Stop Reporting the Past. Start Predicting the Future.

Less than most people think. For descriptive analytics and dashboards, a few months of clean data is enough to surface meaningful patterns. For predictive models, we typically need 12–24 months of historical data with enough examples of the outcome we are predicting. We assess data volume and quality in discovery and tell you honestly whether there is enough signal to build what you need — before you spend money on it.

Data analysts primarily work with structured data, SQL, and BI tools to describe what happened. Data scientists build statistical and machine learning models to predict what will happen. In practice the boundary is blurry — our team does both. We scope engagements based on the business question, not job title distinctions.

BI tools answer the questions you already know to ask. Data science surfaces patterns you did not know to look for, and predictive analytics tells you what happens next — not just what happened. If your current analytics is purely descriptive, there is likely significant untapped value in your data. That said, if better dashboards are what you need, we build those too.

We hold out a test set the model has never seen during training and measure performance on it. For time-series problems we use walk-forward validation — simulating how the model would have performed historically. We report accuracy metrics with confidence intervals, not just a single number. And we always compare against a sensible baseline — if our churn model is not meaningfully better than "flag all accounts that have not logged in for 30 days", we tell you.

Yes — we often embed to provide specific expertise, extra capacity for a defined project, or an external perspective on a problem the team has been stuck on. We adapt to your tooling and processes. We have worked alongside teams using everything from notebooks to full MLOps platforms.

Personal data used for analytics must have a lawful basis and appropriate safeguards. We apply pseudonymisation, aggregation, and access controls as part of the analytics design — not as an afterthought. For sensitive domains (healthcare, finance, HR analytics), we review the legal basis and data handling requirements before accessing any data.

It depends on your team and use case. Power BI is the best choice if you are Microsoft-native and want broad business user adoption at low cost. Looker is strongest for engineering-led organisations that want a governed semantic layer and embedded analytics. Tableau has the best visualisation depth for analyst-heavy teams. Metabase is the fastest to self-serve for smaller technical teams. We do a structured tool selection as part of data strategy engagements.

Dashboard and BI projects typically deliver usable output within 3–4 weeks. Predictive model projects take 8–14 weeks to reach production — but we validate early that the model has predictive power before completing the full build. Data strategy engagements produce a roadmap in 2–4 weeks. We sequence work to deliver the highest-value outputs earliest.