# Best Data Quality Tools - Page 9 ## How Many Data Quality Tools Products Does G2 Track? **Total Products under this Category:** 251 ### Category Stats (Aug 2026) - **Average Rating:** 4.48/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product:** Data Quality Navigator (+3.87%) - Among all products in this category, Data Quality Navigator recorded the largest rating increase compared to last month _Last updated: August 01, 2026_ ## How Does G2 Rank Data Quality Tools Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 12,500+ Authentic Reviews - 251+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## G2 Grid® for Data Quality Tools ![G2 Grid® for Data Quality Tools plotting products by satisfaction and market presence](https://www.g2.com/categories/data-quality/grids.png?focus%5B%5D=1327283&focus%5B%5D=142449&focus%5B%5D=135441&focus%5B%5D=1613254&focus%5B%5D=148877&focus%5B%5D=19607&focus%5B%5D=122327&focus%5B%5D=1646038) Highlighted products: SAS Viya, Monte Carlo, GTM Studio - Powered by ZoomInfo, Data Quality Navigator, dbt, HubSpot Data Hub, DQLabs, and Quest Data Intelligence. Underlying data: [Grid® JSON](https://www.g2.com/categories/data-quality/grids.json?focus%5B%5D=sas-sas-viya&focus%5B%5D=monte-carlo&focus%5B%5D=gtm-studio-powered-by-zoominfo&focus%5B%5D=data-quality-navigator&focus%5B%5D=dbt&focus%5B%5D=hubspot-data-hub&focus%5B%5D=dqlabs&focus%5B%5D=quest-data-intelligence) **Sponsored** ### QuerySurge QuerySurge is an enterprise-grade data quality platform that leverages AI to continuously automate data validation across your entire ecosystem ‐ from data warehouses and big data lakes to BI reports and enterprise applications. With AI-powered test creation, scalable architecture, and the leading DevOps for Data CI/CD integration, QuerySurge ensures data integrity at every stage of the pipeline. Automated Data Validation Use Cases: QuerySurge provides a smart, AI-driven, data validation & ETL testing solution for your automated testing needs. - Data Warehouse / ETL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - Business Intelligence (BI) Report Testing - Big Data Testing - Enterprise Application Data Testing What QuerySurge Provides: - Automation of your manual data validation and testing process - Ease-of-use, low-code/no-code features - Generative AI capabilities for test creation - Testing across 200+ data platforms - Integration into your CI/CD DataOps pipeline - Acceleration of your data analysis - Ensurance of regulatory compliance Key Features: - Data Connection Wizard provides an easy way to link to your data stores - Visual Query Wizard builds table-to-table and column-to-column tests without writing SQL - Generative AI module automatically creates transformation tests in bulk - DevOps for Data provides a RESTful API with 110+ calls and Swagger documentation and integrates into CI/CD pipelines - Create Custom Tests and modularize functions with snippets, set thresholds, stage data, check data types & duplicate rows, full text search, and asset tagging - Schedule tests to run immediately, at a predetermined date & time, or after any event from a build/release, CI/CD, DevOps, or test management solution - Multi-project support in a single instance, new Global Admin user, assign users and agents, import and export projects, and user activity log reports - Webhooks provide real-time integrations with DevOps, CI/CD, test management, and alerting tools - Ready-for-Analytics provides seamless integration with QuerySurge and your BI tool or open-source Metabase to create custom reports and dashboards and gain deeper, real-time insights into your data validation and ETL testing workflows - Data Analytics Dashboards and Data Intelligence Reports track, analyze, and communicate data quality [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=74&secure%5Bchosen_at%5D=2026-08-03T05%3A17%3A20Z&secure%5Bdisplayable_resource_id%5D=74&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=74&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=54942&secure%5Bresource_id%5D=74&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-quality%3Fpage%3D9%26post_lead_product%3Dopenprise&secure%5Btoken%5D=d7def7485a88d22974fd6f735f8e4445b6b377d4c23eee7c7bbbc073655876f0&secure%5Burl%5D=https%3A%2F%2Fwww.querysurge.com%2Fget-started%2Fprivate-demo%3Futm_source%3DG2%26utm_medium%3Dcpc%26utm_campaign%3DG2-reviews&secure%5Burl_type%5D=book_demo) ### [Global IDs Data Governance Platform](https://www.g2.com/products/global-ids-data-governance-platform/reviews) Global IDs is an innovative software company delivering purpose-built solutions for data-centric organizations. Global IDs is committed to helping organizations of any size solve business problems with core metadata management techniques in an automated and scalable approach. Our integrated platform delivers key capabilities that enable transparency, trust and traceability of your data assets. A highly automated approach to implementing a Data Governance methodology that drives cost optimization and revenue growth by uncovering hidden insights and opportunities. **Average Rating:** 4.2/5.0 **Total Reviews:** 3 #### How Do G2 Users Rate Global IDs Data Governance Platform? - **Quality of Support:** 8.3/10 (Category avg: 8.9/10) #### Who Is the Company Behind Global IDs Data Governance Platform? - **Seller:** [Global IDs](https://www.g2.com/sellers/global-ids) - **Year Founded:** 2001 - **HQ Location:** Princeton, US - **Twitter:** @GlobalIDs 3,943 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=afde7f9669ae581dd42f272b1bb875f7ad711cec1de6f5811e59b994408c28fd&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fglobal-ids&secure%5Burl_type%5D=linkedin_company_website) 90 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 33% Large, 33% Medium #### What Are Recent G2 Reviews of Global IDs Data Governance Platform? **["Degital store data in system"](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-7825968)** **Rating:** 5.0/5.0 stars _— Verified User in Financial Services_ [Read full review](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-7825968) **["Transparency, Traceability, Trust along with Analytics for your enterprise data"](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-9083309)** **Rating:** 4.0/5.0 stars _— Sumeet J._ [Read full review](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-9083309) ### [Human Inference DataCleaner](https://www.g2.com/products/human-inference-datacleaner/reviews) DataCleaner is your comprehensive data quality Swiss army knife. **Average Rating:** 3.5/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate Human Inference DataCleaner? - **Quality of Support:** 8.3/10 (Category avg: 8.9/10) #### Who Is the Company Behind Human Inference DataCleaner? - **Seller:** [Quadient](https://www.g2.com/sellers/quadient) - **Year Founded:** 1924 - **HQ Location:** Bagneux, France - **Twitter:** @Quadient 3,878 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=d2072fab36f456a66f4b36979b4e2366f659bdc64f14fc6ed92398977d04a5d8&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fquadient%2F&secure%5Burl_type%5D=linkedin_company_website) 3,966 employees on LinkedIn® - **Ownership:** EPA: QDT #### Who Uses This Product? - **Company Size:** 100% Small #### What Do G2 Reviewers Say About Human Inference DataCleaner? