Top 7 Data Analytics Tools & Software in 2023

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The marketplace for data analytics tools is filled with a diverse range of products designed to pull in a variety of different types of data, help users prepare and analyze that data, and provide insights that can help with strategic decision-making. Increasingly, vendors are integrating artificial intelligence and machine learning (AI/ML) into their data analytics software to automate some of these processes to help businesses discover actionable insights quickly and with less effort. We compared the most popular platforms on the market to see how they matched up on key features and price—here are our top seven picks for data analytics tools in 2023:

• Best for data visualizations and dashboards: Tableau
• Best for Microsoft application users: Microsoft Power BI
• Best for data modeling: Looker
• Best for collaboration: SAS Viya
• Best for SMBs and smaller budgets: Zoho Analytics
• Best for data integration: Sisense
• Best for discovering insights: Qlik

Read on to find more details about each of our picks, how they compare on features and price, and how we chose them.

Comparing the Top Data Analytics Tools

Our review of data analytics software looked at how each platform handles the core functions needed by most enterprise users: data analytics tasks, automation and advanced analytics, security, customer support, and pricing.

Jump to:

• Key Features of Data Analytics Software
• Considerations for Purchasing Data Analytics Tools
• Common Types of Data Analytics Tools
• Methodology
• Bottom Line: Top Data Analytics Tools and Software

Consumers generate a wealth of data about their preferences, experiences, and activities every day that is captured through website visits, online reviews, surveys, customer loyalty programs, and more. Businesses can use this data to better understand their customers and tailor products and services to the ever-changing marketplace. In order to do so, they need to be able to collect, analyze and use this vast amount of data.

The data analytics marketplace is crowded with a wide range of systems promising an even wider range of features. To help narrow your choices, here are our top seven data analytics tools geared toward enterprise users and requirements.

Tableau

Best for data visualizations and dashboards

Tableau has continued to refine and diversify its wide variety of visualizations while releasing new connectors to additional data sources since its acquisition by Salesforce in 2019. The ease of adding new data sets to the platform, along with automated insights and user-friendly chart and dashboard creation tools makes it a top contender in the business intelligence (BI) marketplace.

Tableau comes in two versions: Tableau Cloud, a cloud-based SaaS (Software as a Service) tool; and Tableau Server, which allows companies to host the software within their own IT structure.

Key Features

• Drag-and-drop chart, data visualization and dashboard creation
• More than 30 connectors to a variety of data sources
• Marketplace of pre-built dashboard extensions
• Self-service data preparation
• Augmented analytics powered by AI/ML, including automated modeling and natural language queries
• Add-ons for data management, embedded analytics and more

Pros

• Easy to create visualizations and dashboards
• Pre-built connections to a wide variety of data sources, including big data platforms

Cons

• No automatic or scheduled report data refreshes
• Breadth of features mean it can be overwhelming for new users

Pricing

• Pricing is per user, per month, based on certain types of users. At least one Creator license ($75 per user per month) is required for every deployment.
• Explorer licenses ($42 per user per month) allow for self-service analytics and Viewer licenses ($15 per user per month) allow users to interact with dashboards and visualizations
• 14-day free trial available

Microsoft Power BI

Best for Microsoft application users

The user experience for Microsoft Power BI will feel somewhat familiar to many business users on the Microsoft software ecosystem. Sharing data and reports between MIcrosoft Power BI and Office 365 applications is seamless, and it is also easy to leverage the power of Azure to scale data analytics operations as business grows.

Key Features

• Customized dashboards and interactive visualizations
• Access to hundreds of data connectors
• Self-service data preparation and modeling tools for Premium package subscribers
• AI-powered augmented analytics for Premium package subscribers
• Natural-language search

Pros

• Deep integration with other Microsoft products
• Familiar, user-friendly navigation and controls
• Hundreds of pre-built visualizations available on Microsoft AppSource
• Low-cost solution for smaller businesses

Cons

• Add-ons required for data sharing and working with larger datasets
• Data refresh limitations with lower-priced packages

Pricing

• Pricing is per user, per month, depending on package selected
• Power BI Pro is $10 per user, per month
• Power BI Premium is $20 per user, per month
• Basic Power BI access is free

Looker

Best for data modeling

Google Looker is a browser-based data analytics tool built on the Google Cloud infrastructure. A full-featured platform, Looker’s proprietary LookML modeling language provides a flexible approach to building complex data models. LookML allows users to create reusable code blocks and data models, streamlining the modeling process. Its integration with Google Looker Studio allows users to create and share a wide variety of dashboards and visualizations.

Key Features

• LookML modeling language, which abstracts SQL queries
• Dashboards and visualizations via an integration with Looker Studio
• Predictive modeling and machine learning

Pros

• Can build complex data models
• Integrations with numerous data sources
• Advanced data governance and security features

Cons

• High price tag
• Learning curve for users who are not experienced with LookML or SQL
• Reported slowness when working with large data sets

Pricing

• Three pricing tiers corresponding to the number of API calls per month
• Standard edition starts at $5,000 per month and includes one production instance, 10 Standard Users, 2 Developer Users and up to 1,000 API calls per month
• 30-day free trial available

SAS Viya

Best for collaboration

SAS Viya is a cloud-native data analytics platform that leverages SAS’s industry-leading analytic expertise and the power and security of Azure. The platform is highly scalable and offers numerous integration with other tools and systems to extend its functionality.

