Data governance is the set of policies, processes, roles, and standards that ensure data in an organisation is accurate, available, consistent, and secure. For data engineers, governance is not abstract management — it is a set of concrete technical requirements: who can access which tables, how long data is retained, how changes to sensitive columns are tracked, and what happens when a data quality issue is discovered. Modern analytics engineering practices embed governance requirements directly into dbt models, warehouse configurations, and CI/CD pipelines.
The DAMA-DMBOK (Data Management Body of Knowledge) is the most comprehensive data management framework, defining 11 knowledge areas: Data Governance, Data Architecture, Data Modelling, Data Storage, Data Security, Data Integration, Documents and Content, Reference and Master Data, Data Warehousing, Metadata Management, and Data Quality. For data engineers building analytics platforms, the four most operationally relevant areas are Data Quality (defining and measuring quality dimensions), Data Security (access control, encryption, masking), Metadata Management (data lineage, catalogues, business glossaries), and Data Governance (policies, stewardship, accountability).
Analogy🏏Cricket
🏏 Think of it like cricket: OLTP is the IPL's live ticketing counter — it handles thousands of simultaneous seat reservations, each requiring a precise single-seat record update with immediate confirmation. Speed per transaction and data consistency under concurrent updates are everything. OLAP is the IPL's season statistics department — it runs complex analytical queries across every ball bowled in every match of every season to produce the published rankings, economy rates, and historical comparisons. No one books a seat through the statistics department, and no broadcaster calls the ticketing counter for Bumrah's career economy rate. The two workloads demand completely different systems. Just as the ticketing counter is built for speed and correctness on one seat at a time and would buckle if asked to tally a decade of attendance mid-sale, an OLTP row-store excels at single-record writes but chokes on full-table aggregation; and just as the statistics department pores over millions of past deliveries but would be hopeless at booking a live seat under contention, the OLAP columnar engine sweeps billions of rows yet is the wrong tool for a fast single-row update. The physical design of each — row-oriented for the counter, columnar for the stats desk — is what makes it superb at its own job and unfit for the other's.
🏏 Showing the Cricket analogy — a Cricket version isn’t available for this concept yet.