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **customization options** in Human Inference DataCleaner, enhancing accuracy and relevance of their data solutions. - Users praise Human Inference DataCleaner for its **effective data cleaning and matching capabilities** , ensuring accurate information management. - Users value the **data quality** that Human Inference DataCleaner provides for accurate and reliable data processing. ### [iData](https://www.g2.com/products/idata/reviews) iData is an easy to use, forward-thinking, automated and repeatable on-premise solution designed to reduce the complexities in your data migrations, at the same time provide rapid 100% assurance and coverage of all your migrated data. What does iData do?  Achieves 100% assurance and 100% coverage of all your migrated data.  Rapid execution of scripts to provide feedback in minutes not weeks or months  Easy to use and tailor made to fit your unique requirements  Provides automated generation of script templates  Assures against corrupted data fields  Identifies missing records  Traces and corrects failing records.  Captures and displays in easy to understand reports Why do you need iData for your Data Migrations?  No other tool provides comparison and assurance for migrated data  iData manages the changes made during your migration and automates comparison of all records  100% assurance for migrated data  The only tool in the market that is tailored to fit your unique requirements  A unique primary focus on testing, and assurance  Lightweight and easy to deploy **Average Rating:** 4.0/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate iData? - **Quality of Support:** 8.3/10 (Category avg: 8.9/10) - **Automation:** 10.0/10 (Category avg: 8.7/10) - **Identification:** 10.0/10 (Category avg: 8.9/10) #### Who Is the Company Behind iData? - **Seller:** [Intelligent Delivery Solutions](https://www.g2.com/sellers/intelligent-delivery-solutions) - **Year Founded:** 2014 - **HQ Location:** Manchester, GB - **Twitter:** @intelligent\_ds 235 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=157d90e5dd4d2f7d1419babc222ee6107dd12e978d7649dc4695702c004ec513&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fintelligent-delivery-solutions&secure%5Burl_type%5D=linkedin_company_website) 24 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Large #### What Are Recent G2 Reviews of iData? **["Great product and Responsive Team"](https://www.g2.com/survey_responses/idata-review-6762018)** **Rating:** 4.0/5.0 stars _— Tara T._ [Read full review](https://www.g2.com/survey_responses/idata-review-6762018) ### [Infoveave](https://www.g2.com/products/infoveave/reviews) Infoveave is an AI-powered unified data platform that helps enterprises automate data pipelines, improve data quality, enable predictive analytics, and turn insights into measurable business actions — all within a single environment. Unlike traditional BI tools or standalone ETL platforms, Infoveave connects the entire data lifecycle from ingestion and transformation to governance, analytics, and operational execution. Infoveave is built on a strong foundation of data governance and data security, ensuring consistent data management, compliance, and protection. With capabilities like data lineage tracking, metadata management, row-level security, and audit trails, the platform helps maintain data integrity and control. Key Features Fovea - AgenticAI Assistant Infoveave's AgenticAI, Fovea, is embedded across the platform. Fovea assists in building data transformations, suggesting insights, automating workflows, and simplifying advanced analytics, reducing technical dependency and improving cross-team adoption. Data Automation & Integration • 50+ native connectors (cloud apps, databases, warehouses) • Automated data ingestion & transformation • Workflow orchestration with monitoring & alerts • Real-time pipeline visibility AI-Powered Analytics & Predictive Modeling • AutoML for predictive insights • What-if scenario planning • Python integration for advanced modeling • API-accessible analytics endpoints Conversational Dashboards & Self-Service BI • Natural language queries • 100+ interactive visuals • Drill-down exploration • Scheduled & automated reporting Built-in Data Quality & Governance • Automated validation & anomaly detection • Data catalog & lineage tracking • Role-based access control • Audit trails & governance workflows Data Apps & Operational Workflows • Low-code applications • Integrated data capture forms • Automated decision triggers • Insight-to-action workflows Business Value • Faster deployment of data pipelines • Improved data accuracy and trust • Reduced reliance on multiple disconnected tools • Faster decision-making cycles • Measurable operational efficiency Infoveave unifies data automation, AI-powered analytics, governance, and operational workflows into one intelligent platform — turning enterprise data into trusted, actionable decisions. **Average Rating:** 4.9/5.0 **Total Reviews:** 9 #### How Do G2 Users Rate Infoveave? - **Quality of Support:** 10.0/10 (Category avg: 8.9/10) #### Who Is the Company Behind Infoveave? - **Seller:** [Noesys Software](https://www.g2.com/sellers/noesys-software) - **HQ Location:** N/A - **Twitter:** @infoveave 15 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=031a186e4dacb0ab22fe119526e419c6197ed31793cb242b118d7786dcbcabaa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Finfoveave-pty-ltd%2F&secure%5Burl_type%5D=linkedin_company_website) 3 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 44% Small, 22% Large #### What Do G2 Reviewers Say About Infoveave? