Data and forecasting
Machine learning and predictive analytics: forecasts from your own data
Your systems already record sales, breakdowns, complaints and lead times. Machine learning looks for the patterns in them and turns those into forecasts you can plan against.
Machine learning in plain terms
A machine learning model learns from examples. It is shown past data and what happened next; from that it works out rules nobody wrote by hand, then applies them to new situations. Predictive analytics is the most common use of the technique in business.
The output is never a certainty. It is an estimate, with a margin of error that can be measured. The real question is whether that estimate is reliable enough to improve a decision you already make.
What machine learning makes possible
A few typical uses, to be adapted to your data and your business.
Forecast demand
Estimate the volumes ahead so that stock, purchasing or staffing can be adjusted.
Anticipate churn
Spot the customers or subscribers showing signs of leaving while there is still time to act.
Monitor quality and maintenance
Detect drift on a production line or a machine before it becomes a defect or a breakdown.
Assess risk
Rank cases or transactions by level of risk, so that human attention goes where it matters.
What we deliver
- An audit of the data you actually have, and feature design
- A model trained, then validated against the number you chose
- A dashboard your teams will actually read
- Monitoring and retraining once the model is in production
Who this service is for
- You have historical data on the question, even if it is imperfect.
- A decision comes up again and again and currently rests on instinct or a spreadsheet.
- You can name the number that will show whether the forecast is useful.
With no historical data, machine learning has nothing to learn from. Discovery is there to check that before going any further.
How it works
Discovery starts with the data: what exists, where it sits and what state it is in. The proof of value then covers a single forecast, measured against a number fixed before we start. If it convinces you, the model is integrated with your systems, then monitored and retrained as part of the monthly service. You decide what happens after each step.
Frequently asked questions about AI as a Service
Do we need a lot of data for machine learning?
It depends on what you want to predict. The quality and depth of your history generally matter more than raw volume. The data audit carried out during discovery answers the question for your specific case.
Our data is scattered and incomplete. Is that a blocker?
It is a common situation. Discovery establishes what can be used as it stands and what cannot. Sometimes the conclusion is to start by collecting better data, and when that is the case, we say so.
How do we know a forecast can be trusted?
The model is tested on data it has never seen, and its error is measured against the number chosen at the outset. Once in production, measurement continues: if reliability drops, the model is retrained.
Is there a decision you would like to see coming?
Tell us which one and what data you hold. We reply within one working day.
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