ERP analytics implementation for enterprise programs
Analytics platforms are proven. What decides the outcome is whether anyone governed the data before the agents started reading it. I help you build the decision engine, not another report factory, module by module, layer by layer.
Workday Prism, Oracle Fusion Data Intelligence, and SAP Datasphere are all capable platforms. What decides whether they earn trust is whether someone named a data owner, graded the data, and defined the top metrics once before the dashboards multiplied. Gartner predicts 80% of data and analytics governance initiatives will fail by 2027, and the reason is almost always the same: no crisis forced the ownership question early enough.
I sit on your side of that work. Independent, client-side, with no stake in how many licenses the program buys. The job is to make analytics a governed decision set: a named owner per domain, a semantic layer with one definition per metric, a rationalized report catalog, and a proven AI use case before agents touch production data.
The shift I createThe analytics stack, decided before you scale AI.
Each area is a place where a program quietly loses trust in its numbers, and where a little governance pays for itself many times over.
Governance & ownership
A named data governance owner and steward per domain, before the first dashboard ships.
Data quality & readiness
A graded assessment across ownership, duplicates, completeness, definitions, access, and lineage.
The semantic layer
Your top metrics, defined once, so every tool and every agent returns the same answer.
Integration & blending
Govern data where it lives; decide what actually needs to move versus what does not.
Report rationalization
Retire what nobody opens; a real target is more retired than rebuilt in year one.
In-suite vs best of breed
The right architecture per workload, sequenced by phase, not picked once for everything.
Change management
An executive sponsor and a decision velocity metric, tracked from the first steering meeting.
AI & agent readiness
One proven use case, a governed semantic layer underneath, and a real consumption budget.
Vendor & licensing
Clarity on what is bundled, what is separately licensed, and what AI actually costs at your usage.
The full field guide, and a toolkit for the scope.
The ERP analytics field guide
The full stack, governance, the semantic layer, staying in suite versus going best of breed, and what changes in 2027.
Read the guideThe readiness toolkit
A pre-implementation readiness checklist across nine areas, plus an area-by-area quick reference. Built to keep beside your program plan.
Download the toolkit (PDF)The exception register
The warning signs that an analytics program is heading for a trust problem, with the entry that clears each one, inside the guide.
See the red flags →Put independent eyes on your analytics roadmap.
If executives are already asking why two reports disagree, a candid review of the governance and semantic layer before you scale AI on top of it is the cheapest insurance you will buy.