Data collection and utilization are growing rapidly, and organizations are increasingly relying on data as they transition into the era of AI. However, many face significant challenges in effectively managing investments across cloud, data, and AI. This is largely due to a lack of visibility across their entire data estate—which is often fragmented across silos, heterogeneous systems, on-premises environments, and the cloud. Concerns about data trustworthiness, AI-readiness, and uncertainty around security and compliance further complicate the ability to drive timely business insights.
Elevating data quality means going beyond merely identifying issues—it's about equipping data stewards, analysts, and engineers with the right tools to proactively improve trust, consistency, and readiness of data for AI, analytics, and operations.
Despite the critical importance of data quality, many organizations struggle to activate the full value of their data estate. Research shows that 75% of companies do not have a formal data quality program. This is alarming, especially as data quality has become a cornerstone of successful AI initiatives. Simply put: your AI is only as good as your data.
In the past, when humans were always in the loop, minor data quality issues could be corrected manually. But in today’s world—where AI interprets not just the structure but the content of the data—any inconsistencies, inaccuracies, or noncompliance in the data can directly lead to flawed insights, unreliable AI outputs, and poor business decisions.
Bad data can make your AI wrong. It can make your BI reports misleading. And it can impact your organization's credibility—as well as your reputation as a data professional. After all, there’s nothing worse than building something that no one trusts or uses.
That’s why defining and deploying a robust data quality framework is more critical now than ever before. Organizations must establish a data quality maturity model, track quality across their data estate, and take continuous improvement and remediation actions.
Key steps to maintaining data quality and ensuring the health of your data estate include:
- Define the scope – Identify which data is needed for specific business use cases.
- Measure quality – Assess whether data meets expected standards.
- Analyze findings – Understand patterns, gaps, and root causes.
- Improve quality – Take corrective actions to meet business needs.
- Control quality – Continuously monitor and govern data to maintain high standards.
To ensure your data is fit for purpose, it's essential to establish strong data quality practices within your organization, supported by the right tools, roles, and governance structures.
Profile your data to understand the distribution
Data profiling is the process of analyzing data to understand its structure, content, and quality. It helps uncover patterns, anomalies, missing values, duplicates, and data types—providing valuable insight into the trustworthiness and usability of data for analytics, decision-making, and quality improvement. By examining datasets for structure, relationships, and inconsistencies, data practitioners can identify potential issues and define validation rules for ongoing data quality assurance.
Microsoft Purview Unified Data Catalog provides an integrated data quality experience that supports profiling as a foundational step. With Purview, users can profile data to understand its distribution, patterns, and data types—helping inform data quality programs and define rules for continuous monitoring and improvement.
Purview's data profiling leverages AI to recommend which columns in a dataset are most critical and should be profiled. Users remain in control and can adjust these recommendations by deselecting suggested columns or adding others. Additionally, all profiling history is preserved, allowing users to compare current profiles with historical patterns to detect changes over time and continuously assess data health.
Define and apply rules to validate your data
Applying rules and constraints is essential to ensure that data conforms to predefined requirements or business logic—such as data type, completeness, uniqueness, and consistency. Data profiling results can be leveraged to define these rules and validate data continuously, helping ensure that it is trustworthy and ready for both business and AI use cases. To achieve this, data quality should be measured across all stages: data at creation, data in motion, and data at rest.
While many CRM and web-based applications perform UI-level validations to check user inputs before submission, a significant amount of poor-quality data still enters systems through bulk upload processes. These low-quality records often bypass front-end checks and propagate downstream through the data supply chain, leading to broader data integrity issues.
In Medallion architecture, you can validate and correct data directly in the pipeline. Bad-quality data detected in the bronze layer can trigger notifications to upstream systems to fix issues at the source.
Purview Unified Catalog Data Quality capability provides a user-friendly UI for managing data quality rules. You can configure rules for any supported data sources in cloud or on-premises or datasets in Fabric Lakehouse's bronze layer, schedule DQ jobs, and send notifications to data engineers and stewards when issues arise. This proactive monitoring ensures data quality is addressed early—before data progresses through the silver and gold layers of your architecture.
Visualizing Data Quality Metrics and Driving Action
Visualizing data quality metrics and trends provides critical insights into the overall health of your data and supports informed, data-driven decision-making.
Microsoft Purview Unified Catalog publishes all metadata—including data quality rules and scores—into Fabric OneLake. Data analysts can link Purview metadata with raw data to generate actionable insights. They can also leverage Fabric AI skills to enhance intelligence and integrate with Data Activator to trigger notifications for upstream data publishers and downstream consumers.
Alerts and notifications can be configured directly in the Purview Unified Catalog. Data stewards can set thresholds for one or many data assets to automatically notify upstream and downstream contacts if those thresholds are breached. Notifications can be directed to specific individuals or groups (e.g., a support team).
These alerts empower data providers to resolve issues at the source, and data engineers can address problems within the bronze layer of their analytical storage—such as in Microsoft Fabric. Additionally, alerts can be used to pause data movement from the bronze to the silver and gold layers, ensuring only high-quality data flows downstream.
Users can configure the storage location in the Purview Data Quality solution to publish failed or error records. This allows data stewards and data engineers to review and fix issues, improving data quality before using it for analytics or as input for ML model training.
Integrated Data Observability with Data Quality Scores
Data observability in Microsoft Purview Unified Catalog offers a comprehensive, bird’s-eye view into the health of the data estate as data flows across various sources. Data stewards, domain experts, and those responsible for data health can monitor their entire data landscape from a single unified interface.
This centralized view provides visibility into data lineage—from source to consumption—and reveals how data assets map to governance domains. It enables users to understand where data originates and terminates, pinpoint data quality issues, and assess the impact on reporting and compliance obligations.
By consolidating metadata into a single, accessible location, users can explore how data quality is evolving, track usage patterns, and understand who is interacting with the data.
With full visibility across the data estate, both central and federated data teams can efficiently identify opportunities to improve metadata quality, clarify ownership, enhance data quality, and optimize data architecture.
Summary
Defining and implementing a data quality framework has become more critical than ever. Organizations must establish a data quality maturity model and continuously monitor the health of their data estate to enable ongoing improvement and remediation.
Microsoft Purview Unified Catalog empowers governance domain owners and data stewards to assess and manage data quality across the ecosystem, driving informed and targeted improvements. In today’s AI-driven world, the trustworthiness of data is directly linked to the effectiveness of AI insights and recommendations. Unreliable data not only weakens AI outcomes but can also diminish trust in the systems themselves and slow their adoption.
Poor data quality or inconsistent data structures can disrupt business operations and impair decision-making. Microsoft Purview addresses these challenges with a no-code/low-code approach to defining data quality rules—including out-of-the-box (OOB) and AI-generated rules. These rules are applied at the column level and rolled up into scores at the data asset, data product, and governance domain levels, offering full visibility into data quality across the enterprise. Purview’s AI-powered data profiling capabilities intelligently recommend which columns to profile, while still allowing human review and refinement. This human-in-the-loop process not only improves the relevance and accuracy of profiling but also feeds back into improving AI model performance over time.
Elevating data quality is more than identifying problems—it's about equipping stewards, analysts, and engineers with the tools to proactively build trust, ensure consistency, and prepare data for AI, analytics, and business success.
Microsoft