AI & Data
The full domain, and the other capabilities within it.
Predictive Analytics
Forecasts and risk scores built on your own history, backtested before anyone relies on them.
What this is
Predictive analytics uses historical data to estimate something not yet known: next month's demand, which asset is likely to fail, which account is likely to leave. The work sits mostly in the features - assembling clean, correctly timed inputs - and in validating that the model would have been right on data it never saw.
The common failure is a model that scores well in a notebook and never changes a decision. Two causes: leakage, where a feature quietly encodes the answer and inflates validation scores, and no named owner for the decision the prediction is meant to inform. We also see teams reach for machine learning where a regression or a well-chosen threshold would do the job with far less to maintain.
When you need it
If more than one of these is true, this is usually the right place to start.
- Planning runs on spreadsheets and last year's numbers, and the variance is never analysed.
- A data science pilot produced a model that nobody wired into a business process.
- Maintenance is either reactive or on a fixed calendar, regardless of how the equipment is actually used.
- Nobody can say whether a deployed model is still as accurate as it was on the day it launched.
What the scope covers
- Problem framing: what decision the prediction changes, who owns that decision, and what action follows a given score.
- Feature engineering and a feature store, with point-in-time correctness to prevent leakage.
- Model development and honest backtesting against periods the model never saw during training.
- Deployment into the workflow that acts on the score, rather than a dashboard nobody opens.
- Drift monitoring, retraining triggers and a level of explainability appropriate to the stakes of the decision.
What you receive
| Deliverable | What it contains |
|---|---|
| Decision and baseline definition | The decision the model serves, the current baseline it must beat, and the margin at which deploying it is worth the operating cost. |
| Feature pipeline and store | Reproducible features with point-in-time correctness, so training and production see exactly the same values. |
| Model, backtest and registry | A versioned model with its backtest results across held-out periods and its assumptions written down alongside. |
| Monitoring and retraining plan | Drift metrics, alert thresholds and a documented trigger for when the model is retrained or retired. |
Reference architecture
A reference, not a template. Your estate decides which parts apply and in what order they arrive.
How success is measured
Targets are agreed with you before the work starts, and reported against for its duration.
- Forecast error against a held-out period, compared with the baseline method the business uses today.
- Input and prediction drift measured against the training distribution, with a defined alert threshold.
- Availability of the score at the moment of the decision it was built for, tracked as the measure of adoption.
Questions we are asked
Do we actually need machine learning for this?
Often not, and we will say so. A seasonal baseline, a regression or a well-set threshold solves a surprising share of forecasting and scoring problems, with a fraction of the maintenance and far more transparency. We benchmark against the simple method first and recommend a model only when it beats that benchmark by enough to justify the operating cost.
How much history do we need?
Enough to cover the cycles that matter - several full seasonal periods for demand, and enough recorded failures for maintenance work. Quality matters more than volume: consistent definitions, reliable timestamps and known gaps beat a longer series whose meaning changed halfway through.
Why did our last model perform worse in production than in testing?
The usual cause is leakage: a feature that was not actually available at prediction time, or one that encodes the outcome. Point-in-time correctness in the feature pipeline prevents it, and backtesting on unseen periods exposes it. The second most common cause is that the world changed and nobody was monitoring drift.
Can we explain a prediction to a regulator or a customer?
That depends on choices made at the start. Where explanation is required we favour inherently interpretable models and accept some accuracy cost, rather than bolting a post-hoc explanation onto an opaque model and calling it transparent. Tell us the requirement early, because it changes the design.
Who maintains the model after it launches?
Someone must, and it should be agreed before go-live. Models degrade as behaviour and data change, so the plan covers monitoring, retraining triggers and who decides when a model is retired. We can operate it or hand it over with runbooks, but an unmonitored model in production is a liability.
What happens when a prediction is wrong?
It will be wrong some of the time; a prediction is a probability, not a fact. The design question is what an error costs in each direction and what the process does about it - a review step, a threshold that favours caution, or a human decision for high-value cases. Systems that treat a score as certainty fail the first time conditions move.
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Data Platform & Analytics
Warehouse or lakehouse, tested transformations, a semantic layer and real governance, so reports agree with each other and every metric has an owner.
AIOps
Correlation, anomaly detection and noise reduction across metrics, logs and traces, with runbook automation kept under explicit human approval.
Start with an assessment
The fastest way to a useful answer is a short, scoped look at what you already have.