Data Strategy and Audit
Get your data ready for AI. We audit what you have, fix what is broken, and build foundations that support accurate AI applications.
Data is the foundation of AI
AI is only as good as the data behind it. We audit your data landscape, identify problems, and create a practical strategy for getting your data into shape for AI applications.
Data quality assessment
We examine your data for completeness, accuracy, consistency, and freshness. We identify the specific issues that will cause AI to produce poor results.
Architecture review
We map how data flows through your organisation: where it lives, how it moves, and where it gets stuck or corrupted.
Remediation plan
A prioritised plan for fixing data issues, improving governance, and building the data infrastructure your AI initiatives need.
Practical, not theoretical
We focus on the data that matters for your AI use cases, not a boil-the-ocean exercise. Fix what you need, when you need it.
Use-case driven
We prioritise data work based on your AI goals. Not all data needs to be perfect; we fix what matters for the use cases you want to build.
Source mapping
We document every data source, its quality, accessibility, ownership, and update frequency. You get a clear picture of your data estate.
Governance foundations
We help you establish practical data governance: ownership, quality standards, access controls, and maintenance processes.
Common triggers for a data audit
Before an AI project
You want to build AI but suspect your data is not ready. An audit tells you exactly what needs fixing and how long it will take.
After an AI failure
An AI project delivered poor results and you suspect data quality is the cause. We diagnose the data problems and create a fix.
Multiple data silos
Data is scattered across systems with no single view. We map the landscape and design how to connect it for AI use.
How the audit works
Data audits typically run for two to four weeks depending on the number of sources and complexity of your data landscape.
Discovery and mapping
We catalogue your data sources, map flows between systems, and document ownership, quality, and access patterns.
Quality analysis
We profile your data for completeness, accuracy, consistency, and timeliness. We test samples against your AI use case requirements.
Strategy and roadmap
We deliver a prioritised remediation plan with specific actions, timelines, and resource requirements for each improvement.
Frequently Asked Questions
Do we need perfect data for AI?
No. Different AI use cases have different data requirements. The audit identifies what is good enough and what specifically needs improvement for your priority use cases.
How long does a data audit take?
Two to four weeks for most organisations. Larger enterprises with many data sources may need up to six weeks.
Will you fix the data issues you find?
The audit identifies and prioritises issues. We can then help with remediation as a follow-on engagement, or your team can execute the plan we provide.
What access do you need?
Read-only access to relevant data sources, documentation about your systems, and time with the people who manage your data day to day.
Ready to get your data in shape?
A data audit gives you clarity on where you stand and what to fix first. Book a call to scope the engagement.