Data Foundation Assessment
Built from hands-on work with lending and finance teams where reporting logic lived across dashboards, SQL, spreadsheets, and undocumented workflows.
For companies that...
The symptoms show up before the problem is obvious.
They just feel the symptoms: dashboards do not reconcile, historical reporting is hard to recreate, KPI definitions change across reports, and important business logic lives in BI tools, spreadsheets, SQL queries, or someone's head.
Then leadership asks for AI — but AI cannot fix a reporting foundation the business does not trust.
A focused diagnostic - not a vague audit.
The Data Foundation Assessment identifies where reporting logic is fragmented, what risks that creates, and what data foundation should be built first.
This is an audit of how reporting actually works today and what is preventing the business from getting trusted answers faster.
Scope
What I Review
- Interviews with business and data leaders
- Critical dashboards and recurring reports
- Embedded code, custom logic, and manual workflows
- KPI definitions and metric inconsistencies
- Historical reporting gaps
- Documentation and ownership gaps
- AI-readiness constraints
Deliverables
What You Get
- Key risks in the existing reporting model
- Historical reporting gap assessment
- Reporting architecture recommendation & roadmap
- AI-readiness implications
- Recommended first pilot
- Executive summary and detailed findings report
- Executive Review Session
Value
Value to Your Organization
- Reduce time spent reconciling numbers across teams, dashboards, and spreadsheets
- Align data, finance, operations, and leadership around shared KPI definitions
- Prioritize the first high-value data foundation pilot instead of guessing what to build next
- Surface hidden logic, ownership gaps, and manual workflows before they become larger risks
- Give leadership a clearer path from fragmented reporting to trusted data infrastructure
Leadership gets a clear answer to:
“What reporting foundation needs to exist so we can trust our numbers, recreate history, reduce manual work, and build future AI or automation on solid ground?”
The goal is to identify the first practical layer the business needs to move from fragmented reporting to trusted, reusable, AI-ready data.
Pricing: $14,000.
Scope confirmed before the engagement begins.
Week 1
- Interviews with business and data leaders
- Dashboard & report review
- Source logic review
Week 2
- Findings synthesis
- Risk assessment
- Architecture recommendation
Week 3
- Executive readout
- Recommended first pilot
- Detailed findings report delivered