Top Data Analytics Tools & Software 2022

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Data analytics tools and software deliver deep insights into wide-ranging business events. What data analytics and big data are used effectively, they can fuel faster and better decision-making. This offers significant competitive advantage and boosts digital transformation.

Clearly, data mining using data analytics software is at the center of business success; and of course, using optimal data mining techniques makes all the difference. Yet the volume, variety and velocity of big data keeps on growing—making the task more challenging than ever. Some companies hire data analysts and business intelligence professionals; other have a team of data scientists helping them decode various data sources. In sum, having the right data analytics software tools is part of the solution; having skilled big data experts is just as important.

Also see: Top Business Intelligence Software 

Like most software, selecting the right data analytics platform is critical. Ensuring that a data analytics software connects and interacts with other data sources is essential. It’s critical for monitoring both your edge computing deployment and cloud provider. Likewise, monitoring data in motion across an enterprise and out to a supply chain is increasingly important.

What Are Features and Benefits of Data Analytics Software?

Best-in-class big data analytics solutions offer numerous features and capabilities for making sense of data at all levels. These include real-time visualizations, machine learning and AI capabilities and, in some cases, digital twins. They are powerful tools used by data scientists, data analysts, and other business intelligence pros. Understanding what precisely a data solution delivers is vital as organizations look to build out more expansive and complex data frameworks.

• How to Choose Data Analytics Tools
• Top Data Analytics Tools & Software
• IBM
• Microsoft
• MicroStrategy
• Qlik
• SAP
• SAS
• Sisense
• Tableau
• ThoughtSpot
• TIBCO
• Apache
• Google
• Looker
• Additional Market Leaders: Data Analytics Tools
• Vendor Comparison Chart: Top Data Analytics Software

Also see: Top Data Mining Tools 

How Do You Choose Data Analytics Software?

Selecting a platform for data analysis isn’t a job for the faint of heart. There are numerous factors, features and frameworks to consider. Here are three critical steps that will guide you to the right decision:

• • Analyze your needs – with an eye toward your particular staff. The process starts with an evaluation of what  your organization’s data requirements are and what objectives it has. It’s important to look at who will be using the data – line of business users versus data scientists, for example; how widely used do you expect the platform to be? Does it need to be robust enough for a team of data analysts? Additionally, understand what data sources are required to build models and insights, and what type of analytics is required: data visualizations, statistical analysis, predictive analytics or other specialized needs.
  • Review vendors – think scalability. It’s essential to know whether a big data analytics solution can manage data effectively and consistently deliver the desired results. There’s also the issue of scalability. As supply chains and business partnerships expand, data analytics tools and business intelligence applications must be equipped to ingest data from new sources, process this data effectively and produce actionable information and results. Do you need a data warehouse? A date warehouse helps organize, prepare and data mine your data sources – it’s an integral part of an advanced business intelligence solution.
  • Select a solution – with the understanding that switching is hard. Changing platforms is expensive and extraordinarily complicated. Consequently, it’s important to match your organization’s needs with the right solution provider on the first try; it’s worth more homework upfront. Key factors in selecting a vendor include speed, performance, user interface (UI) and usability (UX), flexibility, scalability, security, the vendor’s roadmap, and the vendor’s commitment to support. Pay particular attention to the vendor’s service level agreement (SLA). In the end, the upfront price isn’t as important at total cost of ownership (TCO).

Also see: Top Data Visualization Tools 

Top Data Analytics Tools & Software

Here are 10 of the top big data analytics software providers:

IBM

Key Insight: Big Blue offers a wide array of data analytics solutions and tools. However, Cognos Analytics with Watson is a leader in delivering insights through data visualizations. It taps the Watson AI and machine learning engine to blend data and deliver broad and deep insights. The platform offers natural language processing and contextual forecasting, including predictive analytics. It also includes integration with social media platforms.

Pros

• • • Powerful ad hoc reporting tools.
    • Advanced AI through the Watson platform.
    • Suitable for line of business users as well as data scientists.
    • Strong compliance and security features, including single sign on and object level security.
    • On premises and cloud options available.

Cons

• • • Better suited to existing IBM customers; the platform can be difficult to integrate with outside data tools.
    • The analytics dashboard and reporting functions is geared for pro users.
    • Large footprint that consumes significant resources.

Microsoft

Key Insight: Power BI is an analytics software platform optimized for Azure cloud. It delivers rich data visualizations through a highly scalable self-service model. The platform supports end-to-end business solutions by connecting Power BI with other Microsoft Power Platform Products—and to Microsoft 365, Dynamics 365, Azure, and hundreds of other apps. It is ranked as a Leader by both Gartner and Forrester.

Pros

• • • A top performing platform for AI and ML.
    • Strong data ingestion engine and data management functions.
    • Superior data visualizations.
    • Enormous user base translates into frequent updates and strong community support.

