The Top 10 Data Quality Tools
Discover the Top Data Quality Tools designed to ensure accuracy and reliability in datasets across various business applications. Explore features such as data profiling, cleansing, and monitoring.
Written by Caitlin Harris
Technical Review by Laura Iannini
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1.
Ataccama Data Quality & Governance
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2.
Collibra Data Quality & Observability
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3.
Experian Aperture Data Studio
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4.
IBM InfoSphere Information Server for Data Quality
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5.
Informatica Cloud Data Quality
Data quality tools help organizations to ensure that their data is accurate, reliable, and consistent. To achieve this, they identify and resolve errors and inconsistencies in data sets, profiling, cleaning, standardizing, and enriching them ready for analysis. But the work doesn’t stop there; the best data quality tools continuously monitor data quality longer-term and provide real-time reports into the quality of a data set. This allows analysts to trust that their data is always up-to-date and accurate, even after its initial cleaning.
Data quality is becoming a focal point for many businesses as they realize that strategic decisions need to be founded on high-quality data, i.e., data that’s accurate, complete, and relevant. Improving data quality manually is a complex and time-consuming task—but that’s why data quality tools were created. They automatically implement a broad range of functions to monitor and improve data quality, enabling analysts to spend less time cleaning their data and more time analyzing it. And by ensuring that all analysis is based on high-quality data, they increase the reliability of any decisions made off the back of that analysis.
While standalone data quality tools do exist, they’re usually part of comprehensive data management platforms that may also include functionalities like data integration, master data management, data cataloging, and metadata management.
In this article, we’ll explore the top data quality tools designed to help you improve the accuracy and reliability of your datasets. We’ll highlight the key use cases and features of each solution, including data cleansing, profiling, monitoring, and governance.
Ataccama Data Quality & Governance
Ataccama Data Quality & Governance improves data accuracy, reduces inconsistencies, and protects sensitive data, ensuring reliable analytics and reporting.
Who it’s for: Ataccama is ideal for organizations looking to improve data quality and governance with advanced AI capabilities and real-time monitoring.
Benefits: Ataccama’s platform offers AI-assisted data preparation and continuous data quality assurance to ensure timely and accurate information.
- AI-assisted data preparation and validation automate data quality assurance and speed up operations.
- Continuously monitors, profiles, and detects anomalies, with automated real-time alerts.
- Integrated data governance for teams to manage metadata, lineage, and stewardship.
- Flexible platform, handles billions of records and millions of API calls from front-end applications, without performance degradation.
The bottom line: Ataccama provides a comprehensive range of insights into your data, with AI-enhanced analytics to improve outcomes.
- Ataccama is headquartered in Prague, with a diversified customer base across various industries.
Ataccama Data Quality & Governance
Collibra Data Quality & Observability
Collibra Data Quality & Observability is a data monitoring tool that detects and quickly address any anomalies in data quality and pipeline reliability.
Who it’s for: Ideal for organizations seeking an automated and intelligent solution to manage and maintain high data quality standards across diverse data ecosystems.
Benefits: This solution automates data quality control using AI and machine learning. It also allows for custom data quality rules via an in-built SQL editor.
- Automatically identify sensitive data, enforce quality, and take actions on faulty data records.
- Build custom data quality rules, allowing users to create personalized rules with an in-built SQL editor.
- AI-powered Adaptive Rules saves time and reduces manual coding efforts.
- Includes repository of auto-validation rules specific to each industry.
The bottom line: Collibra can be implemented on any cloud network and can connect to over 40 varieties of databases and file systems. It allows for scanning of data where it resides, offering both pushdown and pull-up processing.
- Collibra is headquartered in New York City, serving over 500 global customers.
Collibra Data Quality & Observability
Experian Aperture Data Studio
Experian Aperture Data Studio is a self-service data quality management platform that provides consistent, accurate, and comprehensive consumer data insights. It supports deployment on physical hardware or virtual machines, both on-premises and in the cloud.
Who it’s for: Best suited for organizations looking to streamline data quality management and gain in-depth consumer insights while maintaining flexibility in deployment.
