Data strategy and AI-assisted analytics that support real decisions — not a proof-of-concept that never reaches production.
Most organizations don't have a data shortage — they have a trust shortage. Reports disagree, definitions vary by team, and by the time data reaches a dashboard nobody is confident enough to act on it. I help design the data platform and analytics layer that closes that gap, and apply AI where it genuinely accelerates the work rather than as a label.
Data lives in disconnected systems, pipelines are built ad hoc, and by the time it reaches a report, three teams have three different numbers for the same metric. AI initiatives layered on top of that foundation tend to produce interesting demos and little operational value.
Consolidating scattered sources into a pipeline that consistently feeds analytics and downstream systems.
Applying AI to speed up analysis and summarization once the underlying data is trustworthy.
Turning operational data into the kind of dashboard leadership actually checks before a decision, not after.
A data foundation the business trusts enough to act on, not just look at.
AI applied where it saves real time, not adopted for its own sake.
A path from pilot to production, informed by having run that path before.
I start by tracing where the current numbers actually come from and where they diverge, before recommending a platform or an AI use case. That usually means fixing pipeline and data-quality issues in parallel with any analytics or AI work — skipping that step is the most common reason these initiatives stall.
Let's talk through where it breaks down before choosing a platform or an AI use case.
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