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **ease of use** of Infoveave, appreciating quick data import and seamless dashboard creation. - Users value the **easy integrations** of Infoveave, facilitating quick data imports and seamless connections to various sources. - Users value the **ease of use and quick implementation** of Infoveave for efficient data handling and insights delivery. - Users commend the **excellent community support** from Infoveave, enhancing their experience with reliable service and assistance. - Users highlight the **excellent customer support** of Infoveave, noting its reliability and top-notch quality. ##### Cons - Users request more resources, citing **poor customer support** due to insufficient help content and videos available. - Users face **dashboard issues** that necessitate significant restructuring to isolate data from multi-tenant sources effectively. - Users face **initial difficulties with data cleaning** due to complex restructuring between multi-tenant reports and dashboards. - Users face **data inaccuracy** issues, needing extensive restructuring for dashboards to function correctly with multi-tenant reports. - Users face **initial difficulties with data integration** , as multi-tenant reports require extensive restructuring for effective use. #### What Are Recent G2 Reviews of Infoveave? **["Scalable data automation for multi-site operations: Our unified source of truth"](https://www.g2.com/survey_responses/infoveave-review-12599874)** **Rating:** 5.0/5.0 stars _— Satish G._ [Read full review](https://www.g2.com/survey_responses/infoveave-review-12599874) **["Infoveave Delivers Powerful, User-Friendly Shopfloor Intelligence"](https://www.g2.com/survey_responses/infoveave-review-12600038)** **Rating:** 5.0/5.0 stars _— Chore R._ [Read full review](https://www.g2.com/survey_responses/infoveave-review-12600038) ### [iugum Data Software](https://www.g2.com/products/iugum-data-software/reviews) iugum Data Software helps Improve your data management software to cleanse, match and merge your lists, datasets or databases. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate iugum Data Software? - **Quality of Support:** 5.0/10 (Category avg: 8.9/10) #### Who Is the Company Behind iugum Data Software? - **Seller:** [iugum Software](https://www.g2.com/sellers/iugum-software) - **HQ Location:** N/A - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Large #### What Are Recent G2 Reviews of iugum Data Software? **["A decent experience "](https://www.g2.com/survey_responses/iugum-data-software-review-520228)** **Rating:** 4.0/5.0 stars _— Verified User in Computer Software_ [Read full review](https://www.g2.com/survey_responses/iugum-data-software-review-520228) #### What Are G2 Users Discussing About iugum Data Software? - [What is iugum Data Software used for?](https://www.g2.com/discussions/iugum-data-software-what-is-iugum-data-software-used-for) - [What is iugum Data Software used for?](https://www.g2.com/discussions/what-is-iugum-data-software-used-for) ### [Konstellation](https://www.g2.com/products/konstellation/reviews) Konstellation is a data observability tool. Konstellation observes the scoped data sets, identifies anomalies, and prioritizes incidents. Fix What Matters is a fully automated approach to detecting data issues at scale, identifying their root cause, and serving as a prioritized list of incidents based on their impact on the business. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate Konstellation? - **Quality of Support:** 8.3/10 (Category avg: 8.9/10) #### Who Is the Company Behind Konstellation? - **Seller:** [Konstellation Data](https://www.g2.com/sellers/konstellation-data) - **Year Founded:** 2024 - **HQ Location:** Los Angeles, US - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=2a17b570d084d008c8be88aa5c8f39ab2f62056036c78fd35231c753b4fd7d0f&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkonstellation-data%2F&secure%5Burl_type%5D=linkedin_company_website) 6 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Large #### What Are Recent G2 Reviews of Konstellation? **["Observability Done Right"](https://www.g2.com/survey_responses/konstellation-review-9939892)** **Rating:** 5.0/5.0 stars _— William S._ [Read full review](https://www.g2.com/survey_responses/konstellation-review-9939892) ### [Lariat Data](https://www.g2.com/products/lariat-data/reviews) Empower Your Data Engineers with Lariat: The Solution for Continuous Data Quality Monitoring. Lariat stands out as a cutting-edge platform, specifically designed to proactively identify and address data inconsistencies before they impact your end-users. This robust tool is your safeguard against the challenges posed by evolving business logic, fluctuating input data, and dynamic infrastructure changes. With Lariat, ensure the reliability and integrity of your data products, freeing your data engineers from the burden of constant troubleshooting. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 #### Who Is the Company Behind Lariat Data? - **Seller:** [Lariat Data](https://www.g2.com/sellers/lariat-data) - **HQ Location:** N/A - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Small #### What Are Recent G2 Reviews of Lariat Data? **["Monitoring Data the right way"](https://www.g2.com/survey_responses/lariat-data-review-9168901)** **Rating:** 4.0/5.0 stars _— Verified User in Consulting_ [Read full review](https://www.g2.com/survey_responses/lariat-data-review-9168901) ### [MDO](https://www.g2.com/products/mdo/reviews) Create Spare Parts in SAP ERP from SAP Asset Intelligence Network in One Step Utilize the MDO Spare Parts app for SAP Asset Intelligence Network to trigger the creation of materials in SAP ERP using MDO. Select the spare parts you want to have in SAP ERP and press Go! **Average Rating:** 5.0/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate MDO? - **Quality of Support:** 8.3/10 (Category avg: 8.9/10) - **Automation:** 10.0/10 (Category avg: 8.7/10) - **Identification:** 10.0/10 (Category avg: 8.9/10) - **Preventative Cleaning:** 6.7/10 (Category avg: 8.5/10) #### Who Is the Company Behind MDO? - **Seller:** [Prospecta Softwares](https://www.g2.com/sellers/prospecta-softwares) - **Year Founded:** 2002 - **HQ Location:** Chatswood, New South Wales, Australia - **Twitter:** @Prospecta 238 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e8f22355e9ac536bbb6620484f89105b34c36068b73cea5b8392d905a15b7f10&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F107201&secure%5Burl_type%5D=linkedin_company_website) 290 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Medium #### What Are Recent G2 Reviews of MDO? **["MDO - Master Data Online"](https://www.g2.com/survey_responses/mdo-review-9416949)** **Rating:** 5.0/5.0 stars _— Tanay M._ [Read full review](https://www.g2.com/survey_responses/mdo-review-9416949) ### [Pantomath](https://www.g2.com/products/pantomath/reviews) Pantomath is pioneering the Data Operations Center (DOC), establishing a centralized, AI-driven platform necessary to manage data reliability as a strategic operational function. We are the first platform designed to continuously monitor, diagnose, and autonomously resolve data incidents across the entire cross-platform data ecosystem. Our approach transforms data reliability from a constant liability into an assured competitive advantage. Using purpose-built AI agents and a proprietary cross-platform interoperable data fabric, Pantomath automates the entire incident lifecycle: identifying the issue, pinpointing