Cons

• • • Difficult to use with non-Microsoft tools and applications.
    • Can have a steep learning curve.
    • Not a good fit for some mobile platforms and devices.
    • Premium tier is expensive.

MicroStrategy

Key Insight: The vendor bills its BI and Analytics platform as a way to embed “intelligence everywhere.” It connects more than 200 data sources—including top platforms like Snowflake—en route to real-time visualizations for both PCs and Macs. It supports location-based analysis and delivers self-service dashboards that can be used for sophisticated drill-down analysis.

Pros

• • • Powerful engine integrates with most major data platforms through a robust set of APIs.
    • Strong support for mobile devices.
    • Solid security features are built into the platform.
    • Specialized templates and tools for vertical industries such as finance, healthcare, retail, tech and government.

Cons

• • • Interface can be challenging.
    • Unstructured data can be difficult to integrate.
    • User base isn’t as large as other vendors.

Qlik

Key Insight: A longtime vendor in the BI and data analytics space, Qlik offers a moderately priced solution, Qlik Sense, that delivers robust functionality on-premises or in the cloud. It ties together existing databases and data sources and provides self-service visualizations and reporting that can be used across different groups, departments and functions. The platform incorporates AI and ML to deliver active intelligence.

Pros

• • • The platform offers a strong dashboard and easy-to-use tools.
    • Highly scalable and flexible analytics capabilities.
    • Capable of handling large volumes of data.
    • Supports multi-cloud infrastructures; includes strong governance features.
    • Integration with numerous other data tools, including Tableau and Power BI.

Cons

• • • May require customization and third-party extensions.
    • Lacks some key reporting and exporting capabilities.
    • Lower vendor profile and a smaller user base means less community-based support. 

SAP

Key Insight: SAP’s presence in the enterprise application space makes it a good choice for organizations already on the vendor’s platform. SAP Analytics Cloud delivers a streamlined solution with advanced predictive analytics and planning functions. It delivers powerful self-service visualization and simulation tools, real-time insights and integration with numerous outside data sources.

Pros

• • • Delivers a cloud native platform.
    • Powerful dashboard delivers broad and deep insights into data.
    • Supports numerous types of analysis, including visualizations, predictive analytics, augmented analytics and statistical analysis.
    • Offers strong AI and ML capabilities.

Cons

• • • Can be complex and difficult to set up.
    • No on-premises solution.
    • Expensive, particularly for small- and medium-size organizations.
    • Limited support for off-premises applications running on desktops and mobile devices.

SAS

Key Insight: A pioneer in the data analytics software space, SAS offers a sophisticated framework for data analytics. This includes numerous applications that address different requirements. Its visual analytics solution is among the most advanced available, offering a sophisticated dashboard, a low code framework and AI/ML. It connects to numerous data sources, performs interactive data discovery and accommodates augmented analytics, chat-enabled analytics, location analytics and much more.

Pros

• • • Fast and efficient data processing, including strong AI and ML capabilities.
    • Flexible low-code environment for building mobile apps.
    • Powerful security, administration and governance features.
    • Drag-and-drop interface is easy to use.
    • Flexible and highly scalable.
    • Large user base.

Cons

• • • Potentially expensive and difficult to learn.
    • Some users desire expanded customization capabilities.
    • Installation and initial setup can be difficult.

Sisense

Key Insight: The vendor’s data analytics capabilities are among the most sophisticated, and the solution is designed primarily for data scientists, analysts and power business users. The self-service cloud platform connects cloud and on-premises data and includes advanced functionality such as AI and ML. It incorporates low-code and no-code tools and supports numerous types of output, including predictive analytics and visualizations.

Pros

• • • Robust APIs and strong data discovery capabilities.
    • Fast performance and intuitive interface with drag and drop capabilities.
    • Highly customizable.
    • Highly rated customer support.

Cons

• • • Better for power users. Can be difficult to set up, learn and use.
    • Expensive

Tableau

Key Insight: The widely popular data analytics solution, now part of Salesforce, delivers excellent and highly interactive visual dashboards in real time. It connects to a wide range of data sources, handles discovery and data ingestion deftly, and taps AI and ML to deliver an easy-to-use solution that’s ideal for line of business users but sophisticated enough for data scientists. Not surprisingly, there’s a strong focus on CRM, though the solution is suitable for different tasks across a wide range of industries.

Pros

• • • A powerful and highly flexible framework produces outstanding dashboards and visualizations.
    • Extremely intuitive UI.
    • Large user base translates into strong community support.
    • Excellent integration with Salesforce CRM.

Cons

• • • Expensive, particularly for smaller organizations.
    • Some user complaints about customer service and support.
    • Lack of direct integration with AWS S3.

