Data people actually trust
Quality, ownership and definitions settled, so a figure means the same thing in finance as it does in operations.
365 Cloud Advisors
Data Modernization for AI
Make your business data AI-ready.
AI is only as useful as the data behind it. We help organizations clean, connect, govern and structure operational data so people, analytics platforms and AI systems can trust it.
This is for organizations whose reporting already needs caveats, where two systems disagree and somebody reconciles it by hand every month.
AI inherits every problem your data already has.
Most businesses do not have a data problem so much as a fragmentation problem. The information exists. It is sitting in the ERP, the CRM, a shipping system, a finance spreadsheet and an inbox, and none of them agree on what a customer is.
That is survivable when humans do the reconciling, because a person notices when a number looks wrong. It stops being survivable the moment you point AI at it, because AI does not notice. It produces a confident answer from bad inputs, faster than your current process produces a slow one.
The work is unglamorous and it is the difference between AI that earns its place and AI that quietly makes decisions worse: consistent identifiers, defined metrics, trustworthy history and a governed layer that analytics and AI can both read from.
Data architecture and modernization
Microsoft Fabric strategy
ERP and CRM integration
Data quality and governance
Semantic models and Power BI
RAG and enterprise knowledge architecture
What you get
Deliverables, not a deck of slides.
A map of where your data actually lives
Every system that holds business-critical information, what it is authoritative for, and where the same fact is stored in more than one place.
A data quality assessment
Duplicate records, inconsistent identifiers, stale data and the specific places where two systems disagree — with how much each one is costing in manual reconciliation.
An agreed set of definitions
What margin means. What counts as an active customer. Written down and agreed, because AI cannot resolve a definition your own teams argue about.
A target architecture
How data should flow and where the governed, business-ready layer sits — sized to your actual requirements rather than to a platform vendor’s reference diagram.
A platform recommendation, with the honest alternative
Whether you need a data platform at all, and what the simpler option looks like if you do not. Plenty of organizations do not.
A staged delivery plan
What to fix first for the fastest reduction in manual work, and what only becomes worth doing later.
How it works
Where this sits in the route.
Step 1
Assess
Understand your business, systems, processes and AI readiness.
Step 2
Modernize
Strengthen ERP platforms, integrations and data foundations.
Step 3
Connect
Unify trusted data and make it securely accessible to people, apps and AI.
Step 4
Govern
Implement responsible AI with security, permissions and human oversight.
Common questions
The things people actually ask.
Do we need Microsoft Fabric?
Often not. A single ERP with straightforward reporting and modest data volumes is usually served well by Power BI and Power Automate, and adding a data platform buys complexity plus a capacity bill. Fabric earns its place when data is genuinely fragmented across several systems, or an AI programme has to reason across them.
Can we not just point AI at our systems and skip this?
You can, and it will answer. The question is whether you can tell when the answer is wrong. Data work is what makes an AI answer checkable.
How long before we see anything useful?
That depends on the state of the data, which is exactly what the assessment establishes. The plan is deliberately staged so the first phase reduces manual work on its own, rather than only paying off at the end.
Related reading
Written on this, at length.
Data Architecture
Fabric, OneLake, Lakehouses and Data Lakes: What Do They Actually Have to Do With AI?
Everyone nods when somebody says “we should put all our data in the lake.” Almost nobody asks which lake, or what is already floating in it.
ERP Modernization
How to Use AI to Find Technical Debt in Business Central
Everybody knows there is something buried under the snow. Almost nobody can say where it is, how deep it goes, or what it costs to leave it there.
AI Architecture
Building an AI Integration Layer Around Business Central
Connecting an AI model to Business Central takes an afternoon. Connecting it correctly takes an architect — and you find out which one you did in the incident report.
Start here
Not sure this is the right run for you?
The Trailhead Assessment is a short set of questions about your systems, data and processes. It ends with a practical map of what to modernize, what to automate, and what to leave alone.