When the problem isn't the data. It's the process.
Most analytics problems that look like data problems, or tool problems, are really process problems that span layers. A new data source fails because nobody redesigned the reports downstream. A model is accurate but unused because it never connected to a decision. We look across the whole stack, from where data enters to where decisions get made, and design the process that connects them. It is the same instinct behind our measurement work: the analytics are rarely the bottleneck.
From data at the bottom to decisions at the top
We assess each layer on its own and, more importantly, the handoffs between them, because that is where most value leaks. Read it top down, the way the business experiences it: the decision is the point, and everything beneath exists to serve it.
- 05 Decisions Which decisions does this actually inform, and who owns them?
- 04 Insights Do the people who need answers get them in time, in a form they can use?
- 03 Analytics Are our models and metrics answering the questions the business is asking?
- 02 Technology Is the stack built for how we work, or how a vendor assumed we would?
- 01 Data Can we trust what is coming in, and do we know what happens to it on the way?
Which decisions does this analytics investment actually inform, and who makes them?
We start at the top, with the recurring decisions the business actually makes: planning, pricing, investment allocation, assortment, forecasting. Who owns each one, what they rely on to make it, how often it comes around, and whether anyone checks afterward how it turned out.
A map of your real decisions and who owns them, traced back to the data that feeds each one, with a way to track outcomes so a decision can be judged after the fact rather than just made and forgotten.
Do the people who need answers get them in a form they can use, on time?
Every report and dashboard, who it is for, and whether they use it. Most organizations have more reports than decisions, many of them duplicates nobody retired. We look at how long it takes from data landing to an answer reaching the person who needed it, and how much of that is self-serve versus waiting on an analyst.
A leaner set of reports mapped to the people who actually use them, a delivery model matched to each audience, and a clear plan for retiring the redundant ones without anyone losing a number they depend on.
Are our models and metrics answering the questions the business is asking?
Whether a metric means the same thing in two different rooms. The models in use, whether the method still fits the question, and how they are validated and refreshed. How much analyst time goes into reworking the same answer versus producing a new one.
A metric dictionary so the business argues about the decision rather than whose number is right, a plain-language review of which models still earn their keep, and a workflow that spends less time on rework.
Is our stack built for how we actually work, or how a vendor assumed we would?
The platforms and tools in play, how they connect, where the redundancy and the technical debt sit, and what the security and compliance teams require. Where a build-versus-buy-versus-partner call was made by default rather than on purpose.
A clear picture of the stack you have against the one the work needs, a rationalized tool list, and a migration plan with parallel-run and cutover steps, so a platform change is sequenced rather than risked all at once.
Can we trust what is coming in, and do we know what happens to it on the way?
Where the data comes from, how often, and under what contract. Whether product, geography, customer, and time line up across sources, how keys and master data are managed, where sources are reconciled, and who owns a data quality rule when it breaks.
A source-to-target map, the crosswalks that make two systems agree, a reconciliation and quality framework, and an ownership matrix so that when something looks wrong there is a name next to it.
We document how it works, not how it is supposed to work
Stakeholder interviews and process mapping that capture how things actually run today, which is rarely what the documentation says.
A target state built backward from the decisions, not forward from the data. The decision is the fixed point.
Assessed on both your side and your providers' side where external partners are involved, layer by layer, ranked by business impact and risk.
What comparable organizations have done, and candidly, what they would do differently if they ran it again.
Sequencing, parallel-run and overlap periods, cutover criteria, and the risk mitigation that keeps the business running during the switch.
Stakeholder alignment, training, and adoption tracking. A technically clean transition that users do not adopt has still failed.
Our depth in healthcare and claims data, and in syndicated retail and media measurement, informs how we work. The method itself is industry-agnostic.
When organizations call us
- Switching a major data provider, platform, or agency partner
- Consolidating after a merger, reorganization, or vendor rationalization
- Building a new analytics function, or redesigning one that has drifted
- A large analytics investment that is not changing any decisions
- Folding a new data type, healthcare or claims data for instance, into an established commercial process