Data Science & Analytics
You can’t improve what you can’t measure. Most mid-market businesses have reporting, but few have the analytical infrastructure to answer hard questions: which initiatives are actually driving revenue? Where are the bottlenecks? What happens if we change pricing? We build measurement frameworks, predictive models, and BI systems that turn data into decisions. This is the ‘Measure’ step in our methodology — the discipline that closes the loop on modernisation, data infrastructure, and AI investment.
What you get
Real examples
Performance measurement frameworks
Design and implement end-to-end measurement — from event tracking and data collection through to dashboards and automated reports that tie activity to business outcomes.
Predictive modelling
Build models that forecast demand, identify churn risk, predict maintenance needs, or score leads — turning historical data into forward-looking business intelligence.
Common questions
What’s the difference between analytics and data science?
Analytics tells you what happened. Data science tells you why it happened and what’s likely to happen next. We do both — starting with solid measurement foundations and layering in predictive models where they create value.
Do we need a data warehouse first?
It helps significantly. Without clean, centralised data, analytics is slow and unreliable. If you don’t have a warehouse yet, we can build both together — or start with focused analytics on your existing data sources.
What BI tools do you work with?
Looker, Tableau, Metabase, and custom dashboards. We recommend based on your team’s technical comfort, your data stack, and your budget. The tool matters less than the measurement design behind it.
How do you measure AI ROI?
We define baseline metrics before AI deployment, instrument the AI features, and track the delta. Time saved, error reduction, throughput increase, cost avoidance — specific to each use case, not generic ‘AI value’ claims.
Can you work with our existing analytics?
Yes. We regularly audit and improve existing analytics setups — fixing tracking gaps, rebuilding dashboards, and adding the measurement layers needed to evaluate new initiatives like AI.
Ready to get started?
Tell us about your project and we'll tell you honestly how we can help.
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