The agentic AI blueprint for data quality management

Deloitte helps translate business and industry requirements into rules. Databricks’ agents infer, version, validate, and explain them using profiling, metadata, and business context.

Agents use current profiles, metadata, and business definitions to develop new rules if needed, and they make updates necessary to implement the rules. Each rule can be tested before enforcement, tracked through revision history, and assessed for performance. When a record fails, the system explains why and identifies downstream processes that may be affected.

Deloitte helps define cleansing policies, confidence thresholds, and stewardship workflows. Databricks’ Agent Bricks standardize data, resolve duplicates, and apply confidence-based corrections. They automate clear fixes, route uncertain cases to stewards, and preserve lineage and audit trails.

Agents can make targeted fixes rather than applying one-size-fits-all cleanups. For example, they compare records to identify duplicates, standardize formats and values, and automatically correct only those cases that need it. Ambiguous cases are routed to a human steward with the evidence needed to make decisions. Those decisions are then used as feedback for future changes, while each change has an audit trail of its source and history.

Deloitte helps set data cleansing policies, confidence thresholds, and stewardship workflows. Databricks standardizes data, resolves duplicates, preserves lineage, and recommends upstream fixes.

From discovery through solution, issues stay visible with their owner, status, priority, and due date captured at each step. Agents use lineage and pipeline observations to trace bad results back to the job, source, or change that caused it. They then route work to the right team, track service targets, and identify repeating patterns so the organization can prevent the next incident instead of continuously fixing the same recurring issue.

Deloitte helps define data-quality index measures and reporting needs. Databricks’ MLflow and Lakehouse Monitoring capture telemetry and surface quality trends through dashboards, APIs, and conversational tools.

Monitoring is continuous instead of limited to fixed intervals and periodic scorecards. Agents combine measurements of metrics like freshness, volume, completeness, reliability, and pipeline performance into a real-time view of data health. They also identify decline patterns, unusual changes, and their effects on critical reports or processes. Teams can view information in dashboards, connect it to applications through APIs, or ask questions in plain language to understand the changes.

Deloitte helps define and establish governance, compliance, and human-approval requirements. Databricks’ Unity Catalog enforces policies through metadata, lineage, classification, and access controls.

Agents can add context to data assets, identify potentially sensitive data, and apply role-based access controls when needed. Corrections or exceptions that may have high visibility or impact can also be routed to humans for approval. The system creates an audit trail for each action, which makes reviews easier and supports regulatory compliance. Thus, teams can automate routine work where appropriate without losing accountability.