You cannot build on data
you do not understand.

A data audit gives you the complete picture of your data landscape — what you have, what condition it is in, where it is stored, and what it is costing you in poor decisions, operational inefficiency, and missed commercial opportunity.

Growth-stage businesses operating on assumptions about their data quality. Pharmaceutical organisations facing MHRA audits without reliable audit trails. Healthcare providers making decisions from inconsistent records. The data audit is the diagnostic that precedes every data programme we run — and the reason those programmes succeed where others have failed.

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Every data programme that fails can be traced to a step that was skipped: the audit that revealed what was actually there.

Organisations that build data infrastructure on unaudited foundations consistently encounter the same problems — pipelines that carry bad data faster, analytics that surface unreliable conclusions, and AI programmes that amplify the inaccuracies already present in the data. The data audit is not an administrative exercise. It is the commercial and technical foundation on which every subsequent data investment depends. We run it first, every time, without exception.

47%

Of data quality issues in organisations we audit are unknown to the business before the audit — including issues that are actively affecting commercial decisions.

More expensive to remediate data quality issues discovered mid-programme than to identify and address them during a structured audit before any build work begins.

12 WKS

Average duration of a comprehensive data audit and strategy engagement — producing a complete picture of the data estate and a 12-month implementation roadmap.

Four patterns. One invisible cost.

These are the data quality and governance problems that cost organisations commercial value every day — usually without the organisation being clearly aware of either the problem or its scale.

Decisions Made on Data Nobody Has Verified

The organisation runs reports, makes forecasts and communicates numbers to stakeholders — without anyone having tested whether the underlying data is reliable. In pharmaceutical environments, this creates MHRA exposure. In commercial environments, it drives strategic decisions built on inaccurate foundations. The most expensive moment to discover your data is wrong is when something has already depended on it.

SLOMultiple Systems With No Single Source of Truth

Your CRM says one number. Your finance system says another. Your operational database says a third. Without a defined and governed single source of truth, the organisation wastes significant time reconciling data rather than using it — and never fully trusts any of the numbers it produces. Every strategic meeting begins with a reconciliation exercise that should not need to happen.

Data Quality Issues Surfaced During High-Stakes Events

MHRA inspections. Client due diligence. Investor data rooms. Series A fundraises. These are the moments when data quality issues typically become visible — and they are the worst possible moments for that discovery. The organisation that discovers its data is inconsistent during a regulatory inspection or investor review is in a fundamentally different position from the one that discovered it proactively and addressed it.

No Understanding of the Commercial Value of Data Assets

Most organisations treat data as a byproduct of operations rather than as a commercial asset in its own right. They do not know which datasets are commercially valuable, which are creating regulatory exposure through unnecessary retention, or which insights are locked in data they are not currently using. The commercial value of an organisation’s data almost always significantly exceeds what its data management practices would suggest.

Five phases.One complete picture.

A data audit is not a spreadsheet inventory. It is a structured commercial and technical assessment — producing a complete understanding of your data estate, the quality of what it contains, and a strategy for making it work commercially.

Data Landscape Discovery

We build a complete inventory of your data estate — every source system, every database, every spreadsheet, every integration, every third-party data feed. Most organisations significantly underestimate the number of data sources they operate. The inventory reveals not just what exists, but where ownership sits, how current the data is, which systems are authoritative, and which are shadow copies nobody is maintaining.

Source system inventory with data volume, velocity and recency
Data classification catalogue — sensitivity, purpose, regulatory status
Data ownership map — who is accountable for each dataset
Integration dependency diagram — how systems connect and where data flows
Shadow data identification — unofficial datasets outside governed systems

Data Quality Assessment

For each data source identified in Phase 01, we assess quality across five dimensions: completeness (are required fields populated?), accuracy (does the data reflect reality?), consistency (is the same fact represented the same way everywhere?), timeliness (how current is the data?), and validity (does the data conform to expected formats and business rules?). Every finding is graded, evidenced, and mapped to a commercial consequence.