Benefits: Aperture Data Studio’s drag-and-drop workflow and user interface make data validation, cleansing, and deduplication simple and efficient.
- Effective and consistent data transformation through extendable and repeatable workflows across the enterprise.
- Drag-and-drop workflow features allow complex data processes to be built easily, aiding both technical and non-technical users.
- Data can be ingested across diverse sources, including Hadoop clusters, allowing isolated datasets to be integrated for a unified view.
The bottom line: Experian’s intuitive interface and wide range of automations make the platform easy to manage, enabling even users with a non-technical background to improve the quality of their data quickly and easily.
- Experian is a global information services company headquartered in Dublin, Ireland, serving thousands of clients around the world.
Experian Aperture Data Studio
IBM InfoSphere Information Server for Data Quality
IBM InfoSphere Information Server for Data Quality is a comprehensive solution supports data governance by continuously analyzing, cleaning, and standardizing information.
Who it’s for: Organizations aiming to improve data quality and governance as part of a broader data management strategy.
Benefits: IBM automates data investigation and supports flexible deployment options for quick and easy implementation, whether on-premises or in the cloud.
- Continuous data quality monitoring reduces the spread of incorrect or inconsistent data.
- Helps teams build a data governance program with detailed metrics in line with business objectives.
- Offers extensive data validation features and identifies the location of Personally Identifiable Information (PII), helping with classification efforts.
The bottom line: IBM InfoSphere Information Server provides robust data quality management capabilities, with in-built tools to help preserve the privacy of a dataset. We recommend it as a strong tool for organizations looking to improve their data quality as part of a wider data management initiative.
- IBM is a market leader in data quality solutions. Its cloud analytics platform was launched in 2011.
IBM InfoSphere Information Server for Data Quality
Informatica Cloud Data Quality
Informatica Cloud Data Quality helps businesses identify, resolve, and monitor data quality issues across their applications.
Who it’s for: It is ideal for teams requiring a collaborative data quality solution that integrates seamlessly across different platforms to bring together data from data warehouses, data lakes, and SaaS applications.
Benefits: The self-service data quality feature for business users and the intelligent recommendations from the CLAIRE engine are key benefits of implementing this solution.
- Identifies and resolves data issues without requiring additional IT code or development.
- Supports data quality transformations and universal connections for diverse data types and use cases.
- CLAIRE AI engine delivers intelligent recommendations for data quality rules.
- Unified cloud-based for data quality management across departments and applications.
The bottom line: Informatica Cloud Data Quality is a unified data quality tool that can be used across departments, applications, and even deployment models. It’s fully cloud-based and economically priced.
- Informatica Corporation is headquartered in Redwood City, California and supports 1,000s of global customers.
Informatica Cloud Data Quality
Melissa Unison
Melissa Unison allows data stewards to clean and monitor data without requiring programming skills.
Who it’s for: Unison is best suited for teams looking to improve data quality. It is particularly suited for industries with extensive and robust data security and privacy needs.
Best features: Melissa Unison stands out for its comprehensive data cleansing and user-friendly interface. It is highly scalable and features robust security capabilities, making it ideal for handling large datasets.
- Unison uses machine learning and tailored rules to clean and standardize data, ensuring optimal accuracy and usability.
- The platform verifies, enriches, matches, and consolidates data to create a comprehensive customer overview.
- Unison converts addresses into latitude and longitude coordinates, thereby enhancing mapping and analytics.
- It provides user-level access restrictions, on-premises data management security, and detailed logging for audit purposes, ensuring that you remain compliant with relevant legislation.
The bottom line: Unison is a highly scalable data quality platform; it employs container technology for enhanced performance and is capable of handling large datasets quickly and accurately. It also offers in-built security features that provide user-level access restrictions, offer on-premises data management security, and include detailed logging of results for audit trails.
- Melissa is headquartered in the U.S. and serves a global customer base, offering solutions across various industries.
Melissa Unison
Precisely Data Integrity Suite
Precisely Data Integrity Suite is an integrated platform that improves data accuracy and context across the data management lifecycle.
