Service / Enterprise Transformation & Trust

Data Governance & Classification

Establish accountable data ownership, classification, lifecycle, quality and access governance across structured and unstructured information.

Business context

What this capability solves

Security, privacy and AI governance depend on knowing what data exists, who owns it, how sensitive it is and where it flows. This service builds the governance foundation required for reliable controls.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Data Ownership

Domain owners, stewards, custodians and decision rights.

Classification

Business-sensitive, personal, regulated and critical-data taxonomy with handling rules.

Inventory & Catalog

Data assets, locations, systems, interfaces and business context.

Lifecycle Governance

Creation, use, sharing, retention, archival and disposal.

Quality & Lineage

Quality rules, provenance, transformations and critical-data lineage.

Access & Sharing

Need-to-know access, approval, external sharing and monitoring requirements.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Business & Governance

Objectives, risk appetite, regulatory/standard requirements, owners and decision rights.

Assessment & Design

Current-state evidence, target controls, architecture, priorities and implementation backlog.

Delivery & Integration

Technical/process implementation, enterprise integration, testing and change enablement.

Operate & Assure

KPIs, control testing, incident/escalation, evidence refresh, management reporting and continuous improvement.

Controls & governance

Risk-based scope and acceptance criteria
Role-based ownership and approvals
Evidence and audit trail
Exception and escalation workflow
Quality review before sign-off
Defined handover and operating procedures

Priority use cases

Data lake/cloud migration
PDP programme
DLP/classification rollout
AI data readiness
Regulatory reporting
Master/critical data governance

Key deliverables

Data governance framework
Classification standard
Owner/steward RACI
Data inventory/catalog design
Handling matrix
Retention mapping
Governance KPIs

Integration considerations

Identity and organization model
Asset/data inventories
ITSM/workflow
SIEM/logging
Document/evidence repositories
GRC and management reporting

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. DiscoverConfirm scope, stakeholders, evidence, dependencies and risk drivers.

2. DesignDefine target operating model, controls, architecture and prioritized roadmap.

3. ImplementDeploy processes/technology, integrate, test and train accountable teams.

4. AssureMeasure outcomes, close gaps, hand over and establish continuous governance.

Outcome and KPI framework

Owned data assetsClassified critical dataOrphan data reductionRetention complianceAccess review completionQuality issue closure