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Predictive Analytics

Forecasts and risk scores that are trusted enough to act on.

The hard part of predictive analytics is rarely the algorithm. It is choosing a prediction that changes a decision, getting reliable data for it, and running the model in production with monitoring, retraining and clear ownership.

We build models with the business owner from the start and design the decision they will feed before writing code.

The problem

What usually gets in the way

  • Models are built by data scientists but never used by the business.
  • Forecasts live in spreadsheets with no record of accuracy.
  • Models degrade silently when the data or market changes.
  • Regulated decisions need explanations the model cannot give.

What we deliver

Capabilities

01

Use-case framing

Define the decision, the prediction, the action and the value of being right.

02

Feature and data engineering

Reliable, reusable features from governed data.

03

Forecasting and classification

Demand, revenue, churn, risk and failure models suited to the data available.

04

Explainability

Explanations for each prediction where decisions must be justified.

05

MLOps

Versioning, deployment, monitoring and retraining as a managed process.

06

Decision integration

Scores delivered into the workflow where the decision is made.

Delivered by

Services involved

The service lines combined to deliver this solution.

How it fits together

Reference architecture

A vendor-neutral outline. Components are selected for your workloads, skills and regulatory context.

  1. DecisionScores in business appsPlanning toolsAlerts
  2. Model servingBatch and real-time scoringExplanationsA/B and champion-challenger
  3. MLOpsExperiment trackingModel registryDrift monitoring
  4. DataFeature storeGoverned training dataLabelled outcomes

How we engage

Start small, prove value, scale

  1. 01Assess

    2–4 weeks

    Rank use cases by value and data readiness.

    • Use-case ranking
    • Data assessment
    • Success criteria

  2. 02Pilot

    8–12 weeks

    Build and deploy one model into a live decision.

    • Production model
    • Accuracy vs. baseline
    • Monitoring

  3. 03Scale

    Ongoing

    Industrialise MLOps and add models on the same platform.

    • Model portfolio
    • Retraining process
    • Value tracking

Outcomes

What we measure

  • Forecast accuracy vs. current method
  • Share of decisions using model output
  • Time from idea to production model
  • Models monitored for drift
  • Value from improved decisions

Where it applies

Example use cases

  • Telecommunications

    Churn prediction with retention-offer targeting.

  • Retail

    Store and SKU demand forecasting.

  • Energy and Utilities

    Equipment failure prediction for maintenance planning.

  • Banking

    Early-warning credit-risk scoring with explanations.

Accelerators

Reusable assets we are building

Delivered today as services. These accelerators are being developed from that work to shorten future engagements.

In development

Forecasting starter

Reusable pipeline for time-series forecasting with backtesting.

In development

Model monitoring pack

Drift, accuracy and fairness checks with alerting.

Technology

Platforms we design around

Vendor-neutral: examples of platforms commonly used for this solution. Names identify technologies only and do not imply a partnership.

  • Databricks
  • MLflow
  • Dataiku
  • scikit-learn
  • PyTorch
  • SAS Viya
  • H2O.ai
  • Snowflake
See the full technology ecosystem →

Insights

Further reading

Databricks for Pipelines, ML and LLMs

Databricks' real strength is that pipelines, models, agents and their traces share one governance layer. The price is that its newest AI features are serverless, billed on separate lines and unevenly available by region. Adopt it workload by workload, not wholesale.

Read →

Selecting AI Use Cases That Pay Off

Most AI portfolios are built from brainstormed ideas ranked on a value-feasibility grid. Better portfolios start from the work itself, and from one question most grids never ask: how cheaply can the output be checked?

Read →

Measuring the Value of Enterprise AI

Most AI value claims are not wrong so much as unproven. The fix is to design the measurement before the build: a baseline, a credible comparison group and metrics finance has already agreed to accept.

Read →

Talk to us about Predictive Analytics

Start with a short assessment: we will rank the opportunities, check readiness and agree how success will be measured.

Start a conversation

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