Data Science & Predictive Analytics
Forecasting and decision support from your own history, with the validation to know when to trust it.
What this is
Data Science & Predictive Analytics is the discipline of using historical data to answer a forward-looking question: how much demand should we expect next quarter, which accounts are likely to churn, which maintenance items are likely to fail first. It sits downstream of Data Engineering — a model is only as good as the data feeding it — and produces something a decision-maker can actually act on, not just a chart.
Problems it solves
- Planning decisions (inventory, staffing, budget) made on gut feel or a simple trailing average, when the underlying data has enough signal to do better.
- Churn, fraud, or failure that gets noticed after it happens, when the patterns leading up to it were visible in the data all along.
- A one-off model built by a single analyst in a notebook, with no path to running reliably in production or being retrained as new data arrives.
- Forecasts that break down whenever seasonality, promotions, or external shocks change the pattern the model was trained on.
What the work involves
- Problem framing: translating a business question ("will this customer churn," "how much should we stock") into a modeling problem with a clear target, time horizon, and success metric.
- Feature and data prep: building the feature sets a model actually needs, usually in partnership with a Data Engineering pipeline rather than a one-time CSV export.
- Modeling: regression and classification models for structured prediction problems, time series methods (ARIMA-family, gradient-boosted trees, or neural approaches) for forecasting, chosen for the problem rather than for novelty.
- Validation: backtesting against held-out historical periods, and being explicit with stakeholders about a model's error bounds and where it is likely to be wrong.
- Productionization: packaging a model to run on a schedule or behind an API, with monitoring for drift so a model that was accurate at launch does not silently degrade a year later.
What you get
A forecast or prediction with a known error range, built on your own data, that a planning or operations team can act on — plus the pipeline discipline to keep it accurate as conditions change. We are explicit about model uncertainty rather than presenting a single number as certain.
When this isn't the right fit
If a straightforward trailing average or a simple rule already explains most of the variance in what you are trying to predict, a full modeling engagement adds complexity without adding much accuracy. And if the underlying data has fewer than a couple of years of consistent history, most forecasting methods will not have enough signal to outperform a reasonable heuristic — Data Engineering work to start collecting that history cleanly is often the right first step instead.
Where this work lands
Industry pages that draw on this service.
Others in this practice areaData & Analytics Core
Next step
If you are making a recurring planning decision from a spreadsheet formula, talk to our team about whether the data you already have supports something more rigorous.