By Guest Contributor
On March 22, 2026
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India's digital public infrastructure achievements — Aadhaar, UPI, DigiLocker, ONDC — are rightly celebrated as models for the world. But beneath the headline success stories lies a more complex reality: the data flows that make these systems work are governed by a patchwork of sectoral rules, voluntary commitments, and technical standards that were not designed to work together.
As India scales its digital economy toward a $1 trillion target, and as AI systems increasingly depend on large-scale data integration across institutional boundaries, data governance has moved from a compliance concern to a strategic infrastructure question.
The instinct in many organisations is to treat data governance as documentation — policies, registers, and procedures that exist to satisfy auditors. This instinct is wrong, and in an AI-first economy, it is expensively wrong.
Data governance is the technical and organisational infrastructure that makes data assets trustworthy, shareable, and actionable. It determines:
Without data governance infrastructure, organisations cannot answer the questions that regulators, customers, and enterprise AI systems will increasingly ask.
India's Digital Personal Data Protection Act has created the most significant forcing function for enterprise data governance in the country's history. Key obligations that translate directly into technical infrastructure requirements:
| DPDP Obligation | Technical Infrastructure Required | Maturity Level in Most Enterprises | |---|---|---| | Data minimisation | Data classification system, automated retention enforcement | Low | | Purpose limitation | Data lineage tracking, consent-to-purpose mapping | Very low | | Storage limitation | Automated deletion workflows, archive management | Low | | Consent management | Consent management platform, audit trail | Emerging | | Data principal rights | Data discovery + subject access request automation | Low | | Breach notification | Incident detection + notification workflow (72h) | Moderate |
The November 2026 deadline for enhanced consent manager obligations is creating urgency for organisations that have not yet built the foundational infrastructure.
Large enterprises can fund dedicated data governance programmes. The more pressing challenge is enabling the thousands of Indian startups and mid-market companies that power the digital economy to implement governance without the resources of a bank or telco.
A pragmatic minimum viable governance stack:
1. Data Classification (Week 1–4): Tag every data asset by sensitivity category (public, internal, confidential, restricted). This is the prerequisite for everything else. Use automated scanning tools rather than manual surveys.
2. Data Dictionary (Month 2): A shared, searchable record of what data assets exist, where they live, who owns them, and what they are used for. A well-maintained Notion database is better than an unmaintained enterprise MDM tool.
3. Consent Workflow (Month 3): For any B2C product collecting personal data, implement a consent management platform that records consent purpose, version, and timestamp. Open-source options (Consenty, Klaro) exist.
4. Retention Automation (Month 4): Configure automated deletion schedules for personal data categories. Cloud storage lifecycle policies are the minimum viable implementation.
5. Breach Response Playbook (Month 5): A documented, tested workflow for detecting, containing, and notifying a data breach within 72 hours. Test it twice a year.
Organisations that build data governance infrastructure proactively — before regulatory pressure and before AI scale — create durable competitive advantages:
India's digital economy can lead the world not just in scale, but in trustworthiness. Data governance is the infrastructure that makes trustworthiness measurable and demonstrable.