Best Data Quality Tools & Software for 2023

Data quality mangement is a critical issue in today’s data centers. The complexity of the Cloud continues to grow, leading to an increasing need for data quality tools that analyze, manage, and scrub data from numerous sources, including databases, email, social media, logs, and the Internet of Things (IoT).

TABLE OF CONTENTS
What Are Data Quality Tools?
How Are Data Quality Tools Used?
How To Select The Best Data Quality Tool
Top Data Quality Tools & Software
Comparison Chart of Data Quality Software

What Are Data Quality Tools?

Data quality tools clean data by removing formatting errors, typos, and redundancies, while ensuring that organizations apply rules, automate processes, and have logs that provide details about processes. Used effectively, these data quality tools can remove inconsistencies that drive up enterprise expenses and annoy customers and business partners. They also drive productivity gains and increase revenues.

How Are Data Quality Tools Used?

Data quality software helps data managers address four crucial areas of data management: data cleansing, data integration, master data management, and metadata management. These tools go beyond basic human analysis and typically identify errors and anomalies through the use of algorithms and lookup tables.

Over the years, these tools have become more sophisticated and automated, making them easier to use and more advanced. These simplified data quality tools now tackle numerous tasks, including validating contact information and mailing addresses, data mapping, data consolidation associated with extract, transform and load (ETL) tools, data validation reconciliation, sample testing, data analytics, and all forms of big data handling.

Also see: Top 15 Data Warehouse Tools

How To Select The Best Data Quality Tool

Identifying the right data quality management software is important for data managers who want to assess and improve the overall useability of their databases. Finding a superior data quality tool hinges on many key factors, including how and where an organization stores and uses data, how data flows across networks, and what type of data a team is attempting to tackle. 

Although basic data quality tools are available for free through open source frameworks, many of today’s solutions offer sophisticated capabilities that work with multiple platforms and database formats. It is important to understand what a particular data quality tool can do for your enterprise — and whether you may need multiple tools to address more complex scenarios.

Consider these three factors when choosing a data quality management platform to address your business needs:

1. Identify your data challenges.

Incorrect data, duplicate data, missing data, and other data integrity issues can significantly impact — and undermine — the success of a business initiative. A haphazard or scattershot approach to maintaining data integrity may result in wasted time and resources. It can also lead to subpar performance and frustrated employees and customers. To avoid frustrating internal and external responses to data challenges, it’s important to start by conducting an analysis of existing data sources, current tools in use, and problems and issues that occur. This offensive approach delivers insight into gaps and possible fixes.

2. Understand what data quality tools can and cannot do.

There’s no fix for completely broken, incomplete, or missing data. Data cleansing tools cannot perform magic on dated legacy systems or sloppy spreadsheets. If your organization identifies gaps and shortcomings in its data collection and management methods, it may be necessary to go back to the drawing board and examine the entire data framework. This includes the data management tools you’re currently using, how your organization manages and stores data, and what workflows and processes could be changed and improved.

3. Understand the strengths and weaknesses of various data cleansing tools.

It’s obvious that not all data quality management tools are created equal. Data cleansing tools offer different strengths and weaknesses: some are designed to enhance specific applications such as Salesforce or SAP, others excel at spotting errors in physical mailing addresses or email, and still others tackle IoT data or pull together disparate data types and formats, so you need to decide which features are most important to your organization. In your decision making process, it’s also important to understand how a data cleansing tool works and what level of automation it offers, as well as specific features that you will need to accomplish key tasks. Finally, it’s crucial to consider factors such as data controls/security and licensing costs.

Top Data Quality Tools & Software

Jump to:

• Cloudingo
• Data Ladder
• IBM InfoSphere QualityStage
• Informatica Master Data Management
• OpenRefine
• SAS Data Management
• Precisely Trillium
• Talend Data Quality
• TIBCO Clarity
• Validity DemandTools

Cloudingo

Value proposition for potential buyers: Cloudingo is a prominent data integrity and data cleansing tool designed for Salesforce. It tackles everything from deduplication and data migration, to spotting human errors and data inconsistencies. The platform handles data imports, delivers a high level of flexibility and control, and includes strong security protections.

Key values/differentiators:

• The application uses a drag-and-drop graphical interface to eliminate coding and spreadsheets. It includes templates with filters that allow for customization, and it offers built in analytics. APIs support both representational state transfer (REST) and simple object access protocol (SOAP). This makes it possible to run the application from the cloud or from internal systems.

• The data cleansing management tool handles all major requirements including merging duplicate records and converting leads to contacts, deduplicating import files, deleting stale records, automating tasks on a schedule, and providing detailed reporting functions about change tracking. It offers near real-time synchronization of data.

