In the Combined Assessment I run both tracks side by side, find the dependencies between them, and deliver one integrated roadmap that sequences investments by cross-domain business impact.
Two separate assessments miss the connections. The combined engagement looks for them on purpose and sequences the roadmap to take advantage of them.
Most organizations experience technology problems and data problems as distinct issues managed by different teams. They usually aren’t. The data quality problem that looks like a governance failure is often a systems integration gap. The AI use case that can’t get funded is usually blocked by both a platform limitation and a data foundation gap simultaneously.
When I run the technology and data tracks in parallel, and let the findings from each inform the other in week two, these connections show up before the roadmap is written. The result is a sequencing of investments that accounts for cross-domain dependencies: which technology investment unlocks multiple data capabilities, which data fix requires a systems change first.
A technology track flags a legacy AMS as end-of-life. A data track on the same organization flags poor member engagement data quality. Run separately, these become two independent recommendations.
Run together, the cross-track analysis reveals that the data quality issue traces directly to the AMS’s limited API surface. The AMS replacement doesn’t just solve a technology problem. It solves the data quality problem at the same time. The integrated roadmap reflects that, and the investment case is significantly stronger.
Both tracks start at the same time, with two structured kickoffs in week one: one on technology, one on data.
The technology kickoff covers systems, vendors, integration architecture, technical debt, and team capability. The data kickoff covers data sources, quality, analytics, governance, and AI readiness. Both happen in week one, with pre-work sent 48 hours before each.
In week two, I review the findings from both tracks together, looking for cross-domain dependencies. A system flagged in the technology track might be the root cause of a quality issue in the data track. An AI use case blocked on the data side might require a platform change on the technology side. The cross-track analysis brings these connections into the open before the roadmap is written.
Week three delivers all four documents and a single 90-minute integrated readout. Leadership gets one coherent picture, not two separate reports to reconcile after the fact.
Both tracks run in parallel. The 21-day timeline is the same as a standalone assessment. The scope is broader, not the calendar.
Both technology and data tracks run discovery simultaneously. Two kickoffs, stakeholder interviews across both domains, documentation collection from both sides.
I score the findings from each track on their own, then review them together for cross-domain dependencies and compound risks. This step is unique to the combined engagement.
Four documents delivered and one 90-minute integrated readout. One coherent picture, not two separate reports to reconcile.
Three of the four match what a standalone assessment produces. The fourth, the integrated roadmap, is only possible when both tracks run together.
Not two summaries side by side. A single integrated read on how the technology and data environments interact, where they compound each other’s risks, and the sequenced investment case that addresses both. Board-ready from delivery.
Full depth of the standalone Technology Assessment: systems, vendors, contracts, technical debt, team capability, and spend, with cross-references to data dependencies where they exist.
Full depth of the standalone Data Assessment: sources, quality, analytics, governance, AI readiness, with cross-references to technology root causes where they exist.
A single sequenced roadmap accounting for cross-domain dependencies. Investments prioritized by integrated business impact, not by domain independently.
These findings only appear when both tracks run together and the week-two cross-analysis happens.
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Maps every system, vendor, contract, and dollar of technology spend. Right when systems or vendors are the primary concern.
See the Technology Assessment →Maps data sources, quality issues, analytics gaps, and AI readiness. Right when reporting or data trust is the primary pain.
See the Data Assessment →