Predictive analytics for decisions

We start from the historical operational data you already collect: maintenance logs, usage records, billing history, sensor output. A predictive model learns from the past to forecast the future, so the volume and quality of that history matter more than any single algorithm. The first step is a review of the data you hold, how far back it goes, how consistently it was recorded, and where gaps sit. From there, the team scopes which forecasts are feasible on the data as it stands today, and which would need additional collection or instrumentation before a reliable model can be trained. This keeps the work grounded in data that exists, and gives you a clear view of scope before any modeling begins.

Both. We train custom models where the use case requires it, and use established machine learning libraries and managed cloud services where they fit. A churn score on telecom accounts and a failure forecast on fleet sensors call for different approaches, so the method follows the problem rather than a fixed toolchain. Whichever route a model takes, it is deployed inside the data pipelines we already build for European operators, so forecasts reach the teams that act on them without a separate tool to maintain. Deployment is treated as part of the work: the model is packaged, monitored once it runs, and retrained as your operations change. Your team can extend or take over that layer once it is in production.

Each model is validated against historical outcomes before it reaches production. The data is split so the model is trained on one period and tested on another it has never seen, then its predictions are compared with what actually happened: predicted failures against recorded failures, forecast demand against recorded demand. That comparison gives a measurable error rate rather than a vague claim of accuracy, and sets the bar a model has to clear before it goes live. Once in production, accuracy is tracked over time, because operations shift and a model that fit last year can drift. When it does, the model is retrained on more recent data. The aim is a forecast accurate enough to act on, with its limits made explicit to the teams using it.

In most cases, yes. The forecasting pipelines we build run on top of the data infrastructure you already have, whether that is a data warehouse, a cloud platform, or the operational databases behind your systems. Predictive analytics is an application layer on existing data, so a separate platform is rarely needed to get started. We begin by reviewing what is in place and how the relevant data flows through it, then build the modeling and deployment layer around that. Where the underlying platform needs work first, for example if data is fragmented across systems or not yet collected at the right granularity, that scope is handled by our Data Analytics and Cloud services, so you know upfront whether the foundation is ready or needs a step before forecasting begins.