• The application includes strong security controls that include permission-based logins and simultaneous logins. Cloudingo supports unique and separate user accounts and tools for auditing who has made changes.

Data Ladder

Value proposition for potential buyers: The vendor has established itself as a leader in data cleansing through a comprehensive set of tools that clean, match, dedupe, standardize and prepare data. Data Ladder is designed to integrate, link, and prepare data from nearly any source. It uses a visual interface and taps a variety of algorithms to identify phonetic, fuzzy, abbreviated, and domain-specific issues.

Key values/differentiators:

• The company’s DataMatch Enterprise solution aims to deliver an accuracy rate of 96 percent for between 40K and 8M record samples, based on an independent analysis. It uses multi-threaded, in-memory processing to boost speed and accuracy, and it supports semantic matching for unstructured data.

• Data Ladder supports integrations with a vast array of databases, file formats, big data lakes, enterprise applications, and social media. It provides templates and connectors for managing, combining, and cleansing data sources. This includes Microsoft Dynamics, Sage, Excel, Google Apps, Office 365, SAP, Azure Cosmos database, Amazon Athena, Salesforce, and dozens of others.

• The data standardization features draw on more than 300,000 pre-built rules, while also allowing customizations. The system uses proprietary built-in pattern recognition, but it also lets organizations build their own RegEx-based patterns visually.

IBM InfoSphere QualityStage

Value proposition for potential buyers: IBM’s data quality application, available on-premise or in the cloud, offers a broad yet comprehensive approach to data cleansing and data management. The focus is on establishing consistent and accurate views of customers, vendors, locations, and products. InfoSphere QualityStage is designed for big data, business intelligence, data warehousing, application migration, and master data management.

Key values/differentiators:

• IBM offers a number of key features designed to produce high quality data. A deep data profiling tool delivers analysis to aid in understanding content, quality and structure of tables, files, and other formats. Machine learning can auto-tag data and identify potential issues.

• The platform offers more than 200 built-in data quality rules that control the ingestion of bad data. The tool can route problems to the right person so that the underlying data problem can be addressed.

• A data classification feature identifies personally identifiable information (PII) that includes taxpayer IDs, credit cards, phone numbers, and other data. This feature helps eliminate duplicate records or orphan data that can wind up in the wrong hands.

• The platform supports strong governance and rule-based data handling. It includes strong security features.

Informatica Quality Data and Master Data Management

Value proposition for potential buyers: Informatica has adopted a framework that handles a wide array of tasks associated with data quality and Master Data Management (MDM). This includes role-based capabilities, exception management, artificial intelligence insights into issues, pre-built rules and accelerators, and a comprehensive set of data quality transformation tools.

Key values/differentiators:

• Informatica’s Data Quality solution is adept at handling data standardization, validation, enrichment, deduplication, and consolidation. The vendor offers versions designed for cloud data residing in Microsoft Azure and AWS.

• The vendor also offers a Master Data Management (MDM) application that addresses data integrity through matching and modeling, metadata and governance, and cleansing and enriching. Among other things, Informatica MDM automates data profiling, discovery, cleansing, standardizing, enriching, matching, and merging within a single central repository.

• The MDM platform supports nearly all types of structured and unstructured data, including applications, legacy systems, product data, third party data, online data, interaction data, and IoT data.

OpenRefine

Value proposition for potential buyers: OpenRefine, formerly known as Google Refine, is a free open source tool for managing, manipulating, and cleansing data, including big data. The application can accommodate up to a few hundred thousand rows of data. It cleans, reformats and transforms diverse and disparate data. OpenRefine is available in several languages, including English, Chinese, Spanish, French, Italian, Japanese, and German.

Key values/differentiators:

• GoogleRefine cleans and transforms data from a wide variety of sources, including standard applications, the web, and social media data.

• The application provides powerful editing tools to remove formatting, filter data, rename data, add elements, and accomplish numerous other tasks. In addition, the application can interactively change large chunks of data in bulk to fit different requirements.

• The ability to reconcile and match diverse data sets makes it possible to obtain, adapt, cleanse, and format data for web services, websites, and numerous database formats. In addition, GoogleRefine accommodates numerous extensions and plugins that work with many data sources and data formats.

SAS Data Management

Value proposition for potential buyers: SAS Data Management is a role-based graphical environment designed to manage data integration and cleansing. It includes powerful tools for data governance and metadata management, ETL and ELT, migration and synchronization capabilities, a data loader for Hadoop, and a metadata bridge for handling big data. Gartner named SAS a “Leader” in its 2020 Magic Quadrant for Data Integration Tools.

Key values/differentiators:

• SAS Data Management offers a powerful set of wizards that aid in the entire spectrum of data quality management. These include tools for data integration, process design, metadata management, data quality controls, ETL and ELT, data governance, migration and synchronization, and more.