the single root cause, and executing immediate containment and mitigation. We empower organizations to move beyond costly reactive fixes, ensuring trustworthy data is delivered consistently and confidently to all stakeholders and consuming systems. Pantomath is designed for platform reliability teams, data engineers, and leaders responsible for data quality and SLAs. It supports critical use cases such as: - Detecting and resolving data incidents before stakeholders are affected - Unifying metadata, lineage, and job execution data for faster RCA - Automating resolution workflows and reducing mean time to acknowledge, detect, and resolve - Improving data trust across business teams by enabling transparency and accountability Key capabilities include: - Automated Discovery and Monitoring: Map and monitor pipelines, datasets, stored procedures, and dependencies across your stack. - AI-Powered RCA and Recommendations: Use built-in copilots to surface root cause and next steps in minutes. - Incident Correlation and Impact Analysis: Highlight downstream impact and notify the right teams in real time. - Autonomous Remediation: Self-heal pipelines through configurable automation policies. - Bring Your Own Catalog (BYOC): Integrate existing metadata tools to centralize data context. Pantomath gives enterprises a systemic, automated approach to data reliability - delivering trust, reducing noise, and empowering teams to scale data operations with confidence. **Average Rating:** 4.7/5.0 **Total Reviews:** 15 #### How Do G2 Users Rate Pantomath? - **Quality of Support:** 9.2/10 (Category avg: 8.9/10) #### Who Is the Company Behind Pantomath? - **Seller:** [Pantomath Inc.](https://www.g2.com/sellers/pantomath-inc) - **Company Website:** www.pantomath.com - **Year Founded:** 2022 - **HQ Location:** Cincinnati - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=c87cb82c70d30dfb53bef2822959052bbf7b3257bc5c437c721518095b4b0b12&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpantomathdata&secure%5Burl_type%5D=linkedin_company_website) 50 employees on LinkedIn® #### Who Uses This Product? - **Top Industries:** Financial Services - **Company Size:** 73% Large, 27% Medium #### What Do G2 Reviewers Say About Pantomath? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **exceptional data lineage visualization** of Pantomath, enhancing efficiency in managing complex data operations. - Users appreciate the **responsive and helpful customer support** from Pantomath, enhancing their overall experience and satisfaction. - Users commend Pantomath for its **enhanced efficiency** , enabling faster issue resolution and streamlined data operations. - Users value the **comprehensive data visibility** Pantomath offers, enhancing tracking and troubleshooting of their data operations. - Users value the **effective issue resolution** in Pantomath, allowing for quick identification and fixing of data pipeline problems. ##### Cons - Users report **overwhelming alerts** at setup, needing time to fine-tune them for meaningful insights. - Users find the **poor documentation** on Pantomath integration challenging, despite timely bug fixes from the team. - Users find the **complex setup** process challenging initially, requiring time to adjust and optimize configurations effectively. - Users find the **difficult learning curve** of Pantomath challenging, especially for those lacking prior experience. - Users find the **learning curve steep** , making it challenging for inexperienced individuals to utilize certain features effectively. #### What Are Recent G2 Reviews of Pantomath? **["Pantomath: A game changer for Traceabiliity and observability in data pipelines"](https://www.g2.com/survey_responses/pantomath-review-9815305)** **Rating:** 5.0/5.0 stars _— Maritza A._ [Read full review](https://www.g2.com/survey_responses/pantomath-review-9815305) **["First-class data pipeline lineage and visualization"](https://www.g2.com/survey_responses/pantomath-review-9780363)** **Rating:** 5.0/5.0 stars _— Trevor H._ [Read full review](https://www.g2.com/survey_responses/pantomath-review-9780363) ### [SQL Power DQguru](https://www.g2.com/products/sql-power-dqguru/reviews) SQL Power DQguru is a comprehensive data cleansing and Master Data Management (MDM tool designed to enhance data quality across various business entities, including customers, products, employees, and suppliers. By providing a 360-degree view of these entities, DQguru ensures that organizations have accurate and reliable data for informed decision-making. Key Features and Functionality: - Intuitive Graphical User Interface (GUI: Facilitates quick adoption and ease of use for data analysts. - Transform Process Interface: Allows rapid development and deployment of data conversion workflows. - Customizable Data Matching Criteria: Enables users to define specific rules for identifying duplicates. - Duplicate Verification and Merging: Utilizes an innovative interface to identify and merge duplicate records along with their related data. - Cross-Reference Table Generation: Links source system identifiers to target database identifiers for seamless data integration. - Extensive Transformation and Matching Functions: Includes concatenation, phonetic coding (Double Metaphone, Metaphone, Refined Soundex, Soundex, case conversion, string substitution, and substring operations. - Multiple Levels of Data Transformation: Offers match, merge, and cleansing engines to identify duplicates, remove redundant records, and reformat data as per defined rules. - Broad Database Support: Compatible with various databases for both source and target data integration. Primary Value and Problem Solved: SQL Power DQguru addresses the critical challenge of maintaining high-quality data within organizations. By automating data cleansing processes, validating and correcting addresses, and eliminating duplicates, it ensures that businesses operate with complete and accurate information. This leads to improved operational efficiency, better customer relationship management, and more reliable analytics and reporting. Whether for initial data migration or ongoing data maintenance, DQguru provides a robust solution to uphold data integrity across the enterprise. **Average Rating:** 3.0/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate SQL Power DQguru? - **Quality of Support:** 5.0/10 (Category avg: 8.9/10) - **Automation:** 8.3/10 (Category avg: 8.7/10) - **Identification:** 5.0/10 (Category avg: 8.9/10) - **Preventative Cleaning:** 6.7/10 (Category avg: 8.5/10) #### Who Is the Company Behind SQL Power DQguru? - **Seller:** [SQL Power DQguru](https://www.g2.com/sellers/sql-power-dqguru) - **Year Founded:** 1989 - **HQ Location:** Toronto, CA - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8711845043a2783881cb7a770cc9ec2fc123276be234b472e64c2eed401045c3&secure%5Burl%5D=http%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsql-power&secure%5Burl_type%5D=linkedin_company_website) 24 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Small #### What Do G2 Reviewers Say About SQL Power DQguru? _AI-generated summary from verified user reviews_ ##### Pros - Users commend the **automation features** of SQL Power DQguru, enhancing efficiency and streamlining data management workflows. - Users value the **customizable data quality rules** of SQL Power DQguru, enhancing their data management strategies effectively. - Users value the **data validation capabilities** of SQL Power DQguru, ensuring high-quality data through effective profiling and rules. - Users value the **robust reporting capabilities** of SQL Power DQguru, enhancing data analysis and decision-making efficiency. ##### Cons - Users face a **steep learning curve** with SQL Power DQguru, making it challenging to effectively utilize the product. - Users find the **complex setup** of SQL Power DQguru challenging, making it difficult to get started effectively. - Users find the **high cost** of SQL Power DQguru to be a significant barrier to adoption. - Users report **slow performance** with SQL Power DQguru, especially when handling large datasets, impacting productivity. - Users find the **steep learning curve** of SQL Power DQguru challenging, impacting their ability to utilize the product effectively. ### [Synthesized SDK](https://www.g2.com/products/synthesized-sdk/reviews) Apply Synthesized Scientific Data Kit (SDK) to bootstrap data where the density of data is low, automatically rebalance data to improve model performance, and anonymize data for repurposing. Improved model performance Benefit from up to 15% uplift in model performance with data rebalancing, data imputation, and high-quality synthetic data generation. SDK helps increase revenue across conversion, fraud, revenue recovery, and more. API-first extensible framework Extend and plug-in into any data platform or ETL pipeline including Airflow, Dataproc, Spark. Fast and easy deployments using Kubernetes, OpenShift, and Docker. Guaranteed compliance "Data as Code" approach enables you to codify complex compliance requirements into concrete data transformations. Full analytics and reporting Full visibility of key data metrics including data quality, data compliance, and model performance metrics in your reports. **Average Rating:** 4.5/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate Synthesized SDK? - **Quality of Support:** 8.3/10 (Category avg: 8.9/10) - **Automation:** 10.0/10 (Category avg: 8.7/10) - **Identification:** 10.0/10 (Category avg: 8.9/10) - **Preventative Cleaning:** 10.0/10 (Category avg: 8.5/10) #### Who Is the Company Behind Synthesized SDK? - **Seller:** [Synthesized](https://www.g2.com/sellers/synthesized) - **Year Founded:** 2020 - **HQ Location:** London, GB - **Twitter:** @Synthesizedio 3,080 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=6d19b8e75fe021e1250e97ebcd326848bfe711349d26851b364412c11f533ace&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsynthesized&secure%5Burl_type%5D=linkedin_company_website) 40 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Medium #### What Are Recent G2 Reviews of Synthesized SDK? **["Best API level production like testing data provider."](https://www.g2.com/survey_responses/synthesized-sdk-review-9963957)** **Rating:** 4.5/5.0 stars _— Hiten A._ [Read full review](https://www.g2.com/survey_responses/synthesized-sdk-review-9963957) ### [Uniserv Data Quality](https://www.g2.com/products/uniserv-data-quality/reviews) Uniserv is the largest expert of data quality, data integration and data management solutions in Europe. **Average Rating:** 5.0/5.0 **Total Reviews:** 1 #### How Do G2 Users Rate Uniserv Data Quality? - **Quality of Support:** 10.0/10 (Category avg: 8.9/10) - **Automation:** 8.3/10 (Category avg: 8.7/10) - **Identification:** 10.0/10 (Category avg: 8.9/10) - **Preventative Cleaning:** 10.0/10 (Category avg: 8.5/10) #### Who Is the Company Behind Uniserv Data Quality? - **Seller:** [Uniserv](https://www.g2.com/sellers/uniserv) - **Year Founded:** 1969 - **HQ Location:** Pforzheim, DE - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=2d9e186bdd291694359176c7c15b9beecc4f0853f0238c4874cbe2587cd3bc77&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Funiserv-gmbh&secure%5Burl_type%5D=linkedin_company_website) 92 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Small #### What Are Recent G2 Reviews of Uniserv Data Quality? **["Excellent Data Quality"](https://www.g2.com/survey_responses/uniserv-data-quality-review-5303697)** **Rating:** 5.0/5.0 stars _— Verified User in Accounting_ [Read full review](https://www.g2.com/survey_responses/uniserv-data-quality-review-5303697) #### What Are G2 Users Discussing About Uniserv Data Quality? - [What is Uniserv Data Quality used for?](https://www.g2.com/discussions/what-is-uniserv-data-quality-used-for) ### [Vertify](https://www.g2.com/products/vertify/reviews) Grounded by the philosophy that all three key revenue teams—sales, marketing, and customer success—should be aligned by process and technology, Vertify provides business automation software that easily syncs, cleans, and curates customer data within existing revenue tech stacks. - Identify bottlenecks between teams - Unify your customer journey and team's productivity - Unlock new revenue potential - Maximize your existing RevTech ROI - Respond to customers quicker - Gain better insights, smoother lead and customer management, and better campaigns - Scale operations to achieve faster results Aligning and integrating your sales, marketing, and customer success systems means everyone can work together with the same data. Why on earth would you want to have disjointed apps and processes? You and your customers deserve better. You deserve actionable data that gives teams direction, confidence and a shared view. - Best in class UI, API, and workflow automation - Proven ability to scale - Robust governance and security - Cloud-native, flexible delivery **Average Rating:** 4.6/5.0 **Total Reviews:** 5 #### How Do G2 Users Rate Vertify? - **Quality of Support:** 9.3/10 (Category avg: 8.9/10) #### Who Is the Company Behind Vertify? - **Seller:** [Vertify](https://www.g2.com/sellers/vertify) - **Year Founded:** 2016 - **HQ Location:** Austin, US - **Twitter:** @VertifyData 119 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=c78a9bacbd2d0e6f97974a214da7a6ed7a61e0a0b7e4240948bfe4bfbdd9a6d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fvertifydata&secure%5Burl_type%5D=linkedin_company_website) 9 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 60% Small, 40% Medium #### What Are Recent G2 Reviews of Vertify? **["Great platform with wonderful support resources