The Top 10 Data Quality Tools

Ataccama specializes in data quality and management. Their Data Quality & Governance platform helps eliminate data inconsistencies, augment accuracy, and rebuild trust in an organization’s data resources. The aim is to provide secure, quality data for dependable analytics and reporting, and to reduce the risks associated with data management, with built-in protection for sensitive data.

Two crucial features of the Ataccama Data Quality & Governance platform are AI-assisted data preparation and validation, as well as proactive data quality assurance. AI technology is utilized to streamline data readiness, automate the labour-intensive processes of data preparation and validation, speed up operations, and provide managers with timely, trustworthy information. The proactive data quality function involves monitoring, profiling, and detecting anomalies. These capabilities work continuously to identify and rectify issues in real-time; they use automated alerts and notifications so that analyst teams are quickly made aware of any issues that can’t be addressed automatically. Ataccama’s platform also incorporates data governance, including metadata management, lineage, and stewardship. It enables access controls and security measures that prevent unauthorized data modifications and handling, providing a safe and controlled environment for data.

By tightly integrating data governance and quality, Ataccama’s Data Quality & Governance platform can help boost business and operational efficiency. The platform is also highly flexible, utilizing powerful processing capabilities to work on billions of records and handle millions of API calls from front-end apps without compromising performance.

Overall, Ataccama is a strong choice for organizations that want to balance security and control with the need for organization-wide data accessibility.

Collibra Data Quality & Observability is a data monitoring tool that helps detect and quickly address any anomalies in data quality and pipeline reliability. The software 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 Data Quality & Observability stands out for its automatic data quality control, negating the requirement for pre-set rules. By employing data science and machine learning, it can rapidly detect data issues, while its AI-powered AdaptiveRules feature provides users relief from manually coding data quality rules. In addition to its AI- and ML-powered features, Collibra’s platform offers powerful automations that help streamline the data quality improvement process. It incorporates an easily accessible repository of auto-validation rules specific to each industry, and can automatically identify sensitive data, enforce quality, and take actions on faulty data records.

An added functionality of Collibra’s platform is the creation of custom data quality rules, allowing users to create personalized rules with an in-built SQL editor. This helps avoid repetitive rule rewrites and programming language lock-in as data moves across systems. The software also comes with a data pipeline monitoring feature that ensures data-based decisions are made on fresh insights. Finally, it offers schema change detection to track schema evolution to prevent potential issues impacting downstream output.

Experian Aperture Data Studio is a platform that offers self-service data quality management. This software enables users to gain a consistent, accurate, and comprehensive understanding of their consumer data. Deployable on physical hardware or virtual machines, it can function both on-premises and in the cloud.

Aperture Data Studio’s user interface and workflow capabilities facilitate effortless data validation, cleansing, deduplication, and enrichment. These workflows are extendable and repeatable, ensuring consistent data transformation across the enterprise. It also offers a sophisticated drag-and-drop workflow feature, which makes building complex data processes quick and audit-friendly. Another key feature of the Aperture Data Studio is its ability to ingest data from diverse sources, like Hadoop clusters. As a result, previously isolated datasets can be integrated to provide a singular view of the customer. This data can be cleaned and enhanced with Experian’s globally curated sets of consumer and business data, giving users very deep consumer insights.

Aperture Data Studio works well with existing technology stacks and data feeds, which, along with its on-premises and cloud deployment options, makes the platform relatively easy to implement. Once deployed, its 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.

IBM InfoSphere Information Server is designed to optimize data management and quality, transforming raw data into reliable information. The solution can analyze and monitor data quality continuously, clean and standardize data, match records to eradicate duplicates, and preserve data lineage.