• Strong metadata management capabilities aid in maintaining accurate data. The application offers mapping, data lineage tools that validate information, wizard-driven metadata import and export, and column standardization capabilities that aid in data integrity.

• Data cleansing takes place in native languages with specific language awareness and location awareness for 38 regions worldwide. The application supports reusable data quality business rules, and it embeds data quality into batch, near-time, and real-time processes.

Precisely Trillium

Value proposition for potential buyers: Precisely’s purchase of Trillium has positioned the company as a leader in the data integrity space. It offers five versions of the plug-and-play application: Trillium Quality for Dynamics, Trillium Quality for Big Data, Trillium DQ, Trillium Global Locator, and Trillium Cloud. All address different tasks within the overall objective of optimizing and integrating accurate data into enterprise systems.

Key values/differentiators:

• Trillium Quality for Big Data cleanses and optimizes data lakes. It uses machine learning and advanced analytics to spot dirty and incomplete data, while delivering actionable business insights across disparate data sources.

• Trillium DQ works across applications to identify and fix data problems. The application, which can be deployed on-premises or in the cloud, supports more than 230 countries, regions and territories. It integrates with numerous architectures, including Hadoop, Spark, SAP, and Microsoft Dynamics.

• Trillium DQ can find missing, duplicate, and inaccurate records, but also uncover relationships within households, businesses, and accounts. It includes an ability to add missing postal information as well as latitude and longitude data, as well as other key types of reference data.

• Trillium Cloud focuses on data quality for public, private, and hybrid cloud platforms and applications. This includes cleansing, matching, and unifying data across multiple data sources and data domains.

Talend Data Quality

Value proposition for potential buyers: Talend focuses on producing and maintaining clean and reliable data through a sophisticated framework that includes machine learning, pre-built connectors and components, data governance and management, and monitoring tools. The platform addresses data deduplication, validation, and standardization. It supports both on-premises and cloud-based applications while protecting PII and other sensitive data. Gartner rated the firm a “Leader” in its 2020 Magic Quadrant for Data Integration Tools.

Key values/differentiators:

• The data integrity application uses a graphical interface and drill down capabilities to display details about data integrity. It allows users to evaluate data quality against custom-designed thresholds and measure performance against internal or external metrics and standards.

• The application enforces automatic data quality error resolution through enrichment, harmonization, fuzzy matching, and deduplication.

• Talend offers four versions of its data quality software. These include two open-source versions with basic tools and features, and a more advanced subscription-based model that includes robust data mapping, reusable “joblets,” wizards, and interactive data viewers. More advanced cleansing and semantic discovery tools are available only with the company’s paid Data Management Platform.

TIBCO Clarity

Value proposition for potential buyers: TIBCO Clarity places a heavy emphasis on analyzing and cleansing large volumes of data to produce rich and accurate data sets. The application is available in on-premises and cloud versions. It includes tools for profiling, validating, standardizing, transforming, deduplicating, cleansing, and visualizing for all major data sources and file types.

Key values/differentiators:

• Clarity offers a powerful deduplication engine that supports pattern-based searches to find duplicate records and data. The search engine is highly customizable; it allows users to deploy match strategies based on a wide array of criteria, including columns, thesaurus tables, and other criteria like multiple languages. It also lets users run deduplication against a dataset or an external master table.

• A faceting function allows users to analyze and regroup data according to numerous criteria, including by star, flag, empty rows, and text patterns. This simplifies data cleanup while providing a high level of flexibility.

• The application supports strong editing functions that let users manage columns, cells, and tables. It supports splitting and managing cells, blanking and filling cells, and clustering cells.

• The address cleansing function works with TIBCO GeoAnalytics as well as Google Maps and ArcGIS.

Validity DemandTools

Value proposition for potential buyers: Validity, the maker of DemandTools, delivers a robust collection of tools designed to manage CRM data within Salesforce. The product accommodates large data sets and identifies and deduplicates data within any database table. It can perform multi-table mass manipulations and standardize Salesforce objects and data. The application is flexible and highly customizable, and it includes powerful automation tools.

Key values/differentiators:

• The vendor focuses on providing a comprehensive suite of data integrity tools for Salesforce administrators. DemandTools compares a variety of internal and external data sources to deduplicate, merge, and maintain data accuracy.

• DemandTools offers many powerful features, including the ability to reassign ownership of data. In addition, a Find/Report module allows users to pull external data, such as an Excel spreadsheet or Access database, into the application and compare it to any data residing inside a Salesforce object.

• The Validity JobBuilder tool automates data cleansing and maintenance tasks by merging duplicates, backing up data, and handling updates according to preset rules and conditions.

Comparison Chart of Data Quality Software