Data governance is the set of policies, processes, and accountabilities that ensure data is accurate, accessible, secure, and used appropriately across an organisation. It is, in the words of Gartner, "the specification of decision rights and an accountability framework for ensuring the appropriate behaviour in the valuation, creation, storage, use, archiving and deletion of information."
This definition is important because it frames governance as fundamentally about people and accountability, not technology. A data governance programme without clear ownership, without people empowered to make decisions about data, and without consequences for non-compliance, is a set of documents that no one reads.
The practical components of a data governance programme: a data catalogue that inventories the organisation's data assets, their definitions, their lineage, and their quality status; data quality rules and automated monitoring that alert when data falls below defined thresholds; a data classification scheme that identifies sensitive data and triggers appropriate access controls; a metadata management process that keeps documentation current as data structures evolve; and a data stewardship model that assigns ownership of each domain's data to named individuals accountable for its quality and accessibility.
For organisations subject to India's DPDP Act, financial sector regulations from RBI and SEBI, or international frameworks like GDPR, data governance is also a compliance necessity. A well-implemented governance programme reduces the manual effort of compliance audits significantly because the required documentation, access logs, and consent records are maintained as a byproduct of normal operations.
The ROI of data governance is indirect but substantial: fewer data quality incidents that cause downstream errors, faster time to insight because analysts spend less time cleaning data and resolving ambiguity, reduced regulatory risk, and improved trustworthiness of AI models that are trained on governed data.