Ungoverned data is not a technical problem.
It is a commercial and regulatory liability.

We design and implement data governance frameworks — ownership structures, policy suites, access controls, lineage documentation and compliance programmes — that turn ungoverned data assets into trusted, accountable commercial infrastructure.

For pharmaceutical organisations subject to MHRA data integrity requirements. For healthcare providers needing to demonstrate GDPR accountability and NHS Digital governance standards. For any growth-stage business that knows its data is being used in decisions but has no governance structure to ensure it is being used correctly or compliantly.

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Governance is not a compliance exercise. It is the decision about who is responsible for your data — and what happens when something goes wrong.

Most organisations treat data governance as a documentation project — a set of policies written to satisfy an auditor and filed in a folder nobody reads. Effective governance is an operational programme: clear ownership structures that survive personnel changes, policies that are implemented rather than documented, access controls that are enforced rather than described, and a compliance monitoring process that identifies drift before a regulator does. We build governance that works in practice — not just on paper.

83%

Of organisations cannot demonstrate data ownership for all their regulated datasets when asked by a regulator — not because the data is ungoverned, but because ownership was never formally assigned.

More expensive to remediate data governance failures reactively — after a regulatory finding, data breach, or client due diligence challenge — than to implement a proportionate governance framework proactively.

60%

Of GDPR investigations involve a data governance failure that predated the incident that triggered the investigation — typically undocumented processing, unassigned data ownership, or an absent retention policy.

Four gaps. One moment when they all become visible.

These are the governance failures that remain invisible — until a regulator inspects, an investor conducts due diligence, a data breach occurs, or a key person leaves and their data knowledge leaves with them.

No Clarity on Who Owns What Data

Every dataset your organisation generates has a commercial or regulatory purpose — and should have an accountable owner. In practice, data ownership is usually either absent, ambiguous, or assigned informally in ways that do not survive team changes. When ownership is unclear, data quality degrades without accountability, regulatory requests cannot be fulfilled, and errors are nobody’s specific responsibility to correct.

GDPR Accountability Cannot Be Demonstrated

GDPR requires not just compliance, but demonstrable compliance — an organisation that can show the regulator, on demand, what personal data it processes, on what legal basis, for how long, and with which third parties. Most organisations cannot produce this picture in hours. Many cannot produce it at all without a multi-week data collection exercise that is itself evidence of a governance failure.

Data Retained Beyond Its Lawful Basis

GDPR’s storage limitation principle requires that personal data is kept only as long as necessary for the purpose for which it was collected — and deleted thereafter. Most organisations have no systematic retention and deletion programme. Data accumulates indefinitely. Old customer records, historical employee data, obsolete clinical records — all representing regulatory exposure without any corresponding commercial value. The risk compounds silently, every month, until someone asks.

Regulatory Reviews Discovering Governance Gaps Under Pressure

The CQC inspection scheduled for next month. The MHRA GDP visit. The NHS procurement due diligence. The investor data room for the Series A. These are the moments when governance gaps — invisible in normal operations — become urgently visible. An organisation that attemptsto implement governance under time pressure, in response to a scheduled inspection, will produce documentation that satisfies nobody and implements nothing.

Five governance domains.One accountable data estate.

These are not sequential phases — they are interlocking governance disciplines that together create an accountable, compliant and operationally sound data organisation. We design and implement them as a cohesive programme, not as isolated policy documents.

Data Ownership & Stewardship

Every dataset your organisation generates has a commercial or regulatory purpose — and should have an accountable owner: someone who is responsible for its quality, its appropriate use, its regulatory compliance, and its lifecycle from collection to deletion. We design and implement data ownership structures — business owners for accountability, data stewards for operational management — that survive personnel changes and create genuine accountability rather than nominal assignment.

Data ownership register — every dataset, its owner, its steward
Stewardship role definitions and operating model
RACI matrix for data decisions and governance activities
Escalation framework — what happens when quality or compliance issues arise
Data governance committee structure and meeting cadence

Data Policy Framework

Governance policies are the documented standards every data decision is measured against. Without them, governance is a collection of individual habits that change every time a team member leaves. We design and implement a proportionate policy framework — covering data management, classification, retention, acceptable use, and third-party sharing — that is specific enough to be enforceable and accessible enough to be followed by the people it governs.

