Data analysis is a core practice of modern businesses. Choosing the right data analytics tool is challenging, as no tool fits every need. To help you determine which data analysis tool best fits your organization, let’s examine the important factors for choosing between them and then look at some of the most popular options on the market today.
There are a few things to take care of before evaluating the available tools. You should first understand the types of data your enterprise wants to analyze, and, by extension, your data integration requirements. In addition, before you can begin analyzing data, you’ll need to select data sources and the tables and columns within them, and replicate them to a data warehouse to create a single source of truth for analytics. You’ll want to assess data security and data governance as well. If data is shared between departments, for example, there should be access control and permission systems to protect sensitive information.
How to choose a data analysis tool
Once you have data ready, you can try analyzing it using different tools. How do you find one that’s a good fit for your company? Start by considering your organization’s business needs and learning who will be using your analytics tool. Will it be used by sophisticated data analysts and data scientists, by nontechnical users who need an intuitive interface, or should it suit both kinds of users? Some platforms provide an interactive experience for iterating on code development — typically using SQL — while others focus more on point-and-click analysis for less technical users. The tool should also provide support for visualizations relevant to your enterprise.
Consider a tool’s data modeling capabilities. Some support a semantic layer or can perform data modeling themselves. If you want to use one that doesn’t, you’ll have to use SQL or a tool like dbt to model your data prior to analysis.
Finally, consider price and licensing. Some offerings are free, while others charge licensing or subscription fees. The most expensive tools are not necessarily the most feature-complete, and users should not ignore the many robust free solutions available.
Now that you know what factors to look for in a data analysis tool, let’s jump into the list. We’ll start with discussing the eight platforms in the Visionaries band of Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms before covering other popular options.
1. Microsoft Power BI
Microsoft Power BI is a top business intelligence platform with support for dozens of data sources. It allows users to create and share reports, visualizations, and dashboards. Users can combine a group of dashboards and reports into a Power BI app for simple distribution. Power BI also allows users to build automated machine learning models and integrates with Azure Machine Learning.
2. SAP BusinessObjects
SAP BusinessObjects provides a suite of business intelligence applications for data discovery, analysis, and reporting. The tools are aimed at less technical business users, but they’re also capable of performing complex analysis. BusinessObjects integrates with Microsoft Office products, allowing business analysts to quickly go back and forth between applications such as Excel and BusinessObjects reports. It also allows for self-service predictive analytics.
Sisense is a data analytics platform aimed at helping both technical developers and business analysts process and visualize all of their business data. It boasts a large collection of drag-and-drop tools and provides interactive dashboards for collaboration. A unique aspect of the Sisense platform is its custom In-Chip technology, which optimizes computation to utilize CPU caching rather than slower RAM. For some workflows, this can lead to 10–100x faster computation.
4. TIBCO Spotfire
TIBCO Spotfire is a data analytics platform that provides natural language search and AI-powered data insights. It’s a comprehensive visualization tool that can publish reports to both mobile and desktop applications. Spotfire also provides point-and-click tools for building predictive analytics models.
Thoughtspot is an analytics platform that allows users to explore data from various types of sources through reports and natural language searches. Its AI system, SpotIQ, finds insights automatically to help users uncover patterns they didn’t know to look for. The platform also allows users to automatically join tables from different data sources to help break down data silos.
Qlik provides a self-service data analytics and business intelligence platform that supports both cloud and on-premises deployment. The tool boasts strong support for data exploration and discovery by technical and nontechnical users alike. Qlik supports many types of charts that users can customize with both embedded SQL and drag-and-drop modules.
7. SAS Business Intelligence
SAS Business Intelligence provides a suite of applications for self-service analytics. It has many built-in collaboration features, such as the ability to push reports to mobile applications. While SAS Business Intelligence is a comprehensive and flexible platform, it can be more expensive than some of its competitors. Larger enterprises may find it worth the price due to its versatility.
Tableau is a data visualization and analytics platform that allows users to create reports and share them across desktop and mobile platforms, within a browser, or embedded in an application. It can run on the cloud or on-premises. Much of the Tableau platform runs on top of its core query language, VizQL. This translates drag-and-drop dashboard and visualization components into efficient back-end queries and minimizes the need for end-user performance optimizations. However, Tableau lacks support for advanced SQL queries.
9. Google Data Studio
Google Data Studio is a free dashboarding and data visualization tool that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Thanks to its integration with other Google services, Data Studio is great for those who need to analyze their Google data. For instance, marketers can build dashboards for their Google Ads and Analytics data to better understand customer conversion and retention. Data Studio can work with data from a variety of other sources as well, provided that the data is first replicated to BigQuery using a data pipeline like Stitch.
Redash is a lightweight and cost-effective tool for querying data sources and building visualizations. The code is open source, and an affordable hosted version is available for organizations that want to get started fast. The core of Redash is the query editor, which provides a simple interface for writing queries, exploring schemas, and managing integrations. Query results are cached within Redash and users can schedule updates to run automatically.
