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Data Quality and Governance Systems

Governance that runs in the systems, not in the slide deck.

Governance fails when it is a committee and a policy document. It works when ownership, definitions, quality rules and access controls are built into the platforms people already use, and reported like any other operational metric.

We implement governance aligned with DAMA-DMBOK and, in Saudi Arabia, the NDMO data management standards, scaled to what your organisation can actually operate.

The problem

What usually gets in the way

  • Nobody can say who owns a critical data element or which source is correct.
  • Quality issues are found by customers, regulators or AI models, not by controls.
  • Regulatory requirements (PDPL, NDMO, sector rules) are tracked in spreadsheets.
  • Governance programmes produce documents but no change in daily work.

What we deliver

Capabilities

01

Operating model

Domains, owners and stewards with clear decision rights and a light cadence.

02

Critical data elements

Identify the data that matters most and define it once in a business glossary.

03

Quality rules and monitoring

Automated checks at source and in pipelines, with scorecards and issue routing.

04

Catalogue and lineage

Find data, see where it came from and what depends on it.

05

Classification and access

Classify sensitive data and enforce access and masking in the platform.

06

Compliance evidence

Map controls to NDMO, PDPL and sector requirements and produce evidence continuously.

Delivered by

Services involved

The service lines combined to deliver this solution.

How it fits together

Reference architecture

A vendor-neutral outline. Components are selected for your workloads, skills and regulatory context.

  1. People and processDomain ownershipStewardship workflowsIssue management
  2. Governance toolsCatalogue and glossaryLineagePolicy management
  3. Controls in the platformQuality checks in pipelinesClassification and maskingAccess policies
  4. ReportingQuality scorecardsCompliance statusMaturity tracking

How we engage

Start small, prove value, scale

  1. 01Assess

    3–5 weeks

    Maturity baseline against DAMA-DMBOK or NDMO, and a prioritised gap list.

    • Maturity assessment
    • Critical data elements
    • Prioritised roadmap

  2. 02Pilot

    8–12 weeks

    Stand up ownership, quality monitoring and catalogue for one or two domains.

    • Working stewardship
    • Live quality scorecard
    • Catalogue coverage

  3. 03Scale

    Ongoing

    Roll out domain by domain with reusable rules and templates.

    • Enterprise coverage
    • Compliance evidence
    • Maturity improvement

Outcomes

What we measure

  • Quality score of critical data elements
  • Issues detected before reaching users
  • Catalogue and lineage coverage
  • Time to answer audit requests
  • Maturity level by domain

Where it applies

Example use cases

  • Government

    NDMO compliance programme with domain stewardship.

  • Banking

    Regulatory reporting data quality and lineage.

  • Telecommunications

    Customer master data quality for billing and churn models.

  • Healthcare

    Patient data classification and access control.

Accelerators

Reusable assets we are building

Delivered today as services. These accelerators are being developed from that work to shorten future engagements.

In development

NDMO and DAMA control map

Requirements mapped to practical controls and evidence.

In development

Data quality rule library

Reusable rule patterns for common domains such as customer, product and finance.

Technology

Platforms we design around

Vendor-neutral: examples of platforms commonly used for this solution. Names identify technologies only and do not imply a partnership.

  • Microsoft Purview
  • Collibra
  • Alation
  • Informatica
  • Databricks Unity Catalog
  • Ataccama
  • Monte Carlo
  • Great Expectations
See the full technology ecosystem →

Insights

Further reading

Implementing NDMO With DAMA-DMBOK

The NDMO standards tell you what must be true. DAMA-DMBOK tells you how to make it true. Entities that confuse the two end up with 191 documents and very little managed data.

Read →

Governance Without Bureaucracy

Governance that lives in committees grows with the number of meetings. Governance that lives in pipelines grows with the data. Most programs fail because they chose the first and expected the results of the second.

Read →

A Practical Data Quality Framework

Most data quality programs measure everything and fix little. Start from the few dozen data elements that decisions depend on, write rules against them, and run quality as a service with thresholds, owners and resolution times.

Read →

Data Lineage for Trust and AI

Lineage drawn by hand is out of date by the next release. Lineage captured from the systems that move data becomes the evidence layer that supervisors, auditors and AI governance all depend on.

Read →

Talk to us about Data Quality and Governance Systems

Start with a short assessment: we will rank the opportunities, check readiness and agree how success will be measured.

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