We compared the most popular data quality tools on how well they met enterprise requirements for pricing, core features, customer support, data management features, and security. The following are our picks for the seven best data quality tools and platforms available today:

• Ataccama ONE: Best for working with big data
• Data Ladder: Best for managing and enhancing address data
• DQLabs Data Quality Platform: Best for automation
• Informatica Intelligent Data Management Cloud: Best for data integrations
• Precisely Data integrity Suite: Best for responsive customer support
• SAS Data Quality: Best for collaboration
• Talend Data Fabric: Best for understanding your data reliability

Top Data Quality Tool Comparison

All of the data quality tools we explored provided a full range of data cleansing and transformation functions. Differentiators included the level of automation the tools support, the depth of the data management functionality, and pricing.

Jump to:

• Key Features of Data Quality Tools
• How to Choose the Best Data Quality Tools for your Business
• How we Evaluated Data Quality Tools
• Frequently Asked Questions (FAQ)
• Bottom Line

Ataccama ONE

Best for working with Big Data

Overall Rating: 4/5

• Cost: 2.4/5
• Core Features: 4.75/5
• Support: 3.5/5
• Data Management: 4.65/5
• Security: 5/5

Ataccama ONE is an enterprise-grade modular platform that combines a full set of data quality features with data governance and data management capabilities. The tool can be deployed as a cloud-based solution, on-premises, or in a hybrid environment.

Pricing

• Vendor does not provide pricing information
• Starts at $90,000 per year on the Azure Marketplace
• Ataccama’s data quality tool is available as a free download

Features

• AI-enhanced data quality tools
• Anomaly detection and alerts
• Data catalog and governance tools

Data Ladder

Best for managing and enhancing address data

Overall Rating: 2.3/5

• Cost: 1.25/5
• Core Features: 3.9/5
• Support: 3.5/5
• Data Management: n/a
• Security: 3.5/5

Data Ladder is a specialized data quality and matching tool used by a wide range of businesses to profile, clean, and transform customer data. Since it does not include data management and governance functionality, it is less costly than most of the other systems reviewed here. The tool includes a built-in USPS database to ensure consistency in address formats.

Pricing

• Vendor does not provide pricing information
• 30-day free trial available

Features

• Data profile reports
• High-quality data matching
• Address verification
• Match and classify product data

DQLabs Data Quality Platform

Best for automation

Overall Rating: 3.65/5

• Cost: 1.75/5
• Core Features: 4./5
• Support: 3.25/5
• Data Management: 4.65/5
• Security: 4.4/5

DQLabs Data Quality Platform takes an automation-first approach to data quality, harnessing machine learning to perform quality checks on data without the need for extensive coding. Its user-friendly interface is accessible to non-technical users as well as data engineers, and its data observability tools facilitate data governance activities.

Pricing

• Three tiers of service; pricing provided by custom quote
• Free trial available

Features

• Automated incident detection
• Pre-built connectors to data sources
• Auto-discovery of business rules

Informatica Intelligent Data Management Cloud

Best for data integrations

Overall Rating: 4.5/5

• Cost: 2.9/5
• Core Features: 5/5
• Support: 4.75/5
• Data Management: 5/5
• Security: 5/5

A cloud-native solution, Informatica Intelligent Data Management Cloud offers thousands of connectors and integrations to data sources and apps, making it easy and fast to ingest data from nearly any source on the web. The platform’s CLAIRE AI engine facilitates data matching and rule creation, reducing the time it takes to classify data and making it faster to find actionable insights. Its data catalog and Master Data Management tools allow for ongoing monitoring and governance of your business data.