Data quality scorecard by source system and quality dimension
Field-level profiling results with anomaly identification
Data quality issue register with severity grading and evidence
Duplicate and redundant data identification across systems
Benchmark comparison against sector-specific data quality standards

Data Governance Review

We assess your existing data governance posture — policies, ownership, stewardship, access control, lineage documentation, and change management processes. In regulated sectors, this maps against applicable regulatory requirements (GDPR, MHRA, NHS Digital). The governance review consistently reveals a significant gap between what organisations believe their governance posture is and what it actually is in documented, enforceable practice.

Governance posture assessment — policies, stewardship, enforcement
Regulatory compliance mapping — GDPR, MHRA, NHS Digital as applicable
Data access control review — who can access what, and should they
Policy gap analysis with specific remediation recommendations
Data lineage documentation for critical business processes

Commercial Impact Analysis

Every data quality and governance finding is translated into a commercial consequence. Missing records that prevent accurate revenue reporting. Inconsistent customer data creating duplicate communications and wasted marketing spend. Unreliable operational data driving incorrect stock decisions. Incomplete clinical records creating compliance exposure. The commercial impact analysis is the business case for every recommendation that follows — and the number that makes data investment a no-brainer.

Commercial impact statement per finding — revenue and cost quantified
Risk exposure assessment — regulatory, operational and reputational
Opportunity identification — commercial value in underutilised data
Priority matrix — ranked by commercial impact and implementation effort
Executive summary and board-ready commercial case presentation

Data Strategy & Roadmap

The audit findings and commercial impact analysis inform a data strategy — a structured programme for addressing the identified issues in a sequence that maximises commercial return and minimises technical risk. The strategy defines what to fix first, what to build next, what to decommission, what governance changes to make, and what the architecture should look like when the programme is complete. Every recommendation is costed, sequenced, and tied to a commercial outcome.

Data strategy document — vision, principles and programme structure
12-month implementation roadmap with phase sequencing and milestones
Technology recommendations with build/buy/partner analysis
Governance framework design — policies, roles, stewardship model
Success metrics and KPIs for ongoing data programme measurement

What makes this audit commercially useful rather than technically exhausting

Commercial Framing, Not Technical Inventory

A data audit that produces a 200-page technical findings register is not useful to a commercial director or a board. Every finding we produce is expressed in commercial terms — what it is costing, what it is preventing, and what fixing it would return. The technical detail exists and is available, but the deliverable is a commercial argument, not a database report.

Sector-Specific Regulatory Expertise

We understand the specific regulatory context that makes data quality a compliance issue in pharmaceutical and healthcare environments — not just a commercial one. ALCOA+ data integrity, GDPR processing requirements, NHS Digital standards, MHRA GDP frameworks. The audit is structured around the regulatory questions your inspectors will ask.

A Strategy, Not Just a Findings List

The engagement produces a 12-month implementation roadmap, not just a catalogue of problems. Every recommendation is sequenced, costed, and connected to a commercial outcome. The audit is the start of a data programme — not the end of a consulting exercise.

Senior Data Practitioners on Every Engagement

Data audits are only as useful as the judgment behind them — the ability to distinguish a data quality issue that will cost you materially from one that is technically interesting but commercially irrelevant. That judgment requires experience. Every engagement is led by senior practitioners who have conducted audits in pharmaceutical, healthcare and commercial environments.

Connected to the Full Data Programme

The audit is designed to flow directly into the implementation capabilities of the Pillar 02 programme — data infrastructure, analytics, governance, and AI-ready foundations. The strategy we produce is designed to be implemented by the same team that produced it. No translation required, no context lost between audit and build.

Who We Run Data Audits For

We do our most valuable data audit work for organisations that are prepared to find out what their data actually looks like — not what they hope it looks like.

Pharmaceutical distributors approaching MHRA inspections who want to know their data posture before the inspector does
Growth-stage businesses whose forecasting, reporting and analytics are unreliable and whose team has stopped trusting the numbers
Organisations with AI ambitions whose initial AI investments have underperformed because of data quality issues they could not diagnose
Commercial directors who want a credible business case for data investment and need the commercial impact quantified before taking it to the board
Healthcare organisations preparing for CQC or NHS Digital reviews who want to know their data governance posture before the review, not during it

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You cannot build on datayou do not understand.