Data Science Services
Built for Business

BI is primarily about reporting what has happened (dashboards, KPIs, historical trends). Data science goes further: it explains why things happened and uses statistical modelling and machine learning to predict what will happen next. Both are valuable; the right mix depends on your maturity and specific questions.

Yes, this is extremely common. We start by mapping all your data sources (databases, spreadsheets, APIs, third-party tools) and building a consolidated view. Often this involves lightweight data engineering work first. We'll scope this as part of the discovery phase so you know what's involved before we start.

Both. Every engagement includes reproducible notebooks (Python or R), documented data pipelines, and dashboards your team can update independently. We don't believe in creating dependency on us. We hand over code you own and can maintain or build on without us.

We apply appropriate hypothesis testing, report confidence intervals alongside point estimates, document assumptions, and flag potential confounders. We'd rather tell you we can't prove something conclusively with the available data than overstate a finding. Honest analysis is more valuable than confident-sounding but misleading analysis.

Yes. Many of our engagements are designed to be transitional, we do the initial work, establish best practices, build the tooling and documentation, and then mentor your internal hires as they come on board. We're happy to advise on hiring criteria, structure interview processes, and help onboard new team members.