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Data Quality Testing: Methods and Best Practices for 2025

Data quality testing ensures that data is accurate, reliable, and ready for decision-making. It involves validating data at various stages of the...

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9 Common Data Quality Problems and How to Fix Them

Data quality problems like inconsistent formats, outdated records, and human errors are frequent hurdles for organizations. By establishing clear...

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Best Data Profiling Tools in 2025: Full Comparison

Data profiling tools have become vital for ensuring data quality, compliance, and governance. This guide compares top platforms like OvalEdge,...

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Data Quality Dimensions: Key Metrics & Best Practices for 2025

Data quality dimensions, including accuracy, completeness, consistency, timeliness, validity, uniqueness, and integrity, define the standards for...

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Data Lifecycle Management: What It Is, How It Works & Why It Matters

Data Lifecycle Management (DLM) transforms chaotic data environments into structured, compliant ecosystems. This comprehensive guide to Data...

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Data Quality Tools 2025: The Complete Buyer’s Guide to Reliable Data

In 2025, data quality defines business trust, compliance, and AI reliability. With expanding data pipelines and self-service analytics, maintaining...

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Top 7 AI-Powered Open-Source Data Quality Tools in 2025

Open source data quality tools are evolving fast and AI is accelerating that evolution. By combining the flexibility of open frameworks with...

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Implementing All Four Aspects of Data Quality

Data quality implementation follows a series of tried-and-tested methodologies. In this blog, we explore these methodologies, the order in which you...

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Comparing AI Data Readiness and Data Quality

AI data readiness is different from traditional data quality. This article explains that difference using concrete examples so you can better prepare...

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7 Common Data Quality Pain Points for CROs: Examples & Fixes

Data quality issues can severely impact a Chief Risk Officer’s (CRO’s) ability to build accurate, compliant, and data-driven risk models. In this...

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AI Needs Domain Knowledge to Boost Data Quality

In this article, we explain how AI and domain knowledge combined tackle every aspect of data quality and how OvalEdge provides this holistic solution...

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Improving Data Quality at a Regional Bank | A Case Study

A regional US bank used OvalEdge to tackle data quality issues that were leading to inaccurate liquidity risk and credit risk predictions, and...

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Data Quality Purpose-Built for Banking

For banks, issues with data quality can have dramatic repercussions financially and from a compliance perspective. In this blog, we’ll explore the...

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Data Quality Challenges for Fair Lending Compliance in Banking

Data quality is a major pain-point when it comes to fair lending compliance. Most existing tools are not purpose-built to address this challenge....

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Top 8 Features of a Data Quality Tool

High-quality data is a cornerstone of data governance, but many companies still fail to implement ways to ensure it. The best way to boost data...

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Understanding Data Quality Metrics and Dimensions

Data quality is a cornerstone of data governance and has a huge impact on the effectiveness and accuracy of business decisions. In this blog, we...

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Data Quality Management Framework: How to Build Reliable Data Systems

Data quality management is an essential element of data governance. However, to undertake it effectively, you need a dedicated tool. In this article,...

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Data Governance vs Data Quality: Key Differences & Best Practices

Data quality and data governance are closely aligned but there are some important differences between them. That said, successful data quality...

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Best Practices for Improving Data Quality (Must Read!)

If you’ve read our other data quality blogs in this series, you’ll understand the importance of high-quality data for accurate decision making,...

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OvalEdge recognized as a leader in data governance solutions

SPARK Matrix™: Data Governance Solution, 2025
Final_2025_SPARK Matrix_Data Governance Solutions_QKS GroupOvalEdge 1
Total Economic Impact™ (TEI) Study commissioned by OvalEdge: ROI of 337%

“Reference customers have repeatedly mentioned the great customer service they receive along with the support for their custom requirements, facilitating time to value. OvalEdge fits well with organizations prioritizing business user empowerment within their data governance strategy.”

Named an Overall Leader in Data Catalogs & Metadata Management

“Reference customers have repeatedly mentioned the great customer service they receive along with the support for their custom requirements, facilitating time to value. OvalEdge fits well with organizations prioritizing business user empowerment within their data governance strategy.”

Recognized as a Niche Player in the 2025 Gartner® Magic Quadrant™ for Data and Analytics Governance Platforms

Gartner, Magic Quadrant for Data and Analytics Governance Platforms, January 2025

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