The IBM InfoSphere Information Server supplies data cleansing features that automate the investigation of source data, allowing for information standardization and record matching based on defined business rules. It also supports ongoing data quality monitoring to diminish the spread of incorrect or inconsistent data. The solution is also equipped with deployment adaptability. This enables rapid implementation of new applications, data, and services in the most suitable location, whether that be on-premises, in the cloud, or a combination of both. Another of the platform’s key functions is the management of data quality issues, establishing a rectification plan through metrics in line with your business objectives. It assists in managing a data governance program and enables customization of data standardization processes in line with business requirements, such as data enrichment or data cleansing. It also offers data validation features, including configuration for data validation rules. Finally, IBM InfoSphere Information Server includes a “classification” function that identifies the location of Personally Identifiable Information (PII).

IBM InfoSphere Information Server provides robust data quality management capabilities, with in-built tools to help preserve the privacy of a dataset. Overall, we recommend this as a strong tool for organizations looking to improve their data quality as part of a wider data management initiative.

Informatica Cloud Data Quality is a comprehensive solution that helps businesses to identify, rectify, and keep track of data quality issues within their applications. It supports a co-operative approach, combining the efforts of business users and IT staff to develop a data-driven environment. This collaboration promotes faster cloud benefit realization via expedited migrations and high-trust insights from data sources like cloud data warehouses, data lakes, and SaaS applications.

Key features of Informatica Cloud Data Quality include self-service data quality for business users, allowing for the identification and resolution of data issues without the need for supplementary IT code or development. This benefits of this include increased security, reliability, and focus on operational excellence, without added infrastructure investment. The Informatica Cloud Data Quality tool also includes a rich set of data quality transformations and universal connections, providing comprehensive, modular support for all types of data and use cases. Another important feature is CLAIRE, an engine that delivers metadata-driven artificial intelligence to enable intelligent recommendations of data quality rules drawn from similar data management patterns. Consequently, it enhances automatic detection of data similarity, a precursor to detecting and eliminating duplicate data entries.

Overall, Informatica Cloud Data Quality simplifies administrative processes and lowers overhead costs by providing a unified data quality tool that can be used across departments, applications, and even deployment models, all fully cloud-based and economically priced.

Melissa is a software company that specializes in improving data quality to help businesses reduce expenses, augment revenue, and gain in-depth knowledge about their customers. Unison, Melissa’s unified customer data platform, allows data stewards to clean and monitor customer data without the need for programming.

Unison allows a connection to your data for better comprehension, data cleansing for optimal accuracy, and report generation with detailed, user-friendly graphics. With Unison, data can be profiled and monitored to identify low-quality data sources; cleaned and standardized using machine learning and tailored advanced rules; verified, enriched, matched, and consolidated to achieve a comprehensive customer overview; and rules can be applied for generalized knowledge-based data quality. Unison allows for the verification and standardization of U.S., Canadian, and international addresses, with autocomplete features to improve data entry speed and accuracy, and it permits the conversion of addresses into latitude and longitude coordinates for enhanced mapping and analytics. It also offers identity, email, and phone verification features to expedite customer onboarding and to prevent fraud, as well as eliminating duplicate records.

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. As such, we recommend Melissa Unison to any sized organization looking to improve the quality of their data, but particularly those in regulated industries that may have a stronger focus on data security and privacy.

Precisely Data Integrity Suite is an integrated suite designed to improve the accuracy and context of your data. The Suite comprises seven services that aid businesses throughout the data management and analysis cycle. One of these is its Data Quality service, which offers data validation, geocoding, and enrichment capabilities to maximize the value of your essential data assets.

Precisely’s Data Integrity Suite offers a user-friendly interface that visualizes data changes in real-time. This helps streamline the process of creating data rules, as well as accommodating different users to employ these rules. The Suite also comes with a built-in, machine learning-assisted matching and linking system that minimizes data duplication. This system is further strengthened with automated data quality suggestions that provide users with recommended actions to take to improve the quality of their data. The Precisely Data Integrity Suite allows users to design rules in the cloud—ensuring scalability and cost-effectiveness—and deploy them in diverse environments. It ensures consistent and accurate contact information like names, emails, phone numbers, postal addresses, bolstering the trust in your data. The Suite facilitates data management and enrichment using unique identifiers assigned to each postal address, simplifying data management across different systems or datasets.

