Data Quality Audit
Find Where The Data Actually Broke
A broken dashboard is usually the last place a data quality problem becomes visible, not the place it started. This audit traces suspect data through the pipeline to establish where the defect first appeared — and whether it originated upstream or was introduced during processing.
Problem We Solve
Two reports disagree. A metric suddenly moves. A downstream table holds values nobody trusts. The difficult part is rarely detecting that something is wrong — it’s working backwards through transformations, joins and pipeline stages to find where the data stopped being correct. Most teams fix the symptom, and the same breakage returns.
What We Deliver
- Analysis of the affected data flow, from source through the relevant pipeline stages
- Identification of the point at which the defect first appears
- Separation of problems inherited from source systems from those introduced in-pipeline
- Evidence of how the issue propagates downstream
- The transformation or processing logic contributing to the problem
- A prioritised set of remediation recommendations, highest impact first
How It Works
- Start with a free 15-minute conversation to understand the symptom, the affected data and the likely investigation boundary.
- We agree a fixed scope and a fixed fee before beginning.
- The investigation runs inside your existing infrastructure — your data is not moved into an external SaaS product.
- Access is scoped to what the audit requires, and no more.
- We trace the data through the agreed pipeline and isolate where discrepancies first appear.
- You receive the findings and remediation priorities, and decide how implementation is handled.
The Difference:
Most data quality tooling is good at telling you that a test failed, a field is incomplete, or a dataset no longer matches an expected rule. That is detection. The harder question is where the defect entered the chain.
This audit traces data between pipeline hops to localise that point of origin, distinguishing a bad value inherited from source from a correct value corrupted during ingestion or transformation.
Engineering effort then goes to the cause rather than the place the symptom happened to surface.
Where It Helps:
- CTOs dealing with reporting that has repeatedly lost trust
- Heads of Data whose teams spend too long tracing discrepancies by hand
- Engineering leaders with pipelines that have become hard to reason about as they’ve grown
- Organisations where the same data quality incidents keep reappearing downstream
- Teams who can already detect failures but struggle to isolate where they originated
case example
How a UK telecom team cut SLA breaches by 95%
Our technical co-founder spent nine months on a cloud cost and data programme at Lebara, a UK telecom, working as a contractor on their platform. Brittle, monolithic pipelines were breaking reporting and creating compliance risk. The programme rebuilt them on Azure Data Factory and Synapse using a Data Mesh approach, removing single points of failure and adding automated monitoring.
Outcome:
- SLA breaches reduced by 95%
- Higher data quality and completeness
- Delivered in under 3 quarters
That pipeline experience is the basis of the audit: understanding how data behaves between systems, not merely whether a downstream rule has failed.
Not sure you can trust the data behind your numbers?