DATA HEALTH & AI READINESS
Know where your data stands—and what to fix next. An independent assessment that connects stakeholder conversations with evidence from your actual data.
Enterprise data leadership · Certified Data Management Professional
01 / THE FRAMEWORK
Your data challenges don’t live in just one system. The assessment follows data from the moment it’s collected to the decisions, products and AI use cases it supports.
Can you recognize the same customer or product across your business?
Evidence: duplicate rates, match coverage, orphan records.
Is data captured consistently, with someone accountable for it?
Evidence: capture rules, owner interviews, exception samples.
Does everyone understand what the data actually means?
Evidence: glossary coverage, catalog records, definition conflicts.
Can people trust the data—and spot when they shouldn’t?
Evidence: profiling queries, failed checks, incident history.
Can you trace a business metric back to an agreed source?
Evidence: source-to-product traces, contracts, reconciliation tests.
Is the data fit for the AI use cases you actually want?
Evidence: use-case samples, access tests, evaluation criteria.
Each applicable control is scored from 0 (absent) to 4 (measured and continuously improved) against a rubric agreed before testing. Scores combine documented practices with query results, records and interviews. Six equally weighted pillar scores roll up to 100.
Every finding includes its evidence, scope and confidence. Untested controls remain unknown, not automatic zeroes. Coverage is reported alongside the score; a full overall score is withheld if any pillar has less than 70% of its applicable controls tested. Confirmed missing controls score zero.
AI readiness is evaluated for agreed use cases. Unresolved access, permitted-use or critical quality gaps are reported as blockers regardless of the average. This is an independent consulting framework, not a DAMA certification or an industry benchmark.
02 / FROM QUESTIONS TO CLARITY
A focused engagement, scoped to your organization. Start with a business problem, a customer or product domain, or a critical data product.
Meet business owners, stewards, analysts and technology teams. Agree on decisions, systems, priority data and what “good” looks like.
Review documentation and run agreed, read-only profiling queries. Your team can run the queries in your environment if direct access isn’t appropriate.
Review findings together. Leave with a scored baseline, clear risks and an improvement backlog ranked by impact, effort and dependencies.
03 / WHAT YOU WALK AWAY WITH
A shared understanding of today.
A practical direction for tomorrow.
Pillar scores, tested coverage and confidence—with the findings behind each score.
Near-term fixes and 30 / 60 / 90-day priorities, proposed owners, effort estimates and measurable success criteria.
Which use cases have a sound data foundation, what needs work and which blockers must be resolved first.
A clear executive narrative and actionable detail your business and technical teams can use.
YOUR ASSESSMENT PARTNER
I’ve spent 10+ years building enterprise data organizations—and connecting the work of data teams to the needs of the business.
My experience spans master data management, data engineering, governance, analytics and AI enablement at multibillion-dollar organizations. I’ve led a 60-person global organization and delivered unified customer and product data across complex business environments.
I bring that combination of leadership perspective and hands-on technical depth to understanding where your data works, where it breaks down and what to improve next.
A FEW GOOD QUESTIONS
No. The scope meets you where you are—from CRM and operational systems to a warehouse, lakehouse or established data products. We agree on a manageable starting point.
Not necessarily. We can work with read-only access, approved samples or queries run by your team. Access, evidence handling and confidentiality are agreed before the assessment begins.
Timing and pricing depend on the number of domains, systems, stakeholders and use cases. After an initial conversation, you receive a proposed scope, deliverables, timeline and fee before committing.
No. Readiness depends on the intended use. The assessment flags specific quality, access, provenance and evaluation gaps; it does not certify a model, guarantee AI performance or replace a legal or security review.
LET’S FIND YOUR STARTING POINT
Tell me what’s working, what’s getting in the way and what you’d like your data to do next.