Who it’s for: Organizations seeking to enhance the accuracy and manageability of their data assets with a leading data integrity tool.
Benefits: Precisely’s Data Integrity Suite offers a user-friendly interface that visualizes data changes in real-time
- Machine learning-assisted matching and linking system that minimizes data duplication.
- Automated data quality suggestions that provide users with recommended actions to take to improve the quality of their data.
- The integrated data catalog organizes technical information into an easily recognizable format, improving data asset management.
The bottom line: The Precisely Data Integrity Suite simplifies data management across different systems or datasets. It ensures consistent and accurate contact information like names, emails, phone numbers, postal addresses and helps build trust in your data.
- Precisely is a market leader, headquartered in Burlington, Massachusetts. It’s used by 99 Fortune 100 companies.
Precisely Data Integrity Suite
SAP Master Data Governance
SAP Master Data Governance improves the quality of business-critical data, promotes efficiency, and supports better decision-making processes.
Who it’s for: Teams looking for a robust data governance solution that ensures data consistency across various systems and supports flexible deployment models.
Benefits: The solution supports both on-premise and cloud deployments, providing flexibility for organizations transitioning to cloud environments.
- It integrates closely with other SAP solutions and third-party services, allowing for the reuse of data models, business logic, and validation frameworks.
- The platform enables collaborative workflow routing, notifications, and the maintenance of validated data attributes.
- Data quality and analytics features help to define, validate, and monitor business rules to analyze performance.
The bottom line: SAP Master Data Governance provides a unified, simplified view of your business to help you work more effectively with a centralized master data management layer.
- SAP Master Data Governance is a popular tool, reportedly used by over 4,000 customers globally.
SAP Master Data Governance
SAS Viya
SAS Viya is a cloud-native, cloud-agnostic solution for data preparation and data quality management.
Who it’s for: Organizations that need a robust data quality tool that is accessible to both technical and non-technical users.
Benefits: SAS Viya simplifies data preparation with a visual user interface, reducing dependency on IT support.
- Drag-and-drop transformations allow for hassle-free data preparation for analytics and eliminate the need for coding or reliance on IT support.
- Supports low-code data processing with multi-language code support and an easy-to-use visual flow builder.
- Advanced data profiling and quality assurance to identify and fix issues throughout the data pipeline.
- Teams can easily collaborate and reuse data preparation tasks.
The bottom line: SAS Viya ensures consistency and quality throughout the data life cycle. We recommend SAS Viya as a robust, yet user-friendly, data quality tool that’s suitable even for non-technical users.
- SAS is a global analytics leader headquartered in Cary, North Carolina. 90% of Fortune 100 companies or affiliates have used SAS.
SAS Viya
Talend Data Quality
Talend Data Quality is a module within Talend’s Data Fabric platform that ensures data integrity and governance, with capabilities for real-time data profiling, cleansing, and masking.
Who it’s for: Best suited for organizations seeking a comprehensive, real-time, and user-friendly data quality management solution.
Benefits: Talend’s user-friendly interface and built-in machine learning components help address data quality issues.
- Automatically profiles and cleans data in real-time, ensuring high data quality and trust.
- Integrated machine learning provides actionable recommendations for data quality improvements.
- In-built features for masking sensitive data help meet data privacy and protection regulations.
- Talend’sTrust Score offers an immediate, understandable, and actionable assessment of data confidence.
The bottom line: Talend offers a comprehensive platform that helps business manage data quality, integrity, and governance
- Talend was acquired by Qlik in May 2023.
Talend Data Quality
FAQs
Everything You Need To Know About Data Quality Tools (FAQs)
If your business wants to make data-driven decisions, you need to base those decisions on high-quality data. Data quality refers to the completeness, accuracy, and relevance of data. It’s becoming increasingly important for organizations to improve and maintain the quality of their data. However, the huge volumes of data that we use today—alongside a plethora of diverse data types—makes this easier said than done. That is, unless you implement a data quality tool.