Key Features

• Data preparation and cataloging
• Model deployment and monitoring
• Conversational AI

Pros

• Cloud-native architecture allows access to data from anywhere
• Easy to share and collaborate on dashboards and visualizations
• Can handle large volumes of data

Cons

• Cost
• Limited support options and training

Pricing

• The vendor does not make specific pricing available—contact sales directly
• Customer reviews mention that the software is expensive

Zoho Analytics

Best for small and budget-conscious businesses

Zoho Analytics is an easy-to-use self-help analytics platform with pricing that won’t break the bank for SMBs and enterprises with lower data volumes. It provides a modular approach to data analytics, so that it can grow as data programs mature. The tool has a range of data connectors and integration options and can connect with both cloud-based and on-premises data sources. Some of the more technical data analytics features require a Premium subscription or add-on software.

Key Features

• Integrations with more than 250 data sources
• Data preparation and management via the Zoho DataPrep app (available with Premium subscriptions)
• Collaboration tools for reports
• Ask Zia natural language queries

Pros

• User-friendly dashboard and report creation
• Scalable and modular solution that can grow with your business

Cons

• Limited data visualization options
• Online reviews say that training videos and resources could be improved

Pricing

• Standard plan is $48 per month for five users, with additional users billed at $6.40 per user per month
• Premium plan is $115 per month and includes 15 users
• Enterprise plan is $455 per month and includes 50 users
• 15-day free trial available

Sisense

Best for data integration

Sisense is a BI tool that brings together disparate sources of data to create useful interactive dashboards and visualizations that can be drilled down into to expose actionable insights. These reports can then be embedded in a wide range of applications. Sisense is built as a cloud-native product but can also be run on-premises or as a hybrid solution.

Key Features

• Data blending tools to merge data from disparate sources
• Interactive dashboards and visualizations
• AI-driven insights
• Embeddable in a wide variety of applications

Pros

• Connects to a wide variety of data sources
• Drill-down charts and dashboards
• Easy to embed and share visualizations

Cons

• Reports of performance issues when working with extremely large data sets
• Significant learning curve for new users

Pricing

• Pricing is not available, as each deployment is a custom quote based on a company’s requirements
• Online sources note that it costs around $25,000 per year
• Free trial available

Qlik

Best for discovering insights

Qlik is a data analytics and BI platform that leverages an AI-powered associative engine when importing data from different sources to speed up the process of combining the data and surface new actionable insights.

Key Features

• ML-powered augmented analytics with intelligent alerts
• Interactive charts and visualizations
• Mobile-first design with iOS and Android apps

Pros

• Associative engine streamlines data processing
• Automated workflows

Cons

• Can get expensive quickly when you need to scale up the number of users
• Mobile-optimized design means some charts are lower resolution

Pricing

• Standard starts at $20 per user, per month with a minimum of 10 users
• Premium starts at $2,700 per month
• Free trial available for the Standard plan

Key Features of Data Analytics Software

The market of data analytics platforms is broad, and there are tools to fit just about every business need. While they each have their own strengths and weaknesses, there are a number of key features that each needs to meet.

Data Management

Data management refers to the process of gathering, storing, securing and using data within an analytics platform. It is helpful for the software to be able to bring in and use different types of data from a variety of sources.

Data Preparation

Data preparation is the process of cleaning and reformatting data before it is used for analysis. The quality of your data directly impacts the quality of your analysis.

Data Modeling

In data modeling, a platform helps define the different types of data you collect and spells out the relationships between these disparate data sets.

Data Governance

Data governance helps insure the quality and security of your data through a set of defined standards and policies. Data analytics platforms help data governance efforts by automating and monitoring these standards and policies.

Dashboards and Visualizations

Dashboards and visualizations are the most useful tools for communicating the insights uncovered in your data with non-technical business users. Dashboards help businesses monitor their key performance indicators and other important metrics, while visualizations can help convey important insights at a glance.

Scalability

As a business grows, so too does the amount of data it collects and needs to analyze. That’s why scalable data analytics platforms are important, so they can grow with your business as your data grows.

Security

Much of the data businesses collect is sensitive or proprietary, and therefore data analytics platforms need to have robust security controls to prevent theft or misuse of their data. This includes features such as granular access controls, end-to-end encryption and logging and audit tools.

Considerations for Purchasing Data Analytics Tools

When selecting a data analytics tool, the primary consideration is how you will use it. Do you need it to help you optimize your marketing or strategic decision making? Do you need it for research or mathematical analysis? Do you need to track sensitive information, like healthcare data? Certain data analytics tools specialize in specific industries or focus on certain types of data analysis. Once you’ve identified your need, there are other considerations to help you narrow down your choices.

Cost

Since many data analytics tools are priced based on the number of users or the amount of data in your platform, it helps to develop realistic requirements of just who needs to use the system, what they need to do and how much data you will be analyzing.

