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 quality is with a dedicated tool. In this blog, we’ll run through the key features of a data quality tool so you can find the best fit for you.
In this guide, we break down the top data quality software features you should look for, how these tools support your broader data strategy, and why full-spectrum data quality matters for long-term governance success.
If your data isn’t high quality, it’s practically useless. Poor data quality leads to broken dashboards, misleading analytics, and business decisions that can cost organizations millions.
Most companies deal with two major types of data quality challenges:
1. Source-Level Issues
These occur when data is inaccurate, inconsistent, or poorly understood right at the point of creation.
2. Downstream Issues
When data deteriorates during transformations, migrations, or integrations often due to a lack of visibility, ownership, or monitoring.
A strong data quality tool, especially a full-spectrum solution, addresses both layers. It helps teams validate data at the source while catching and correcting issues downstream.
This is why modern data quality platforms' features are designed to support both business and IT stakeholders.
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A good data strategy shouldn’t include assumptions that data manipulation is necessary. The best outcome is to ensure data quality at the source, but this isn’t always possible, and can sometimes be overly cumbersome.
That’s why you need a tool that provides both source-level and downstream capabilities.
Ensuring data quality involves navigating a complex ecosystem, where data is moved from one place to another and between numerous users and departments.
A data quality tool will help support various aspects of data quality in an organization, but to succeed, it must lead to three core outcomes:
In this blog, we'll walk you through the most critical features of a data governance tool that can tackle source-level and downstream issues and demonstrate how these features support the core outcomes we've highlighted above.
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These are the features every modern organization should expect, especially when evaluating data quality software features to support enterprise-wide governance.
A good data quality tool ensures that when an issue is reported, it automatically reaches the right owner, not just the data team. These workflows must be contextual, meaning they consider:
This ensures faster resolution and real accountability.
Most business users don't naturally understand what "bad data" actually means.
A strong tool educates them on critical data quality dimensions such as:
This improves both issue reporting and decision-making. A good data quality tool will help business users understand the various dimensions of data quality so they can make better judgments on how and when to escalate issues.
A data quality tool must enable collaboration between the technical and non-technical stakeholders within the data pipeline and create a workflow between the various business entities to resolve specific issues.
It should enable business users and data operations teams to collaborate, which works both ways.
A powerful collaborative analytics platform or data quality system should eliminate silos. Business users and data operations teams must be able to:
This cross-team collaboration is essential for preventing recurring issues and ensuring governance maturity.
When either side finds an issue, they can work together to fix it. As such, the workflow engine that enables this collaboration overlaps departments, roles, and responsibilities.
For Data Operations teams, proactive alerting is essential. They need to have proactive alerts in place so they can be notified immediately when a problem arises.
Proactive alerting features minimize the impact of a data quality issue because problems can be dealt with quickly before they infiltrate further into a company's data ecosystem.
Data Operations teams must have the capacity to investigate alerts and reports by looking into data pipelines, lineage, and more.
A modern data quality tool should include:
These investigative features allow data teams to find the source of an issue instead of patching symptoms.
Data profiling features enable users to ingest and understand the data in their ecosystem quickly and at scale.
Data profiling helps users quickly understand:
Profiling is one of the most powerful data quality platform features because it enables scale, especially when onboarding new sources.
With anomaly detection, technical teams can utilize AI/ML-enabled features to identify any data anomalies proactively.
This out-of-the-box solution uses intelligent automation to search for discrepancies in the data.
Manual checks can’t keep up with modern data velocity.
AI/ML-powered anomaly detection:
This is critical for enterprise-level data governance.
From a business perspective, a data quality tool must enable organizations to correct issues with the data once they have been surfaced.
Data cleansing, or Master Data Management (MDM), provides users with the means to stay on top of data as it's ingested and fix errors with issues like duplication.
The best tools offer AI-based data cleansing tools that identify where there are errors and correct them.
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OvalEdge is a comprehensive data governance platform. Within OvalEdge is a set of dedicated data quality improvement tools that enable you to measure, assess, and evaluate the quality of your data and take proactive steps to fix any data quality issues on an ongoing basis.
OvalEdge provides all the capabilities, both at the source level and downstream, that you need to ensure high-quality data.
A data quality tool is software that helps organizations measure, monitor, improve, and maintain clean, accurate, and reliable data across systems.
Key features include contextual workflows, data profiling, anomaly detection, proactive alerts, collaborative capabilities, lineage tracing, and cleansing tools.
Look for scalability, ease of use, workflow automation, AI-enabled detection, integrations, governance features, and support for both source-level and downstream issues.
Clean data leads to accurate analytics and reporting, reducing guesswork and enabling confident, data-driven decisions.
Profiling analyzes data to understand its condition. Cleansing corrects issues found during profiling or monitoring.
Utilize this free self-service tool to identify root causes of poor data quality and learn what needs fixing. Download now.