Pricing

• Vendor does not provide pricing
• Subscriptions are based on a package of processing units, which provides access to platform
• Starts at $129,600 per year on the AWS Marketplace
• 30-day free trial available

Features

• Data profiling, cleansing, and standardization tools
• Automated data discovery
• Data sharing across teams via a data marketplace

Precisely Data Integrity Suite

Best for responsive customer support

Overall Rating: 3.3/5

• Cost: 0/5
• Core Features: 5/5
• Support: 4/5
• Data Management: 3.5/5
• Security: 3.9/5

A modular suite of seven interoperable data quality and management tools introduced in 2020, Precisely Data Integrity Suite includes: Data Integration, Data Observability, Data Governance, Data Quality, Geo Addressing, Spatial Analytics, and Data Enrichment. The platform supports all major cloud data warehouses and receives high marks for their responsive customer support.

Pricing

• Vendor does not provide pricing
• Software costs $500,000 per year on AWS Marketplace

Features

• Automated data anomaly and outlier alerts
• Curated datasets for data enrichment
• Plain language search

SAS Data Quality

Best for collaboration

Overall Rating: 4.2/5

• Cost: 1.25/5
• Core Features: 5/5
• Support: 4.5/5
• Data Management: 5/5
• Security: 5/5

SAS Data Quality offers a user-friendly interface for data cleansing, transformation, and monitoring that allows teams across the business to collaborate on the development of a business glossary and data lineage. Visualization and reporting tools make it easy to monitor and share information about data health.

Pricing

• Vendor does not provide pricing
• Two-week free trial is available

Features

• Data profiling, standardization, cleansing, and monitoring
• Business glossary, lineage, and metadata management
• Data visualization and reporting

Talend Data Fabric

Best for understanding your data reliability

Overall Rating: 3.5/5

• Cost: 0.75/5
• Core Features: 4.65/5
• Support: 4/5
• Data Management: 3.5/5
• Security: 4.9/5

Talend Data Fabric is a platform that brings together the company’s Data Integration, Data Quality, and Data Integrity and Governance tools, along with its Application and API Integration. The tool automatically cleans and profiles data as it enters your systems and assigns trust scores to your data to help you understand its reliability at a glance.

Pricing

• Vendor does not provide pricing
• Two-week free trial available

Features

• Data profiling with built-in trust score
• Build reusable data pipelines
• Automated recommendations for addressing data quality issues

Key Features of Data Quality Tools

All of the data quality tools included here provide a full range of data cleansing and transformation functions. But data quality tools have begun to evolve with the rise of artificial intelligence and machine learning, which help automate workflows and alerts. In addition, several tools on the market have moved past cleansing and transformation into data management and governance, allowing businesses to continually monitor their data for any issues, trace problems back to the root cause, and take steps to mitigate any errors. Here’s a look at the key features of data quality tools.

Data Profiling

At the heart of data quality tools, data profiling is the function of analyzing the data to determine structure, content, and relationships. This data parsing helps determine what needs to be done to integrate data into your systems.

Data Validation

Data validation applies a set of business rules and criteria to the data to flag any issues that need to be addressed before adding it to your system.

Data Standardization

Data often comes into your systems in a variety of formats and with different column headers and vocabularies. Data standardization is the process that applies a set of uniform attributes to the data so that it can be combined.

Deduplication

In large data sets, there is usually some overlap and duplicate content. The process of deduplication finds matching data, compares the data using rules set up by the business, and either combines the data or removes any duplicate records.

Data Matching and Enrichment

Data matching is the process of comparing different sets of data to determine if there is any commonality or overlap. Data matches can be exact or software can use “fuzzy logic” to determine if there’s a match. Data enrichment is the process of connecting data in the system to additional data held elsewhere to develop a more comprehensive profile or record.

Automation

As data sets get larger and data collection is more frequent, automation is a cost- and labor-saving feature that can free up staff time to work on other projects. One of the most common areas of automation in data quality tools is automated workflows. Another common area is automated alerts, which are triggered when the system detects certain conditions or thresholds.

Error and Anomaly Detection

In order to keep data as high quality as possible, it is important to be notified of any errors or potential anomalies as it is ingested so that steps can be taken to mitigate any problems before they arise.

Data Pipeline Integration

Standalone data quality tools need to be able to bring in and work with data from a variety of sources and in a variety of formats. Tools that have pre-built connectors to common data sources and formats help simplify the process of data ingestion.