and easy setup"](https://www.g2.com/survey_responses/vertify-review-7435215)** **Rating:** 4.5/5.0 stars _— Samantha M._ [Read full review](https://www.g2.com/survey_responses/vertify-review-7435215) **["Vertify - An excellent extension of our team!"](https://www.g2.com/survey_responses/vertify-review-7155854)** **Rating:** 5.0/5.0 stars _— Verified User in Glass, Ceramics & Concrete_ [Read full review](https://www.g2.com/survey_responses/vertify-review-7155854) ### [Zaloni Data Governance](https://www.g2.com/products/zaloni-data-governance/reviews) At Zaloni, we believe in the unrealized power of data. Our data management software, Arena, provides an augmented catalog that enables self-service data enrichment and consumption. We work with the world's leading companies, delivering exceptional data governance built on an extensible, machine-learning platform that both improves and safeguards enterprises’ data assets. To find out more visit www.zaloni.com. **Average Rating:** 4.0/5.0 **Total Reviews:** 1 #### Who Is the Company Behind Zaloni Data Governance? - **Seller:** [Zaloni](https://www.g2.com/sellers/zaloni) - **Year Founded:** 2007 - **HQ Location:** Research Triangle Park, US - **Twitter:** @zaloni 1,289 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=d0e7142da7e0acdd7b79bf6340813db1803e56be9169e1a1fc9bfa14d17149d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F859448&secure%5Burl_type%5D=linkedin_company_website) 62 employees on LinkedIn® #### Who Uses This Product? - **Company Size:** 100% Small #### What Are Recent G2 Reviews of Zaloni Data Governance? **["Great product"](https://www.g2.com/survey_responses/zaloni-data-governance-review-4419904)** **Rating:** 4.0/5.0 stars _— Jackie H._ [Read full review](https://www.g2.com/survey_responses/zaloni-data-governance-review-4419904) #### What Are G2 Users Discussing About Zaloni Data Governance? - [What is Arena by Zaloni used for?](https://www.g2.com/discussions/what-is-arena-by-zaloni-used-for) ### [1Platform](https://www.g2.com/products/1platform/reviews) 1Platform by Polestar Analytics is a unified, low-code data intelligence ecosystem that transforms enterprise data management through its powerful orchestration engine, AI capabilities, and comprehensive analytics portal. Built with a no-code platform for data engineering, it seamlessly integrates 100+ data sources while providing automated data lake acceleration, historical data migration, and comprehensive data quality scorecards with execution logs for complete governance. What sets 1Platform apart is its ability to deliver actionable intelligence at scale through unified insights access, agentic and generative AI capabilities that go beyond data summarization to extract additional insights and advanced preloaded, scalable and customisable LLM and reasoning models reducing time and talent requirement. The platform operates through a single, intuitive interface across any cloud environment with modular, plug-and-play architecture, featuring access-based configuration for analytics, dashboard management, and a comprehensive notifications hub for tracking key activities. Backed by 350+ successful clients, 1,000+ implementations, and an exceptional 87% repeat business rate across 20+ global markets, Polestar Analytics delivers the agility, speed, and intelligence that modern enterprises demand to maintain their competitive edge. #### Who Is the Company Behind 1Platform? - **Seller:** [Polestar Analytics](https://www.g2.com/sellers/polestar-analytics) - **Year Founded:** 2012 - **HQ Location:** Plano, US - **Twitter:** @PolestarLLP 508 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8facb9a65073b1327dc53c4a0fc6dfe85d2bcff111ea83abd0ce43aac0fa4d1f&secure%5Burl%5D=http%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpolestarsolutions%2526services&secure%5Burl_type%5D=linkedin_company_website) 634 employees on LinkedIn® - [‹ Prev‹ Prev](/categories/data-quality?order=g2_score&page=8&post_lead_product=openprise#product-list) - [1](/categories/data-quality?order=g2_score&post_lead_product=openprise#product-list) - [2](/categories/data-quality?order=g2_score&page=2&post_lead_product=openprise#product-list) - … - [5](/categories/data-quality?order=g2_score&page=5&post_lead_product=openprise#product-list) - [6](/categories/data-quality?order=g2_score&page=6&post_lead_product=openprise#product-list) - [7](/categories/data-quality?order=g2_score&page=7&post_lead_product=openprise#product-list) - [8](/categories/data-quality?order=g2_score&page=8&post_lead_product=openprise#product-list) - 9 - [10](/categories/data-quality?order=g2_score&page=10&post_lead_product=openprise#product-list) - [11](/categories/data-quality?order=g2_score&page=11&post_lead_product=openprise#product-list) - [12](/categories/data-quality?order=g2_score&page=12&post_lead_product=openprise#product-list) - [13](/categories/data-quality?order=g2_score&page=13&post_lead_product=openprise#product-list) - … - [16](/categories/data-quality?order=g2_score&page=16&post_lead_product=openprise#product-list) - [17](/categories/data-quality?order=g2_score&page=17&post_lead_product=openprise#product-list) - [Next ›Next ›](/categories/data-quality?order=g2_score&page=10&post_lead_product=openprise#product-list) Spotlight Categories [Restaurant POS Systems](https://www.g2.com/categories/restaurant-pos) [Compensation Management Software](https://www.g2.com/categories/compensation-management) [Video Conferencing Software](https://www.g2.com/categories/video-conferencing) [Marketing Analytics Tools](https://www.g2.com/categories/marketing-analytics) [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) Similar Categories - [Infrastructure as a Service (IaaS)](/categories/infrastructure-as-a-service-iaas) - [Active Metadata Management](/categories/active-metadata-management) - [Address Verification](/categories/address-verification) - [AIOps Platforms](/categories/aiops-platforms) - [Application Server](/categories/application-server) - [Blockchain](/categories/blockchain) - [Cloud File Storage](/categories/cloud-file-storage) - [Database Software](/categories/database-software) - [Data Center Infrastructure Management (DCIM)](/categories/data-center-infrastructure-management-dcim) - [Data Center Networking](/categories/data-center-networking) - [Data Exchange Platforms](/categories/data-exchange-platforms) - [Data Fabric](/categories/data-fabric) - [Data Integration Tools](/categories/data-integration-tools) - [Data Management Software](/categories/data-management-software) - [Data Mapping](/categories/data-mapping) [Browse Data Quality Themes](/categories/data-quality/themes) ![Shalaka Joshi](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Shalaka Joshi") SJ Researched and written by [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi) Updated March 10, 