Ease of Use

The usability of platforms varies widely. Some systems require deep expertise in data manipulation or knowledge of certain programming languages to get the most out of them, while others are designed as low- or no-code solutions.

Natural language processing in some systems allows users to explore data through conversational queries. It is helpful to understand the types of users who need access to the data in your platform and their level of technical expertise.

Data Handling

Platforms also differ in their ability to handle large data sets or to ingest data from a variety of sources. If your data is spread across multiple systems, look for a platform with a wide variety of data connectors. If your business deals with big data, be sure to read reviews of the software you are considering to see if there are any performance issues when dealing with large data sets.

Data Preparation

Data analytics software varies in its ability to tackle data preparation tasks. Some software requires add-ons or integrations to clean and prepare data, while others are able to do these tasks natively. As your data grows more complex, you will need to look for a platform that can handle a variety of data models and easily manage complex models.

Data Governance

Similarly, as the complexity and volume of data grows, look for a platform that includes data governance functions that let you implement policies and practices and monitor your data to ensure compliance.

Customer Support and Education

Depending on the expertise of your staff, you should also look into each platform’s support and training availability. Some platforms have steep learning curves for all but the most technical staff. Training options are particularly helpful if you want the platform to be used effectively by a wide range of stakeholders.

Security

If you collect sensitive data or data that is governed by privacy laws, security is of the utmost importance to your company. That is why you should look at the security features of each platform, such as encryption, user access controls, what types of system activities are logged and data privacy features.

Desktop vs. Cloud

Data analytics platforms often can be hosted in a variety of environments, from the cloud to on-premises to hybrid, but many of the tools have differences in how they function on a desktop versus in a cloud environment.

In addition, while cloud-hosted services do offer enterprise-level security, you may still need to have an on-premises-hosted solution, depending on the data you collect or the way your IT is governed. You also need to think about your maintenance and support resources when making a hosting decision, as well as your backup and recovery capabilities.

What are Common Types of Data Analytics Tools?

There are numerous types of data analytics tools on the market and the boundaries between them can be somewhat blurry, as many of them can be used for a variety of industries and tasks. Examples of how some companies are using these tools to transform their operations and services can easily be found online.

Business Intelligence 

Many of the data analytics tools on the market are business intelligence tools, focused on bringing together your data for the purpose of better understanding your market and for making strategic business decisions. Business intelligence tools can also be used in education, healthcare and IT settings to optimize service delivery to students, patients and clients. Most business intelligence platforms have robust dashboards and data visualization capabilities so that the data can be accessed and understood by a wide variety of stakeholders.

Data Science

Similar to business intelligence platforms, data science platforms are often used by companies looking to transform their data. Many of the new data science platforms use machine learning to develop models for activities like predictive analytics.

Statistical Analysis

Researchers, economists, scientists, students and engineers need workhorse platforms to do deep statistical analysis of data. These programs include popular statistical analysis models, like regressions and Bayesian procedures.

Collaboration

While other types of data analytics platforms facilitate collaboration between users, there are some products on the market specifically designed for data scientists to collaborate with each other. These platforms allow users to share notebooks, code, and data to help with workflows.

Methodology: How We Chose These Picks

Datamation does not rank recommended solutions based on any vendor partnerships, only on features and capabilities. We may also analyze comprehensive user reviews and the vendors’ own data on their products to determine whether we think that product is a good fit for our audience.

We considered six key composite criteria when evaluating data analytics solutions: pricing, core features, support, dashboards and visualizations, augmented analytics, and security.  We then analyzed the products using a weighted scoring rubric. Our scoring system ranks products from 0 to 5, and the best solutions are chosen from that short list. Percentages represent the weight of the total score for each product.

Core Features | 20 Percent

The core features of a data analytics platform include data management, data processing, data preparation, data modeling, data governance, collaboration and scalability.

Pricing | 20 Percent

In evaluating the top seven data analytics solutions, pricing considerations include the advertised cost, the price of add-ons and options, available pricing tiers, any upgrades or discounts, and whether a free tier and/or trial version is available.

Support | 10 Percent

The support evaluation included the availability of training and onboarding, phone and online support, a knowledge base, a user community and videos and webinars.

Dashboards and Visualizations | 20 Percent

We looked at how the systems handled dashboard creation and data visualizations, including whether they could be creating using drag-and-drop functionality, as well as the ability to interact with the data via natural language processing.

Augmented Analytics | 10 Percent

The number of solutions using artificial intelligence and machine learning to optimize their analysis is growing, so we looked at how systems used these tools, whether they could handle predictive analytics and whether they could surface automated insights from the data.

Security | 20 Percent

We looked at data access controls, logging and auditing, encryption, how the systems handle data masking, the results of security audits (SOC 1 Type 2, SOC 2 Type 2, ISO 27001), and whether they facilitate GDPR and CCPA compliance.

Bottom Line: Top Data Analytics Tools and Software

The universe of data analytics platforms is vast and diverse, which means that there are numerous tools available that fit your company’s individual needs. The seven platforms here all provide strong analytics tools for working with a wide range of data, and all excel in different ways.

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