Data Management

As the functionality of data quality tools has expanded, they have taken on a variety of data management functions. This includes the ability to catalog the data in your systems, facilitate data governance, monitor data over time and alert users to any issues, trace the lineage of data in the system, analyze the root cause of any problems, and analyze the impact of any problems.

How to Choose the Best Data Quality Tool for Your Business

There is a great deal of diversity in the data quality tool market. All of the platforms included in this roundup excel at core functions, but some are better suited for different applications. Here are a few tips to help you narrow the choices.

• If you need a standalone data quality tool for your business—Consider your budget, the amount of data you need to analyze and transform, the type of data you collect, how much you can and would like to automate your data quality processes, and whether you also need data management and governance functionality.
• If you are limited in budget—Look at tools that specialize in data quality rather than a larger platform or fabric, as specialized tools tend to be lower cost.
• If you work with vast amounts of data—Look for tools that integrate well with large data warehouses and data lakes so that it is easy to set up a pipeline for big data. Certain systems have better ability to work with and transform specific types of data and others have sophisticated automation features.
• If you need a tool that can catalog your data and help with governance and risk management—Look for platforms that combine several different types of tools rather than trying to find separate tools that need to integrate.

Learn about the 10 best enterprise tools for master data management.

How We Evaluated Data Quality Tools

To rate these data quality tools, we considered five key composite criteria: cost, core features, data management features, support, and security. We then analyzed the products using a weighted scoring rubric—our scoring system ranks products from 0 to 5. The best solutions are chosen from that short list. Percentages represent the weight of the total score for each product.

Cost | 20 percent

In evaluating the top seven data quality tools, 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. If the vendor did not provide pricing for a tool, they did not receive any score in that category.

Core Features | 25 percent

The core features evaluated for each data quality tool included data profiling, data validation, data standardization, deduplication, data matching, data enrichment, automated workflow, automated alerts, rule creation, error detection, and data pipeline integration.

Data Management Features | 25 percent

Since many top data quality tools have expanded to include data management features, we also looked at whether the platform offers a data catalog, data governance, data monitoring, data lineage, root cause analysis, and impact analysis.

Support | 15 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.

Security | 15 percent 

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

Frequently Asked Questions (FAQs)

FAQ #1 What features should businesses look for in data quality tools?

• Data cleansing: ability to profile, validate, standardize, match, deduplicate, and enrich data.
• Ability to create and automate workflows and alerts.
• Connections with a variety of data sources and formats.
• Ability to manage and monitor data over time.
• Responsive customer service.
• Robust security and privacy tools.

FAQ #2 Can data quality tools address data privacy and compliance concerns?

Data privacy and compliance is a critical feature of data quality tools. Look for tools that offer end-to-end encryption of data in transit, that encrypt data at rest, and that make it easy to mask personal identifying information (PII) data. Make sure that the tool creates and allows you to download logs of all user activities so that you can use them for audit purposes. Ask vendors how their platform can help you comply with regional privacy regulations, such as GDPR and CCPA.

FAQ #3 Are there specific industries that can benefit the most from using data quality tools?

Any business can benefit from using data quality tools. If your business collects and wants to use customer data, sales data, operations data, or industry data, it is important to ensure that you have data that is clean, accurate, and complete to help you make business decisions.

Bottom Line: Enterprise Data Quality Tools

Technological advances have allowed businesses to continually collect more data about their customers, prospects, and operations in a diverse array of systems and formats. The challenge for companies is in transforming this data into useful and actionable insights. Evidence-based decision making is only as good as the data that informs the process and small problems with data sources can compound to create faulty insights down the road.

Data quality tools help organizations with a wide range of functions that together help ensure that it is accurate and reliable. If you want to use evidence-based decision-making to grow your business, you need high quality data to inform those decisions—and the right data quality tool to help you clean, validate, and standardize that data is a critical component of your analytics software stack.

Learn about the 7 best data analytics tools for enterprises and how they fit with data quality as part of a larger data management strategy.

• 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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