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Data & Analytics

Data governance

Governance before the deadline, not after the penalty

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The Engagement

Data governance

Three situations indicate that data governance advisory is required. Each one carries a deadline, a regulatory exposure, or both.

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Methodology

Our Approach

A four-phase advisory rhythm, assess, design, advise, support, repeated across every engagement.

  1. Data landscape assessment

    Inventory of every data source the organization relies on: ERP, CRM, HRIS, financial systems, operational databases, cloud storage, email archives, and any third-party data feeds. For each source: what personal data it contains, where it is stored, who accesses it, and under what authority. The output is a data register that maps the organization's actual data footprint against PDPL requirements. Gaps surface here, not later.

  2. Ownership and classification model

    Assignment of a data owner for every category of data in the register. Definition of the classification scheme (public, internal, confidential, restricted, personal, sensitive personal). Mapping of each classification level to retention, access, and security requirements. The ownership model is what turns governance from a document into an operational reality: a named person is accountable for every data category.

  3. Policy and control framework

    Drafting of the governance policies the organization must have: data processing policies, data retention and disposal policies, data quality standards, data subject rights procedures (access, correction, erasure, portability), breach notification procedures, and cross-border transfer controls. Each policy is aligned with the specific articles of Federal Decree-Law No. 45 of 2021. Controls are specified at the operational level: who does what, when, and how compliance is evidenced.

  4. Readiness validation and gap remediation plan

    The governance framework is tested against a compliance checklist derived from the PDPL. Remaining gaps are documented with remediation actions, owners, and deadlines. The organization receives a readiness assessment that can be presented to regulators, auditors, or the board. The assessment is honest: it shows where the organization stands and what remains.

Strategic Outcomes

What success looks like

A data register that maps every data source, every category of personal data, every processing activity, and the lawful basis for each, aligned with Federal Decree-Law No. 45 of 2021.
A data ownership model that assigns a named owner to every category of data, with defined responsibilities for quality, access, retention, and security.
A classification scheme (public through sensitive personal) with retention, access, and security requirements mapped to each level.
Governance policies covering data processing, retention, disposal, quality, subject rights, breach notification, and cross-border transfer. Each policy references the specific PDPL articles it addresses.
A readiness assessment with a scored compliance position, identified gaps, remediation actions, named owners, and deadlines.
Documentation at a level of detail that stands up in a regulatory inquiry, an external audit, or a board-level governance review.
A data register that maps every data source, every category of personal data, every processing activity, and the lawful basis for each, aligned with Federal Decree-Law No. 45 of 2021.
A data ownership model that assigns a named owner to every category of data, with defined responsibilities for quality, access, retention, and security.
A classification scheme (public through sensitive personal) with retention, access, and security requirements mapped to each level.
Governance policies covering data processing, retention, disposal, quality, subject rights, breach notification, and cross-border transfer. Each policy references the specific PDPL articles it addresses.
A readiness assessment with a scored compliance position, identified gaps, remediation actions, named owners, and deadlines.
Documentation at a level of detail that stands up in a regulatory inquiry, an external audit, or a board-level governance review.
Who This Is For

Built for these teams

Compliance officers and DPOs responsible for PDPL readiness who need a governance framework before the January 2027 enforcement date.
CIOs and technology directors managing data platforms where governance, ownership, and quality standards have not been formalized.
CFOs and audit committee members who need assurance that the organization's data practices are documented, auditable, and defensible.
Organizations reporting to the CBUAE that face sector-specific data governance requirements in addition to the PDPL.
Entities preparing for AI adoption that need data governance, quality, and lineage documentation in place before AI projects begin.
Government entities building data governance frameworks aligned with TDRA guidelines and federal digital strategy requirements.
Compliance officers and DPOs responsible for PDPL readiness who need a governance framework before the January 2027 enforcement date.
CIOs and technology directors managing data platforms where governance, ownership, and quality standards have not been formalized.
CFOs and audit committee members who need assurance that the organization's data practices are documented, auditable, and defensible.
Organizations reporting to the CBUAE that face sector-specific data governance requirements in addition to the PDPL.
Entities preparing for AI adoption that need data governance, quality, and lineage documentation in place before AI projects begin.
Government entities building data governance frameworks aligned with TDRA guidelines and federal digital strategy requirements.
Common questions

Frequently asked

Procurement-grade answers to the questions counsel and CIOs ask most.

  • Data governance is the formal accountability structure around how an organization captures, stores, uses, shares, and retires data, with named owners for every critical dataset. It is required in the UAE because the Personal Data Protection Law (Federal Decree-Law 45/2021) demands documented data flows and legal basis for processing, sector regulators (CBUAE, DHA, MOHAP, ADGM, DIFC) audit data handling, and large enterprise contracts and government tenders increasingly require evidence of governance maturity.

  • PDPL compliance requires three things data governance delivers natively: a documented inventory of personal-data processing activities, a defined legal basis per processing activity, and an operational way for data subjects to exercise rights (access, correction, deletion). Bahgat Expert maps each PDPL article to the corresponding governance control so the framework is audit-ready when the UAE Data Office or a sector regulator asks.

  • A governance committee with named members and reporting line to the board, a data catalogue listing every critical dataset and its owner, a classification scheme (public, internal, confidential, restricted), a quality framework with metrics and thresholds, a lineage layer showing how data flows from source to use, a privacy register aligned with PDPL, and an exception process for cases the framework does not anticipate.

  • A Chief Data Officer or equivalent, reporting to the CIO or directly to the CEO depending on data maturity. Below the CDO sit data stewards (business-line representatives accountable for the quality of their domain's data) and a data governance committee that approves classification changes, new processing activities, and material exceptions. Without a named owner above the line-of-business level, data governance reverts to a project rather than an operating function.

  • On three levels. The data catalogue is reviewed quarterly to confirm coverage and owner assignment. Sample processing activities are walked through annually to verify the documented controls operate in practice. Trigger-based reviews follow any material data incident, regulatory inquiry, or change in the data environment. Bahgat Expert designs the audit cadence to match the regulator's expectations for the client's sector and the organization's risk appetite.

Data Engagement

Discuss data governance

Discuss governance, dashboards, and analytics frameworks built for the boardroom rather than the data team.

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