Data management policy suite — sector-specific and proportionate
Data classification policy — sensitivity tiers and handling requirements
Data retention and deletion policy with automated schedule
Acceptable use policy — what staff can and cannot do with data
Third-party data sharing policy and DPA framework

Data Access & Security Governance

Data access governance defines who can see what data, under what conditions, with what audit trail, and subject to what review. It is the intersection of data governance and information security — and one of the most commonly ungoverned areas in organisations with no formal governance programme. We design access governance frameworks that implement the principle of least privilege: every person has access to the data they need for their role and no more, with every access decision documented and periodically reviewed.

Data access rights matrix — role to data asset mapping
Access request and approval workflow design and implementation
Privileged access management for sensitive and regulated datasets
Periodic access review programme — quarterly revalidation
Access audit log design and monitoring implementation

Data Lineage & Catalogue

Data lineage documents where data comes from, where it goes, what transformations it undergoes, and how it is used in business processes and regulatory reporting. It is the audit trail that allows an organisation to answer “how was this figure calculated?” — critical for regulatory reporting, AI model governance, and confident business decision-making. Combined with a data catalogue, lineage documentation creates organisational visibility into what data exists and how it moves.

Data catalogue implementation — every dataset, its definition, its owner
Data flow documentation — source to destination lineage mapping
Transformation lineage — what business logic is applied at each stage
Data impact analysis capability — what changes if this dataset changes
Business glossary — agreed definitions for every key business term

Regulatory Compliance Governance

Regulatory compliance governance is the ongoing programme that ensures data practices comply with applicable regulations as those regulations evolve and as the organisation’s data landscape changes. GDPR and UK GDPR for all organisations. NHS Digital standards and DSPT for healthcare. MHRA data integrity requirements and GDP for pharmaceutical. We design governance frameworks that are structured around the specific regulatory environment of your organisation — not a generic template applied regardless of context.

Regulatory compliance framework — mapped to your specific regulatory environment
GDPR Article 30 register — maintained and current
DSPT evidence management system — live status, not quarterly snapshot
MHRA data integrity governance for pharmaceutical environments
Compliance monitoring programme — continuous, not point-in-time

What makes governance that works different from governance that documents

Proportionate Framework Design

We do not apply a one-size template to governance. The framework we design is scaled to your organisation’s size, sector and actual risk profile. A growth-stage business with 40 staff needs different governance architecture from a pharmaceutical distributor with 200 — and both need frameworks they can actually operate, not aspirational structures that exceed their capacity to maintain.

Sector-Specific Regulatory Expertise

We understand GDPR as it is enforced in the UK — by the ICO, in practice, across healthcare and commercial environments. We understand MHRA GDP data integrity requirements as they are inspected. We understand DSPT as it is assessed. We design governance to satisfy the actual regulatory standard — not a generic interpretation of it.

Operational Embedding, Not Just Documentation

We do not consider a governance engagement complete when the documentation is written. We verify that every governance decision is implemented: policies are signed, access controls are configured, training is completed, ownership is confirmed. The difference between a governance framework on paper and governance that holds under inspection is implementation — and we deliver both.

Connected to the Full Data Programme

Data governance does not exist in isolation. The governance framework we design is connected to the data infrastructure it governs, the analytics layer it enables, and the AI capabilities it makes possible. We design every governance engagement in the context of the complete Pillar 02 data programme — so governance enables everything downstream rather than constraining it.

Maintained, Not Static

We design ongoing governance maintenance into every engagement — a defined monitoring and review programme that keeps the framework current as regulations evolve, as the data landscape changes, and as the organisation grows. Governance that is not maintained is governance that gives false assurance.

Who We Build Governance Frameworks For

We do our best governance work for organisations that are ready to treat data governance as an operational discipline — not a compliance documentation exercise.

Healthcare organisations that need DSPT Standards Met and CQC-defensible information governance documentation
Pharmaceutical distributors that need GDP data integrity governance and MHRA-audit-ready documentation
Organisations that have received a regulatory finding on data governance and need structured remediation on a defined timeline
Growth-stage businesses with AI ambitions that require governed, documented data before any AI programme can be responsibly deployed
Commercial leaders preparing for Series A due diligence or client procurement processes that require demonstrable data governance capability

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Ungoverned data is not a technical problem.It is a commercial and regulatory liability.