards to analyze their Google Ads and Analytics data in order to understand customer retention and conversion. Data Studio is able to work with data coming from many sources. However, it must first replicate the data to BigQuery through a data pipeline like Stitch.

10. Redash
Redash is an affordable tool that allows you to query data sources and build visualizations. It is open-source, and organizations can get started quickly with a hosted version. Redash’s core component is the query editing interface. This allows for easy queries to be written, schema exploration, and integration management. Redash stores query results and allows users to schedule automatic updates.

11. Periscope Data
Sisense acquired Periscope Data in order to provide integrations for many popular data warehouses. Data can be transformed by technical analysts using SQL, Python, and R. Users with less technical skills can create and share dashboards. Periscope Data is also certified in security, including HIPAA-HITECH.

12. Metabase
Metabase, an open-source business intelligence and analytics tool, is free. Metabase lets users “ask questions” regarding data. This allows non-technical users to access a point and click interface to query construction. This is great for filtering and aggregations, but more advanced users can use raw SQL to perform more complicated analysis. Metabase can also push analytics results to Slack.

13. A Jupyter Notebook is an open-source web application that enables users to create and share documents that contain live code, equations, visualizations, and narrative text. It is a powerful tool for data science, interactive computing, and scientific research.
Jupyter Notebook (free, open-source web app) can be installed using the Anaconda Platform or Python’s Package Manager, pip. Developers can create reports using live code. More than 40 programming languages are supported by the system. Jupyter Notebook, formerly IPython Notebook, was initially programmed in Python. It allows developers to use the many Python packages for visualizations and analytics. There are many developers who use the tool in different languages.

14. IBM Cognos
IBM Cognos provides business intelligence platforms that include built-in AI tools. These AI tools can reveal hidden patterns in data and help explain them in plain English. Cognos can also be used to automate data preparation, which automatically cleans and aggregates data sources. This facilitates the integration of data sources for analysis and allows for rapid experimentation.

15. Chartio is a powerful, easy-to-use analytics platform.
Chartio integrates with different data warehouses, and allows for easy imports of files such spreadsheets. Chartio offers a visual representation of SQL that is unique. It allows business analysts who don’t know the language to create queries with a click.

16. Fashion
Mode is an analytical platform that gives data scientists an easy-to-use and iterative working environment. It features an interactive SQL editor with notebook, visualization, and collaboration tools. Mode’s unique data engine, Helix, streams data from other databases and stores it within memory for interactive and fast analysis. It can store up to 10GB data in memory for analysis.

17. KNIME
KNIME is shorthand for Konstanz Information Miner. It’s a free and open-source platform that allows data integration, processing, visualization, reporting, and reporting. It integrates machine learning and data-mining libraries with little to no programming. KNIME works well for data scientists that need to use machine learning and data mining libraries, but do not necessarily have programming skills. The interface’s graphical interface permits for easy modeling and analysis.

18. Gazer
Looker, a cloud-based data analytics and business intelligence platform, is available. It provides automatic data model generation by scanning data schemas and inferring relationships between tables. A built-in editor allows data engineers to modify the models.

19. RapidMiner is a software suite that provides machine learning, data mining, and predictive analytics capabilities.
RapidMiner is a technology that allows users to combine, clean, and transform data prior to running predictive analytics or statistical models. The interface is very simple and users can do most of the above using it. RapidMiner supports Python and R scripts. Additionally, the company offers a wide range of third-party plugins. RapidMiner’s graphical interface allows analysts to create and run their own models.

20. Domo is a virtual assistant that simplifies everyday tasks.
Domo has more than 1000 built-in connectors that allow users transfer data from and to cloud-based systems. Domo supports developers building custom apps that connect with the platform. They can also access the connectors and visualization software immediately. Domo is a single platform which includes an ETL software and data warehouse. This means that businesses with a data pipeline or data warehouse setup may need to consider Domo.

21. Oracle Analytics Cloud
Oracle Analytics Cloud includes a number of cloud business analytics and intelligence applications. It helps large businesses transition from legacy systems to modern cloud platforms. You can use its many analytics features to create simple visualizations or use machine learning algorithms for insights.

22. Reality
R is an open-source programming language. It also provides a computing environment. R focuses on statistics, data visualization, and graphics. R includes many graphical tools, over 15,000 open-source packages, and many that can be used to load, manipulate, model, or visualize data. Although R allows technical analysts with programming skills, it is not suitable for users who don’t have the necessary programming skills.

23. Python
Python, a high-level open-source programming language, is used frequently by technical analysts and data scientist. It has over 200,000 available programs and boasts more global developers than Java. Python is capable of performing many analyses by itself and can also integrate with third party packages for data visualization and machine learning. Plotly and Matplotlib are some of the most popular data visualization tools. You can also use Python to interface with other analytics systems.

24. Excel
Microsoft Excel, the most used spreadsheet tool, is widely used to build analyses and manipulate data. Excel is a well-known tool for supporting almost all types of analytics workflows. It has been developed over decades and is extensible through Visual Basic, the native programming language. Excel is good for basic analysis. However it is not designed for large-scale data analysis. Excel’s limit of 1 million rows means that it cannot be used to collaborate or version. Enterprises should be looking at cloud-based platforms that can handle large, collaborative analyses.

Stitch Data Analysis Tools
Data warehouses are the best place to store data for data analysis. Stitch can be used to create a data warehouse from more than 100 sources. Stitch is free to try today.