Grid® Report for Data Quality | Summer 2022

Grid® for Data Quality Tools

Leaders
High Performers
Contenders
Niche
DemandTools
Introhive
RingLead
Openprise
Melissa Clean Suite
Melissa Data Quality Suite
Trifacta
Anomalo
IBM InfoSphere QualityStage
Syncari
WinPure Clean & Match
BizProspex CRM Cleaning
Insycle
Microsoft Data Quality Services
Talend Open Studio for Data Quality
D&B Optimizer
SAP Data Services
Informatica Data Quality and Governance Cloud
SAS Data Quality
Oracle
TIBCO Omni-Gen
IBM Infosphere
OpenRefine
DataMatch Enterprise
Cloudingo
DupeCatcher
Market Presence Information
Satisfaction Information
Data Quality Tools Definition

Data quality tools analyze sets of information and identify incorrect, incomplete, or improperly formatted data. After profiling data concerns, data quality tools cleanse or correct that data based on previously established guidelines. Deletion, modification, appending, and merging are all common methods of data set cleansing or correction; data analysts, marketers, and salespeople are just a few positions that benefit from leveraging data quality solutions.

By targeting and cleaning data lists, data quality software allows businesses to establish and maintain high standards for data integrity. These solutions are also helpful for ensuring that data adheres to these standards, based on the required industry, market, or in-house regulations. This process of maintaining data integrity enhances the reliability of such information for business use. Data sets can range from customer contact information to granular financial statistics and much more.

Data quality software products may also share features or coexist with master data management (MDM) software, data integration software, or big data software. While tangentially related to data quality solutions from a functional standpoint, address verification software differs through its distinct use cases, focus on physical location data, and reliance on authoritative location data sourcing to verify correctness.

To qualify for inclusion in the Data Quality category, a product must:

  • Enable data profiling and identify data anomalies
  • Provide basic data cleansing functionalities like record merge, append, and delete
  • Allow data modification and standardization based on predefined rules
  • Allow automated and manual cleaning options
  • Offer preventive measures to preserve data integrity
Data Quality Grid® Scoring Description
Products shown on the Grid® for Data Quality have received a minimum of 10 reviews/ratings in data gathered by May 31, 2022. Products are ranked by customer satisfaction (based on user reviews) and market presence (based on market share, seller size, and social impact) and placed into four categories on the Grid®:
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