11. Periscope Data
Periscope Data — now owned by Sisense — is a business intelligence platform that supports integrations for a variety of popular data warehouses and databases. Technical analysts can transform data using SQL, Python, or R, and less technical users can easily create and share dashboards. Periscope Data also boasts a number of security certifications, such as HIPAA-HITECH.
Metabase is a free, open source analytics and business intelligence tool. Metabase allows users to “ask questions” about data, which is a way for nontechnical users to use a point-and-click interface for query construction. This works well for simple filtering and aggregations; more technical users can go straight to raw SQL for more complex analysis. Metabase also has the ability to push analytics results to external systems like Slack.
13. Jupyter Notebook
Jupyter Notebook is a free, open source web application that can be run in a browser or on desktop platforms after installation using the Anaconda platform or Python’s package manager, pip. It allows developers to create reports with data and visualizations from live code. The system supports more than 40 programming languages. Jupyter Notebook — formerly IPython Notebook — was originally programmed using Python, and allows developers to make use of the wide range of Python packages for analytics and visualizations. The tool has a wide developer community using other languages as well.
14. IBM Cognos
IBM Cognos is a business intelligence platform that features built-in AI tools to reveal insights hidden in data and explain them in plain English. Cognos also has automated data preparation tools to automatically cleanse and aggregate data sources, which allows for quickly integrating and experimenting with data sources for analysis.
Chartio is a self-service business intelligence system that integrates with various data warehouses and allows for easy import of files such as spreadsheets. Chartio has a unique visual representation of SQL that allows for point-and-click construction of queries, which lets business analysts who aren’t familiar with SQL syntax modify and experiment with queries without having to dig into the language.
Mode is an analytics platform focused on giving data scientists an easy and iterative environment. It provides an interactive SQL editor and notebook environment for analysis, along with visualization and collaboration tools for less technical users. Mode has a unique data engine called Helix that streams data from external databases and stores it in memory to allow for fast and interactive analysis. It supports in-memory analysis of up to 10GB of data.
KNIME — short for the Konstanz Information Miner — is a free, open source data analytics platform that supports data integration, processing, visualization, and reporting. It plugs in machine learning and data mining libraries with minimal or no programming requirements. KNIME is great for data scientists who need to integrate and process data for machine learning and other statistical models but don’t necessarily have strong programming skills. The graphical interface allows for point-and-click analysis and modeling.
Looker is a cloud-based business intelligence and data analytics platform. It features automatic data model generation that scans data schemas and infers relationships between tables and data sources. Data engineers can modify the generated models through a built-in code editor.
RapidMiner provides all the technology users need to integrate, clean, and transform data before they run predictive analytics and statistical models. Users can perform nearly all of this through a simple graphical interface. RapidMiner can also be extended using R and Python scripts, and numerous third-party plugins are available through the company’s marketplace. However, the product is heavily optimized for its graphical interface so that analysts can prepare data and run models on their own.
Domo provides more than 1,000 built-in integrations — called connectors — that allow users to transfer data to and from on-premises and cloud external systems. Domo also supports building custom apps that integrate with the platform, which allows developers to extend the system with immediate access to the connectors and visualization tools. Domo comes as a single platform that includes a data warehouse and ETL software, so businesses that already have their own data warehouse and data pipeline set up may want to look elsewhere.
21. Oracle Analytics Cloud
Oracle Analytics Cloud is a suite of cloud business intelligence and analytics applications. It’s focused on helping large enterprises transition their legacy systems to a modern cloud platform. Users can take advantage of its wide range of analytics features to do everything from producing simple visualizations to using machine learning algorithms to obtain insights from data.
R is an open source programming language and computing environment with a focus on statistics and graphical data visualization. R features numerous graphical tools and over 15,000 open source packages available, including many for loading, manipulating, modeling, and visualizing data. The environment allows technical analysts with programming skills to build almost any type of data analysis, but users without those programming skills should look elsewhere.
Python is an open source, high-level programming language that’s often used by technical analysts and data scientists. It now boasts more worldwide developers than Java and has more than 200,000 available packages. Python can handle many different analyses on its own, and can integrate with third-party packages for machine learning and data visualization. Popular data visualization packages include Matplotlib, Plotly, and Seaborn. Python is also used as a programming interface to other analytics systems.
Microsoft Excel is the most common tool used for manipulating spreadsheets and building analyses. With decades of development behind it, Excel can support almost any standard analytics workflow and is extendable through its native programming language, Visual Basic. Excel is suitable for simple analysis, but it is not suited for analyzing big data — it has a limit of around 1 million rows — and it does not have good support for collaboration or versioning. Enterprises should consider more modern cloud-based analytics platforms for large and collaborative analyses.
Using data analysis tools with Stitch
Data analysis tools work best with accessible data centralized in a data warehouse. Stitch is a simple data pipeline that that can populate your preferred data warehouse for fast and easy analytics using more than 100 data sources. Try Stitch for free today.