Turn scattered data into a trusted, governed asset.
A DAMA-DMBOK program engineered for Indonesia’s reality — where UU PDP has been in full force since 17 October 2024, and data management is now a legal obligation, not a preference.
11
DAMA-DMBOK knowledge areas
5
Maturity levels
5
Phase delivery model
The challenge
Managing data in the modern organization
Organizations run on data — for operations, decisions, and innovation. In practice, that data is scattered across systems, inconsistent, and hard to trace.
No clear data ownership
No one owns data across teams — accountability falls through the gaps between business units.
Definitions differ everywhere
The same term means different things in each business unit, so numbers never reconcile.
Inconsistent quality
Data quality varies by system, and no one can say which copy is authoritative.
No confidence it's current
Hard to be sure the data in use is correct and up to date.
Regulatory exposure
Using data in ways that don't comply with regulation — a legal risk, not just an operational one.
Data governance
Why it’s a priority now
Data governance is the framework that ensures data is managed clearly, consistently, and accountably — ownership, definitions, quality, access control, and how data is used across every business process and system.
In Indonesia, UU PDP has been in full force since 17 October 2024. Managing data is now not only a business need but a legally enforceable obligation.
Regulation that is active and enforceable
UU PDP has been in full force since 17 October 2024. Organizations now have a legal obligation to manage personal data clearly, accountably, and auditably. Non-compliance carries legal sanction and reputational damage.
Rising data-environment complexity
Modern architectures span cloud, distributed systems, and third-party integrations. Without clear governance, data becomes hard to control and hard to trust.
Growing business dependence on data
Data now drives decisions, operational efficiency, and AI- and analytics-based products. Its quality and consistency directly shape business outcomes.
Real operational and business risk
Poorly governed data leads to wrong decisions, operational inefficiency, and rising compliance and security risk.
The framework
The 11 knowledge areas of DAMA-DMBOK
Every Zentara program is built on the international data-management body of knowledge — so nothing gets governed in isolation.
Data Governance
Directs and controls all data-management activity.
Data Architecture
Blueprints data management aligned to business strategy.
Data Modeling & Design
Designs data models that represent business needs.
Data Storage & Operations
Manages storage, infrastructure, and database operations.
Data Security
Protects privacy, confidentiality, and access control.
Data Integration & Interoperability
Connects, moves, and aligns data across systems.
Document & Content Management
Manages the lifecycle of unstructured data and content.
Reference & Master Data
Keeps core data consistent across systems.
Data Warehousing & BI
Provides data for analytics, reporting, and decisions.
Metadata Management
Manages metadata so data is findable and understood.
Data Quality
Measures, monitors, and continuously improves quality.
Zentara's approach
Six dimensions in every program
We apply the same six dimensions to every Data Governance engagement — the difference between a policy binder and a program that runs.
Roles & Responsibilities
Defines who owns data, who stewards it, and who decides.
Organization & Culture
Drives cross-functional collaboration and a consistent data-management culture.
Activities
Builds clear processes to manage quality, access, and the data lifecycle.
Techniques
Applies global best practice in a way that's practical to implement.
Tools & Technology
Implements data catalog, quality monitoring, and access control.
Key Outcomes
Produces policy, standards, quality metrics, and periodic reporting.
Where you stand
Five levels of maturity
We score your baseline in Phase 1 and re-evaluate periodically — progress is a number, not a narrative.
Self-check
Where does your data governance stand?
Six questions, mapped to the five-level model above. It's indicative — the formal, evidence-based score comes from Phase 1 — but it's an honest first read.
Ownership
Who owns your data?
The engagement
Five phases. One governed program.
Every phase ships a named output — from the maturity score to the audit reports. Scope follows evidence, not assumption.
Assessment & Discovery
Understand the current state, the gaps, and the key risks.
- Data-governance maturity assessment (people, process, technology, policy)
- Data-risk identification and remediation priorities
- Data asset, system, and data-flow mapping
- Key-stakeholder discussions
- Gap analysis against UU PDP and related regulation
Output — Current-state report, risk register, gap analysis, maturity score
Strategy & Roadmap
Define the target state and the implementation plan.