Finally, the integrated data catalog in the Precisely Data Integrity Suite collates technical information about your business data assets into an easily understandable format. Once your data assets are cataloged, quality rules can be created for any data asset, maximizing efficacy and productivity.

SAP Master Data Governance is a central hub for managing and improving the quality of your business-critical data, allowing for more efficient work practices and enhanced decision-making processes. Utilizing SAP’s Master Data Management Layer, which is based on the SAP Business Technology Platform, this application consolidates and manages master data centrally.

SAP Master Data Governance offers domain-specific data governance. With this, businesses can control and consolidate or create, change, and distribute master data across their enterprise systems. Tight integration with other SAP solutions supports the reuse of data models, business logic, and validation frameworks. The application also supports open integration with third-party products and services. SAP Data Governance enables teams to own unique master data attributes and maintains validated values for specific data points via collaborative workflow routing and notification. For data quality and process analytics, it defines, validates, and monitors business rules, confirming readiness on the master data, and analyzing its management performance.

SAP Master Data Governance can be deployed on-premises or in private and public cloud environments and offers a cloud edition for hybrid and cloud systems, thus empowering organizations to move to the cloud at their own pace, all the while maintaining consistent master data. It supports all master data domains and implementation styles, and has prebuilt data models, business rules, workflows, and user interfaces.

SAS Viya is a data preparation and data quality solution by global analytics leader, SAS. As a cloud-native and cloud-agnostic solution, SAS Viya makes data preparation simple and accessible. Its visual user interface eliminates technical hurdles and enables individual users to blend, shape, and process data, freeing up IT resources for more strategic tasks.

SAS Viya’s features include efficient data preparation and in-memory data cleansing functions, allowing users to dedicate more time to data analysis and responsive decision-making. The platform’s drag-and-drop transformations allow for hassle-free data preparation for analytics and eliminate the need for coding or reliance on IT support. SAS Viya supports low-code/no-code data quality, assisting data processing efforts with multilanguage code support and a robust low-code visual flow builder. Its leading data profiling, data quality, and entity resolution technologies aid in the identification and rectification of data quality issues throughout the data pipeline.

With SAS Viya, collaboration and task reuse is streamlined thanks to its integrated platform for proficient data preparation and data quality management. This ensures consistency and quality throughout the data life cycle, as teams can seamlessly collaborate on data projects and share and reuse data preparation tasks. Overall, we recommend SAS Viya as a robust, yet user-friendly, data quality tool that’s suitable even for non-technical users.

Talend’s Data Fabric is a comprehensive platform that integrates data quality, integrity, and governance into a modular system. Its data integration module facilitates the collection, transformation, and mapping of data; ensures data trust throughout the data lifecycle with its data integrity and governance features; and its data quality module automatically profiles, cleans, and masks data in real time. Talend Data Fabric is also equipped for application and API integration, providing users the capability to share and deliver value from trusted data internally and externally.

As an essential component of Talend Data Fabric, the Data Quality module uses machine learning to recommend solutions for data quality issues in real time data flow. It offers a user-friendly interface that is easy to navigate for both business and technical users, promoting collaboration across the company. Within the Data Quality module, Talend’s data profiling functionality allows for the swift identification of data quality issues and the discovery of hidden patterns and anomalies. This is made possible through summary statistics and graphical representations. The built-in Talend Trust Score offers an immediate, understandable, and actionable assessment of data confidence. This feature ensures the safe sharing of datasets and indicates which datasets need to undergo additional data cleansing.

Talend also automatically cleans incoming data and enriches it with details from external sources. By handling data tasks in this manner, it emancipates business and data analysts to concentrate on more substantial tasks. Finally, the platform offers in-built compliance support, providing a feature for masking sensitive data and ensuring alignment with internal and external data privacy and data protection regulations.