Data quality tools enable organizations to identify, understand, and resolve flaws and inconsistencies in their data. To do this, they implement and automate a range of functions, such as data profiling, parsing, standardization and cleaning, enrichment, and monitoring. This means that data analyst teams can spend less time combing through their data sets for inconsistencies, and more time analyzing the data. It also means that any analysis they undertake will be based on accurate, consistent, complete, and well-governed data—which, in turn, increases the reliability of any conclusions drawn from the analysis.
Data quality tools use a combination of profiling, cleaning, standardization, validation, monitoring, and reporting features to ensure that data is accurate, consistent, and reliable throughout its lifecycle. When implemented, data quality tools are integrated with your data pipelines and workflows so that they can automatically carry out quality checks to prevent poor-quality data from entering the system as it’s collected.
After this initial check, once the data is collected, the data quality tool will carry out a series of more advanced functions to help improve the quality of the data set. Usually, data quality tools start by profiling the data. This helps the tool to understand the structure and characteristics of the data and identify any patterns or relationships within it. The data quality tool then standardizes the data, converting it into a common format, and cleans it. This involves correcting errors and inconsistencies, such as misspellings and duplicates.
Once the data has been cleaned, some data quality tools enrich or augment it with data from other sources. This can add more context to the data, giving it more quality in terms of depth and usefulness.
The final check that the data quality tool does is to validate the data against a set of pre-defined rules. This ensures that the data is accurate (i.e., relevant for its intended use), adherent to business rules and processes, and compliant with data protection and privacy standards.
After all checks have been carried out, data quality tools continue to monitor the quality of the data over time. They provide reports into the state of the data, which highlight areas for improvement so that data analyst teams can quickly address issues, keeping the data accurate and up-to-date over longer periods of time.
Choosing the right data quality tool is crucial for ensuring the accuracy, reliability, and consistency of your data. To help you find the right solution for your business, here are the key features you should look for in a data quality tool:
- Data Profiling: Your chosen solution should analyze and profile data to understand its structure, completeness, and quality, as well as identifying any anomalies, duplicates, and missing values.
- Data Cleaning: Data quality tools should offer a broad range of cleaning functions that correct the errors found during profiling. These should include standardizing the data’s format, units, and values to maintain consistency; merging or removing duplicate records; fuzzy matching to handle variations in the data and improve accuracy; removing outliers; and filling in missing fields.
- Data Enrichment: Some data quality tools offer the ability to enhance data by adding missing information from external sources. To do this, they need to be able to integrate with third-party enrichment services.
- Data Validation: In order to ensure compliance with your own internal rules and federal- or industry-mandated data protection and privacy regulations, your solution should enable you to define and enforce custom validation rules.
- Data Governance and Security: The solution should offer role-based access controls and encryption to secure sensitive data.
- Data Monitoring and Reporting: The work shouldn’t end after the above checks are carried out; the best data quality tools continuously monitor data quality to identify issues as they arise and ensure the data remains accurate and up-to-date over longer periods of time. They also offer customizable dashboards for monitoring and analyzing data quality trends, and comprehensive reporting features to track and visualize data quality metrics.
- Integration: Your chosen solution must be compatible with the workflow and business process management tools you’re already using, as well as all types of data source that you’re going to be pulling data from (e.g., data storage systems, databases, and data integration tools).
- User-Friendly Interface: There’s no use in implementing a data quality tool that is too complicated or inaccessible for your team to use. Look for an intuitive and user-friendly interface that support for both technical and non-technical users.
Written By
Caitlin Harris is Deputy Head of Content at Expert Insights. Caitlin is an experienced writer and journalist, with years of experience producing award-winning technical training materials and journalistic content. Caitlin holds a First Class BA in English Literature and German, and provides our content team with strategic editorial guidance as well as carrying out detailed research to create articles that are accurate, engaging and relevant. Caitlin co-hosts the Expert Insights Podcast, where she interviews world-leading B2B tech experts.
Technical Review
Laura Iannini is an Information Security Engineer. She holds a Bachelor’s degree in Cybersecurity from the University of West Florida. Laura has experience with a variety of cybersecurity platforms and leads technical reviews of leading solutions. She conducts thorough product tests to ensure that Expert Insights’ reviews are definitive and insightful.