Data Science & Analytics
The measure of an analytics engagement is whether a decision changed. Most dashboards fail that test — not because the numbers are wrong, but because nobody specified which decision the number was supposed to inform, or what they would do differently at each value.
Analysis that nobody acts on
A forecast reported as a single number invites false confidence and gets overridden the first time it misses, because it never communicated how uncertain it was. A segmentation that splits customers into elegant clusters changes nothing if no team has a different action for each cluster. And correlational analysis presented as if it were causal produces the most expensive mistakes: the customers who used the feature retained better, therefore push the feature — when the truth was that customers who were going to retain anyway were more likely to try it. The analysis was correct and the conclusion drawn from it was wrong.
The three calls that decide the outcome.
Made explicitly, with the trade-off written down, before anything gets built.
Point forecasts or prediction intervals
Almost every planning decision is really a decision about risk, which means the interval matters more than the point estimate. Inventory, staffing and capacity decisions are asymmetric — being short costs differently from being long — so the useful output is a distribution with the cost of each error direction attached. A single number hides exactly the information the decision needs.
Where forecasts have to reconcile
Forecast a total and its components independently and they will disagree, which is where finance loses trust in the model. Hierarchical reconciliation makes the country forecasts sum to the region and the region to the group, and the choice between top-down, bottom-up and optimal reconciliation is a real trade-off between accuracy at the level you plan at and coherence across the hierarchy. Deciding which level matters most is a business question, not a statistical one.
Correlation, or a causal design
If the output will drive an intervention, correlation is not enough. That means an experiment where one is possible, and where it is not, a quasi-experimental design — difference-in-differences, regression discontinuity, or a synthetic control — with its assumptions stated and tested. This is more work than a regression and it is the difference between knowing what predicts an outcome and knowing what moves it.
The capabilities we bring on day one.
Each engagement assembles from this menu, sized to your scope, paced to your calendar.
Decisions first
Every workstream tied to a named decision and what would change at each outcome.
Honest uncertainty
Intervals and error costs, not point estimates that invite false confidence.
Causal where it counts
Experimental or quasi-experimental designs when the output will drive an intervention.
Pipelines that survive
Reproducible data pipelines, so the analysis can be rerun next quarter without archaeology.
Personal data, proportionately handled
Customer analytics runs on personal data, and the constraints are practical rather than abstract: a lawful basis for the processing, purpose limitation that stops a dataset gathered for billing being repurposed for scoring without review, and pseudonymisation wherever the analysis does not genuinely require identity. Most segmentation and forecasting work does not need identified records at all. We design for the least identifiable dataset that still answers the question, which is both the compliant position and the one that survives a breach with the least damage.
From kickoff to live in four phases.
Each phase has named deliverables, named owners and a named gate, and every one respects the systems you already have in place.
Discover
Which decisions are in scope, who makes them, on what cadence, and what evidence would actually change them.
Design
Analytical approach per decision, uncertainty and reconciliation requirements, and whether a causal design is needed or correlation suffices.
Build
Reproducible pipelines, models and the reporting surface the decision-maker will genuinely use, in the tool they already open.
Run & hand off
Refresh automation, monitoring for the data quality issues that silently corrupt analysis, and enablement for your analysts.
What a good fit looks like.
Stated up front, so neither of us spends a call finding out this was the wrong conversation.
Engagement shape
A scoped sprint of roughly eight weeks against one decision area, or an embedded analyst inside your team for continuous work.
From your side
Access to source systems rather than extracts, the decision-makers themselves, and clarity on the cadence at which decisions get made.
When not to hire us
If what you need is a BI rebuild — dashboards, semantic layer, self-serve reporting — that is a data engineering engagement. We will say so rather than dress it up as data science.
Data Science & Analytics questions, answered.
BI answers what happened, reliably and at scale. This work is about what will happen and what to do about it — forecasting with usable uncertainty, segmentation attached to actions, and causal analysis where a decision will follow. The two are complementary, and if your actual need is reporting infrastructure we will tell you.
Yes, and part of discovery is being honest about which questions the data can and cannot support. Messy data usually constrains precision rather than preventing analysis. What it does not survive is being presented as if it were clean, so we state the limitations alongside the findings.
Because most analytics questions that matter are causal, and correlation systematically misleads on exactly those. If the output will drive an intervention, an experiment — or a quasi-experimental design with stated assumptions — is the difference between knowing what predicts an outcome and knowing what moves it.
Where a dashboard is the right delivery mechanism for a recurring decision, yes, and we build it into the tool your team already opens rather than introducing another one. What we avoid is delivering a dashboard as a substitute for an analysis, which is how most reporting ends up unread.