A complete map of your data sources, quality issues, analytics gaps, and AI readiness — with a sequenced investment plan for closing each one.
A 90-minute structured call with the right people in the room. By the end, we have what we need to run the full assessment independently.
The kickoff brings together your data or analytics lead, an IT contact who understands where data lives, and a senior business stakeholder from finance or operations. Forty-eight hours before, we send a pre-work packet: a data source list, examples of reports leadership actually uses, and a prompt on recent decisions where better data would have changed the outcome.
The kickoff covers the decisions data should be informing but isn’t, who uses data today and how, what analytics tools and reports have been built and abandoned, and where trust in the numbers breaks down and with which audience.
The questions that surface things quickly: which number does the board use for revenue, which does finance use, and are they the same? Where does the data team spend most of its time, and how much of that is cleaning versus analyzing?
The Data Assessment covers the full data environment — not just the tools, but the quality, governance, and organizational capacity to use what exists.
Every system that generates or stores data the organization depends on — named, described, and assessed for reliability and accessibility.
How data moves between systems, where it is transformed, where it breaks, and where manual intervention fills the gaps.
Accuracy, completeness, consistency, and timeliness assessed by domain. Quality issues scored by business impact, not by how messy the data looks.
Every reporting tool in use, who uses it, what it measures, and whether it is trusted. Tool proliferation and the analytics graveyard are mapped explicitly.
Who owns what data, how definitions are managed, and whether finance and ops agree on the numbers when it matters.
Whether the data foundation exists to support the AI use cases leadership is asking about. Scored by use case, not by aspiration.
Who does what with data, what is manual, where the bottlenecks are, and how much of the data team’s time is spent cleaning rather than analyzing.
We work from documentation and structured interviews. No systems accessed, no work disrupted.
Kickoff, stakeholder interviews, data source documentation, sample report collection, analytics landscape walkthrough.
Every data source, quality issue, governance gap, and AI use case assessed and prioritized by business impact.
Three documents and a 90-minute readout. The executive summary is written for the board and board-ready from delivery.
Written for the right audience at every level, from the board to the person who will implement the roadmap.
Two to four pages for senior leadership and the board. The headline data gaps and the top three investments that would change how decisions get made.
Full picture for data, analytics, and IT leadership. Every source assessed, every quality issue documented, analytics maturity and AI readiness scored.
Foundation work first, then the analytics layer, then advanced use cases. Each action has a timeline, estimated cost range, and the business outcome it unlocks.
Most organizations recognize at least three of these before the readout call is over.
Fill in a few details. We’ll confirm scope and price before anything starts.
Maps every system, vendor, contract, and dollar of technology spend. Right when systems or vendors are the primary concern.
See the Technology Assessment →Runs both tracks in parallel, finds the dependencies. Four deliverables, one integrated roadmap, same 21-day timeline.
See the Combined Assessment →