Data Quality Insights and Guidance from Qualytics

Data Quality vs Data Control: Why AI Demands Controls

AI removes the human safety net that contained bad data. The data control layer validates data at the moment it's acted on.

Gorkem Sevinc
CEO & Co-Founder
May 19, 2026
7 min read

The Data Quality Maturity Model: Moving from Incident Response to Proactive Data Trust

A framework outlining how organizations evolve data quality from reactive detection to proactive, governed control across increasingly complex data environments.

Click to View All resources from this category

Free Tools

Data Quality Maturity Assessment

Score your data quality program across seven dimensions. See your maturity level, where gaps exist, and whether you're ready for AI.

.png)

Cost of Bad Data Calculator

Take 2 minutes to estimate what poor data quality is costing your organization, based on your industry, team, and current approach.

Reports

Data Quality vs. Data Observability

This guide demystifies the Data Observability and Data Quality disciplines so you can determine which approach will set your organization up for lasting success.

Guides

Data Quality Automation: How Modern Platforms Validate at Scale

Learn how automated data quality platforms infer validation rules, detect anomalies, and support remediation at scale.

From Reactive to Reliable: A Guide to Modern Data Quality Frameworks

Learn the six core components of a data quality framework and how they work together to ensure reliable data.

What to Look for in Data Quality Software: A Guide to Features

Learn which data quality software features help teams build and sustain scalable, automated quality programs.

Customer Stories

Catching Financial Data Issues Before They Impact Quarterly Close: A Global Alternative Asset Management Firm + Qualytics

Automated reconciliation at scale, reducing manual effort and accelerating financial data confidence.

.png)

Powering Proprietary Credit Data at Scale: Octus + Qualytics

Scaled trusted data operations while reducing QA costs and empowering domain experts.

Catching Hidden Data Quality Errors Before They Cost Millions: MAPFRE USA + Qualytics

Shifted from reactive cleanup to proactive controls, preventing costly downstream data errors.

The Latest Product News

How to Validate Semi-Structured Data (Arrays, Structs, and Nested JSON) Without Flattening

Qualytics introduces native validation for nested JSON, arrays, and structs, enabling comprehensive data quality checks without costly flattening pipelines.

Trusted AI and Analytics at Scale with Databricks and Qualytics

AI-augmented data quality on Databricks, delivering proactive profiling, scalable rules, continuous monitoring, and governed remediation for trusted analytics and AI.

.png)

What We Delivered in 2025 to Empower Data Quality Teams in 2026

A look at the ten Qualytics features shipped in 2025, built to help data quality teams operate at scale and support AI, governance, and analytics.