- Data-governance vision and objectives
- Operating-model and governance-structure design
- Phased implementation roadmap
- Business case and value justification
Output — Strategy document, implementation roadmap, operating-model blueprint, business case
Framework & Policy Design
Build the foundation of policy and standards.
- Data-governance policy (ownership, access, quality, lifecycle, privacy)
- Data standards (classification, definition, metadata, retention)
- Roles & responsibilities (RACI)
- Data-quality framework and UU PDP-aligned classification
Output — Policy documents, data standards & guidelines, RACI matrix, classification & quality framework
Implementation & Technology Enablement
Deploy the framework and enabling technology.
- Stand up the Data Governance Council
- Implement data catalog and metadata management
- Implement data-quality and access controls
- Training and organizational change management
Output — Implementation platform, training program, council charter, implementation playbook
Monitoring & Continuous Improvement
Ensure sustainability and compliance.
- Data-governance KPI monitoring
- UU PDP and internal-policy compliance audits
- Periodic maturity re-evaluation
- Continuous refinement of policy and process
Output — Governance reports, audit reports, maturity updates, continuous-improvement plan
Start with an assessment
Score your data-governance maturity — and your UU PDP gap
Phase 1 gives you a current-state report, a risk register, a gap analysis against UU PDP, and a maturity score. Not ready to engage? Take the two-minute self-check first.
The operating model
Who governs the data
The operating model is built from Zentara's implementation experience and global best practice — a Data Governance Council that directs how organizational data is used, protected, and managed.
Council structure
Executive Council
Sets strategic direction and serves as the highest escalation path.
Enterprise Data Governance Oversight
Oversees the program and ensures alignment across the organization.
Data Governance Council (DGC)
Leads policy development, priorities, and governance initiatives.
Data Governance Operations (DGO)
Runs daily operations and coordination across data domains.
Strategic meeting
An annual forum to set strategic direction and top priorities.
Steering meeting
Operational sessions, held as needed to work through specific issues.
The layered policy & data-standards hierarchy
Data Governance Policy
The master document that sets the organization's principles and mandate.
Data Control Standards
Govern access, use, and movement of data.
Data Design Management Standards
Govern architecture, modeling, and data design.
Data Operations Management Standards
Govern storage, backup, recovery, and retention.
Data Quality Management Standards
Govern measurement and improvement of data quality.
Why Zentara
Seven reasons the program holds
Proven track record
We've run data-governance programs for large organizations like MIND ID — with measurable gains in compliance, role clarity, and data quality.
A strong cybersecurity foundation
We combine data governance with cybersecurity, so data is not only organized but protected from leakage and misuse.
Local regulatory expertise
We understand Indonesian compliance — UU PDP and OJK rules — so the framework we build is directly relevant to regulators and audits.
Internationally certified team
Delivered by professionals holding CDMP, CISSP, CISA, and ISO 27001 — international best practice, applied locally.
A proven methodology
Our methodology follows DAMA-DMBOK, COBIT, and ISO 38505 — made practical and focused on business outcomes.
End-to-end service
We stay from assessment through continuous monitoring, so the program runs consistently from day one into operation.
Technology-agnostic
Experienced across Collibra, Microsoft Purview, Informatica, and Atlan. We choose to fit your needs, never locking you to one vendor.
Track record
Delivered end-to-end for MIND ID, Indonesia's state mining holding — signed contract, full program through implementation.
Resources
The playbook and the deck
The full program in writing. These documents are in Bahasa Indonesia — written for local teams.
FAQ
Common questions
Straight answers. If yours isn't here, ask us directly.
Data governance is the framework that ensures an organization's data is managed clearly, consistently, and accountably — ownership, definitions, quality, access control, and how data is used across systems. In Indonesia it became urgent when UU PDP entered full force on 17 October 2024: managing data is now a legal obligation, not just a business preference, and non-compliance carries legal and reputational consequences.
Trusted data. Proven compliance.
Start where every program starts — a scored maturity baseline and a UU PDP gap analysis. Then build the governed asset your board and your regulator can both rely on.