2026 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](https://www.g2.com/categories/master-data-management-mdm), [data integration software](https://www.g2.com/categories/data-integration), or [big data software](https://www.g2.com/categories/big-data). While tangentially related to data quality solutions from a functional standpoint, [address verification software](https://g2.com/categories/address-verification) 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 Show More * * * ## How Do You Choose the Right Data Quality Tools? ### What You Should Know About Data Quality Tools ### What are Data Quality Tools? Data quality software is a set of various tools and services created to derive meaningful data for organizations. The tools condition the data to meet the specific needs of the users. Data quality is an integral part of data governance and data management processes through which all the data of the organization is governed. Data quality tools make it possible to achieve accuracy, relevancy, and consistency of data to make better decisions. High-quality data can deliver desired outputs, whereas poor-quality data can result in disastrous insights. Organizations that are data-driven and frequently use data analytics for decision-making make data quality a prime factor in deciding its usefulness. ### What are the Common Features of Data Quality Tools? Features of data quality tools mainly consider the dimensions or the metrics that define quality. These solutions can support some or all of the functions as mentioned below to deliver useful end results: **Data cleansing:** It is the process of removing redundant, incorrect, and corrupt data. It is sometimes referred to as data cleaning or data scrubbing. Being one of the critical stages in data processing, most data quality tools have this feature. A few of the common data inaccuracies include incorrect entries and missing values. **Data standardization:** It is a major step in organizing data. It involves converting data into a common format which makes it easier for users to access and analyze the data. This stage fulfills one of the parameters of data quality—consistency. Bringing the data into a single common format makes sure that data is consistent. Data standardization plays a key role in achieving accuracy which is another factor in data quality. It helps by giving users access to the latest cleansed and updated data. **Data profiling:** Data profiling is the process of analyzing data, understanding the structure of data, and identifying the potential projects for the specified data. Data is minutely analyzed using analytical tools to detect characteristics like mean, minimum, maximum, and frequency. **Data deduplication:** It is a process to eliminate excessive copies of data and reduce storage requirements. It is also called intelligent compression or single-instance storage or data dedupe. **Data validation:** This feature ensures that data quality and accuracy are in place. In automated systems, there is minimal or almost no human supervision when the data is entered. This makes it essential to check that the data entered is correct. Common types of data validation include data check, code check, range check, format check, and consistency check. There also are certain data quality rules defined for data management platforms. **Extract, transform, and load (ETL):** When organizations advance in the technology strategy, data from existing systems are transferred to the new systems. ETL forms a vital task of the data migration process. The end goal is to maintain data quality for the data that is being migrated. ETL stands third in the phases of the data quality lifecycle. Other phases are quality assessment, quality design, and monitoring. It involves extracting data from the data sources, transforming it by deduplicating it, and loading it into the target database. **Master data management (MDM):** This feature manages quality data by organizing, centralizing, and enriching data. It includes non-transactional data like customer data and product data. MDM is important for enterprise data management. **Data enrichment:** This feature is the process of enhancing the value and accuracy of data by integrating internal and external data with the existing information. **Data catalog:** Data catalog hosts data and metadata to help users with their data discovery. Data quality monitoring tools have this feature to increase transparency in workflows. **Data warehousing:** Data warehousing focuses on unifying data from various data sources. It ensures enterprise data quality by improving the accuracy of data. **Data parsing:** Data usually is conformed to specific formats. For example address, telephone number, and email address all have data patterns. Parsing helps with such address verifications and also if the telephone numbers are conforming to the patterns.  Other features of data quality software: [ERP Capabilities](https://www.g2.com/categories/data-quality/f/erp) and [File Capabilities](https://www.g2.com/categories/data-quality/f/file). ### What are the Benefits of Data Quality Tools? Data is one of the most valuable resources for organizations today. Having high-quality data has the following advantages: **Effective data implementation:** Good quality data improves the performance of teams and results in better business. It keeps all the departments of the organization on the same page and helps them work efficiently. [**Improved customer relationships**](https://www.g2.com/categories/data-quality/f/crm) **:** Data quality plays a major role in retaining customers. It helps organizations track customer preferences and interests. **Insightful decision-making:** The decision-makers always need up-to-date information to make better decisions. Data quality tools ensure business intelligence is attained through high-quality data. Good data quality helps in reducing the risk of bad decisions based on poor-quality data and increasing the efficiency of the decision-making process. **Effective customer targeting:** With high-quality data at their fingertips, organizations can track the characteristics of their existing customers and create personas depending on what their customers prefer. This can further lead to forecasting the needs of the target market. **Efficient product development:** Engineering teams in software development companies can audit their KPIs like engagement with the new product online. Auditing data points like button clicks can help engineers understand how ready their product is to be launched in the market or if there are any changes needed.  **Data matching:** Effective data quality monitoring tools help in data matching. Data matching is the process of comparing two different data sets and matching them against each other. This process helps in identifying duplicate data within a [database](https://www.g2.com/categories/data-quality/f/database). ### Who Uses Data Quality Tools? Data being the new fuel is driving organizations to figure out how it can be used to make business decisions. Below is a list of departments that utilize data quality management software : **Data quality analysts:** They monitor the quality of data using data quality tools that help companies make informed decisions. They work with database developers to modify database designs as per the need. This persona primarily helps with data analysis, further improving the quality. **Marketing teams:** Marketing managers must have high-quality data at use because good quality data helps drive efficient marketing campaigns in the future. Data quality tools help the teams filter unnecessary information and focus on the target market to gain a better understanding. **IT teams:** Several times there are duplicate records which makes it difficult for IT teams to have data quality control in place. With the use of software, it is easier to govern the data and optimize data quality management. ### Challenges with Data Quality Tools  Data quality changes with what is fed into the system. Sometimes there are a few of the below-mentioned difficulties faced while using data quality tools: **Duplicated data:** Data deduplication tools are a must before passing over the data to the next steps. Since large amounts of data are generated through various disparate sources, it is often flawed, or some entries are duplicated. However, deduplication tools can identify the same data points and assign them for deduplication.  **Lack of complete information:** Manual entries can cause incomplete information or not having information for every dataset. This could cause data quality tools to underperform. **Heterogenous formats:** Inconsistent data formats are always a common pain point for data analysts. While working with data outsourcing services providers, it is recommended to specify preferred formats. ### How to Buy Data Quality Tools? #### Requirements Gathering (RFI/RFP) for Data Quality Software Depending upon the industry, there are a variety of data quality dimensions that must be kept in mind before the purchase of the software. Data management strategy is expected to address data governance requirements. Along with it, there are other requirements like data retention and archiving. An RFI or RFP from vendors helps to optimize the evaluation process.  #### Compare Data Quality Products **Create a long list** To begin with, organizations should make a list of data quality software vendors providing features like data profiling, data preparation, deduplication, and other relevant features depending on the results they are looking to achieve. **Create a short list** On the basis of the fulfillment of primary requirements, the next step covers shortlisting the vendors by asking a few questions like: - Do they provide automation in their software? - How do the products/tools maintain performance and scale? - What are their support timings and escalation procedures? **Conduct demos** Demos are an efficient way of verifying which vendor fits the bill. It gives the organization an in-depth understanding of the software. Organizations can also get answers to how well-stacked the vendor is. Usually, demos for data quality software would include the presentation of various tools and capabilities of the software such as data standardization feature, metadata management, and data quality management to name a few. #### Selection of Data Quality Tools **Choose a selection team** The team involved in making this decision must include relevant decision makers. A chief marketing officer, who often needs clean data to nurture leads from their team, can test the tools during the demo. The next member to be kept in the loop is the sales lead. Data quality is equally important for the sales workforce as they want to focus more on revenue generation than just updating the data in the CRM. Data analysts are also involved since they are the ones who use these tools for data quality assessments. Along with it, data quality analysts are included in the team because they use the software to examine the data for quality requirements depending on different departments and share this processed data with them. **Negotiation** Because data quality is of utmost importance, it is advisable to choose the right tools for assessment. Tools that work in real time and that can be used easily by business users are something organizations want to have. It is advisable to look at the pricing of the software, if there are any additional costs, and also if the vendor offers any discount. Many data quality tools are available in both cloud and on-premises structures. It is better to have tools in the cloud as manual data quality monitoring for enterprise data could be difficult for one person or even a team. **Final decision** The decision to buy data quality software has to be taken by the teams involved throughout the buying process. Sales, marketing, and data analyst teams can benefit from buying the right data quality software. ### Data Quality Trends **Data warehouse modernization** Data warehouse modernization helps the current data warehouse environment work in synchronization with rapidly changing requirements. Organizations are coping with managing the expansion of data and data systems by modernizing the data warehouse. This emerging trend focuses on data automation to achieve the desired quality of data and business practices alike. **Modern data hubs** Data hubs are data storage architectures with a seamless flow of data that follow the hub and spoke model. Modern data hubs have features like data storage, harmonization, governance, metadata, and indexing. These features indicate that data hubs are more efficient than data consolidation. **Data democratization** Recently, organizations are making data available to independent business functions. This is to improvise transparency and consistency amongst all the departments in the organization. Advancements in visualizations have made data visibility easier at a technical level and as the trend progresses, it is expected to have the same effect on non-technical users, i.e., ease of access to data. **Machine learning (ML) algorithms in data quality**  Machine learning (ML) algorithms have become important for a company's data management strategy. Enterprise data is usually big data which makes it essential to have automation. Machine learning algorithms can make it possible to automate the process giving end results. ML algorithms help in improving data quality scores by identifying wrong data, incomplete data, duplicate data, and also help in performing functions like clustering, detecting anomalies, and association rule mining.