Insights · Topic
Governance and DPDP
Ownership, quality, access, and India-ready data governance controls. 6 guides, plus related customer work and papers.
About this topic
Governance here means controls that run inside the data platform rather than policies that sit beside it. The guides describe how ownership, quality rules, access reviews, lineage, and retention become working parts of a system, and what records an auditor or regulator can expect to see.
India’s Digital Personal Data Protection Act shapes much of this material. The DPDP operating-model guide connects notices, consent, rights requests, retention, and deletion to deployed controls, and the customer work listed below shows where the same approach has been applied to master data and reporting.
Technical guides
Every guide in this topic, including the articles featured on the insights index.
Monitoring a data pipeline from source to action
Monitor a data pipeline end to end: five signals between source and decision, each with a named owner and a response defined in advance.
Why does master data keep breaking?
Master data stays usable through written matching rules, a steward-owned exception queue, and measures for duplicate rate and backlog age.
How do you measure data quality in production?
Freshness, completeness, validity, and consistency: where data quality tests run and who gets paged when production data fails.
Data governance in production: ownership, quality, access, and audit records
Build a data governance operating model with critical-data scope, named owners, quality controls, access records, and an exception process.
Prepare data systems for India's DPDP requirements
Indian data systems become DPDP ready when every personal-data record carries its purpose, owner, and retention rule, consent is captured and enforced downstream, data principal requests run on a tracked workflow, and the whole state is auditable. The Digital Personal Data Protection framework makes the data principal's rights operational duties, so readiness is a systems program, not a policy document.
Data engineering: from source system to operating decision
Understand the responsibilities, design choices, controls, and measures behind reliable data paths.
Customer work and research
Customer work and technical guides on this topic.
Other topics
Continue through the library by problem area.