Analytics, demand planning, churn, fraud risk and predictive maintenance, on one trusted foundation.
Most dashboards fail quietly. Two teams pull the same metric from different systems, get different numbers, and the meeting turns into an argument about whose figure is right. Once that happens nobody trusts the report, and any forecast built on top of it never stood a chance.
So we start underneath. We build visualisation and prediction on a governed data foundation: a cloud data warehouse, a medallion architecture and a semantic layer that gives every team one version of the truth. Each metric is defined once and reused everywhere, which means the dashboard, the forecast and the board pack all agree. It is the boring part of the work, and it is the part that decides whether anyone believes the output.
On that footing we build the predictive layer: demand planning, churn, fraud risk and predictive maintenance, alongside the day-to-day analytics that run the business. If you are weighing up where reporting ends and prediction begins, our guide to business intelligence versus data analytics draws the line, and our predictive analytics explainer covers the modelling side.
A semantic layer defines each metric once, so every dashboard, report and model reads from the same source.
Demand, churn and fraud-risk models that look forward, instead of describing last quarter in more detail.
A live view of the indicators that matter, so teams respond to signals as they appear rather than to historic reports.
Performance consolidated into a consistent executive view: trends, risk and opportunity in one place.
We ask which decisions each dashboard or model is meant to change. If a number will not alter what anyone does, we leave it off the page.
We assess where your data lives and how reliable it is, then structure it on a governed warehouse with a semantic layer, so every metric has one definition.
Dashboards for the day-to-day, predictive models where the data supports them: demand, churn, fraud risk, maintenance.
Every model goes through validation testing before it reaches production, so performance is measured rather than assumed.
Models are versioned, monitored and governed as standard, and accuracy is checked continuously as your business changes.
Demand planning, churn, fraud risk and predictive maintenance are the most common, alongside the analytics and reporting that sit around them. What makes sense for you depends on the data you already collect.
Usually because each one calculates its metrics from a different system, or from a different definition of the same term. A semantic layer fixes this at the root: each metric is defined once and every dashboard reads from that definition.
No. We design around the systems you already run. The foundation improves how information from them is used, not where it lives.
Every model is validated before launch and monitored afterwards. Versioning, observability and governance come as standard, so drift shows up in the monitoring rather than in a bad decision.
Access, permissions and architecture are designed with your IT and security teams from the start, within enterprise security standards.
A forecast is only as good as the pipeline feeding it, and a dashboard nobody trusts is decoration. Tell us what you want to see and predict, then talk to us: we will give you a straight answer on whether your data can support it yet.
Tell us where your data is today and what you want AI to do. We will come back with a straight answer on what your foundation needs and where the quickest real win is.
Talk to us