ThoughtSpot

Key Insight: The vendor focuses on an approach it calls “search and AI-driven analytics.” The cloud-based solution delivers an appealing front end for data. It offers powerful tools for discovering, ingesting, connecting and managing data—through APIs and AI/ML. ThoughtSpot embeds search and insight-driven actions into apps using a low-code developer-friendly platform. It supports non-technical users and delivers a single source of truth, with robust security and governance.

Pros

• • • Supports numerous data types and provides numerous and flexible report templates.
    • A powerful Google-like search engine and accompanying AI/ML supports complex natural language queries and questions.
    • Delivers rich and flexible visualizations.
    • Ideal for non-technical users.

Cons

• • • Performance may lag on extremely large data sets.
    • Some users complain about the lack of tutorials and customer support.
    • Some multi-tenant/multi-cloud features and support are lacking. 

TIBCO

Key Insight: Tibco has a solid reputation in the BI and analytics arena. Spotfire delivers real-time data visualization through NLQ powered search, AI-driven recommendations, and direct manipulation. It supports both on-premises and cloud frameworks, with a powerful and highly scalable analytics engine. The result is immersive dashboards, predictive analytics, geolocation analytics, and streaming analytics. Spotfire Mods allows organizations to build custom analytics apps.

Pros

• • • Includes more than 60 built-in connectors and support for almost every data type through customer APIs.
    • Strong AI engine generates recommended visualizations on the fly.
    • Handles extremely large data set well.
    • Delivers tight coding integration through python and R.

Cons

• • • User interface isn’t particularly intuitive and drag-and-drop features are sometimes absent.
    • Customizations can be difficult.
    • User community is smaller than competitors.

Apache Software Foundation

Key Insight: Apache Spark is a software framework designed specifically for Big Data. Built on Hadoop, Spark operates in a similar way, in that it distributes heavy analytics workloads across many computers. Spark operates in-memory rather than saving to disk like Hadoop, so performance is considerably faster and Spark is often used for real-time analytics. It has a rich library of machine learning algorithms called MLlib.

Pros:

• • • Free.
    • Fast, in-memory real-time analytics.
    • ML library.
    • Easy to use.

Cons:

• • • No file management system.
    • Requires Hadoop and other Apache apps.
    • Tigid user interface.

Google

Key Insight: Google Data Studio offers considerable simplicity of use in even the free version. In addition to its ease of use, it offers connectivity with other Google cloud applications and makes it easy to collaborate with both internal and external colleagues. It comes with data templates and a drag=and-drop interface for rapidly building analytics applications and a wide array of data source connections.

Pros:

• • • Enables collaboration.
    • Easy to use interface.
    • Many templates for building apps.
    • Rich reporting.

Con:

• • • Can be buggy.
    • Not as scalable as some competing products.

Looker

Key Insight: Technically, Looker Data Sciences is a Google product and is a part of Google Cloud Platform, but still operates as its own data analytics platform. Looker can load data from any SQL source and has unique data modeling layers to make data available to anyone in real-time. The Looker API provides a secure, “RESTful” interface to create custom applications and data-driven workflows. Looker offers developers the option of adding embedded analytics to their applications, websites, and portals. This lets them put analytics in external-facing applications to share insight with partners and customers.

Pro

• • • Great for cleaning and manipulating data.
    • Google owned but not tied to Google Cloud.
    • Supports a wide range of data sources.

Con

• • • No on-prem support.
    • Can get slow with large data sets.

Data Analytics Tools: Additional Market Leaders

Google Charts

Google offers a free data visualization tool that works with JavaScript to generate presentations and reports.

OpenText

OpenText provides a variety of tools for generating data insights across a variety of vertical industries, including finance, automotive, healthcare and energy.

Birst

The vendor aims to deliver meaningful data insights from the boardroom to the shop floor. It focuses on pre-built industry and role-specific content and metrics. 

Domo

A “BI for All” framework is at the center of Domo’s solutions. It supports strong data integration, BI and analytics, intelligent apps and embedded analytics.

Zoho

The self-service tool delivers robust visualizations via intuitive dashboards. Powerful connectors pull together a multitude of data types and formats.

Xplenty

The platform handles ETL and reverse-ETL functionality within a highly scalable platform. It delivers strong compliance and security features.

KNIME

The free, open-source data analytics solution delivers data integration, modeling and visualization capabilities.

 HubSpot

The CRM data platform focuses on marketing and customer insights. It features an appealing interface and robust analytics tools.

RapidMiner

The advanced analytics platform taps machine learning and AI to generate a wide variety of data insights, including predictive analytics.

Yellowfin

The vendor focuses on appealing dashboards to promote digital storytelling. The solution incorporate powerful natural language capabilities.

Data Analytics Software Tools: Vendor Comparison Chart