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Data maturity audit

Data maturity and infrastructure audit.

A structured review of the data infrastructure you already have: what works, what is quietly broken, what it costs, and what to fix in what order. You get a written assessment and a prioritized roadmap, whoever ends up doing the work.

  • 2 to 3 weeks
  • Written assessment
  • Prioritized roadmap

What you get

Know how bad it is, and what to fix first.

A full inventory

Every pipeline, source and report, including the undocumented ones.

Hidden risks surfaced

Silent data quality issues and single points of failure.

The real cost

Warehouse compute, connector licenses and analyst hours on manual work.

A costed, ordered roadmap

The thing that unlocks budget.

The problem

Nobody can say how bad it actually is.

Teams know something is wrong with their data. Reporting is late, numbers get questioned, and every new request takes weeks. What nobody can state precisely is where the failure is: ingestion, modeling, the BI layer or process. So remediation gets guessed at, often by buying a tool that addresses a symptom. Meanwhile the risks that matter, such as a single undocumented pipeline one person understands, stay invisible until that person leaves.

What the audit covers

What's included.

  • A full inventory of pipelines, sources, transformations and reporting surfaces, including the undocumented ones
  • Reliability assessment: what fails, how often, how it is detected, and how long it takes to notice
  • Data quality and modeling review, including whether key metrics are defined consistently
  • Cost review across warehouse compute, connector licensing and analyst hours spent on manual work
  • Key-person and documentation risk: what breaks if a specific individual is unavailable
  • A prioritized roadmap with effort estimates, sequenced by impact and dependency

How we work

How we run it.

  1. Step 1

    Interview the people who actually run and consume the data, not only the sponsor

  2. Step 2

    Get read access to the warehouse, orchestration and repos, and inspect the real state

  3. Step 3

    Trace two or three important metrics from source to dashboard and find where they diverge

  4. Step 4

    Present findings, take challenge from your team, and deliver the final written roadmap

What we use

Tools, platforms and methods.

  • Warehouse review
  • Pipeline tracing
  • dbt / SQL review
  • Cost analysis
  • Stakeholder interviews

FAQ

Common questions.

What access do you need?

Read-only access to the warehouse, orchestration tool and transformation repo, plus time with two or three people who work with the data daily. We can work from documentation and interviews alone if access is genuinely blocked, but the findings will be weaker and we will say so.

Is this useful if we already know our problems?

Usually, for two reasons. First, the audit turns known problems into a prioritized, costed sequence, which is what unlocks budget. Second, it consistently surfaces things nobody knew, most often silent data quality issues and single points of failure.

What do we actually receive?

A written report covering current state, findings ranked by severity, and a sequenced roadmap with effort estimates, plus a working session to walk through it. It is written to be handed to your team or another vendor without translation.

How does this differ from a vendor assessment?

A vendor assessment exists to justify a purchase, and the conclusion is known before it starts. This one can conclude that your stack is fine and the problem is process, which has been the outcome more than once.

Book a data audit.

Tell us roughly what your stack looks like and we will scope the audit and the access it needs.

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