Solutions

Predictive Analysis Solutions for Enterprise Decision-Making

Predictive analysis turns enterprise data into forecasts, risk scores and demand models that teams can act on. DS Stream builds these models and runs them on production-grade MLOps, so predictions reach planners, category managers and commercial teams in CPG, retail and data-intensive businesses as a monitored, repeatable service.

Talk to our predictive analysis team

Key takeaways

DS Stream delivers predictive analysis as three model families: forecasting, risk scoring and demand models.
Models are built on production-grade MLOps, so they are deployed, monitored and retrained instead of ending as one-off analyses.
The solution is designed for enterprise data teams and the commercial decisions they support: demand planning, category, assortment, pricing and promotion.
DS Stream works technology-agnostically, building on the cloud and data platform a client already uses.
Predictive analysis connects to adjacent DS Stream solutions such as sales forecasting, promotion analytics and customer segmentation.

What is predictive analysis as a DS Stream solution?

It is a packaged capability that produces forecasts, risk scores and demand models from your enterprise data, and keeps them running in production. DS Stream designs the models, builds the pipelines around them and operates them with MLOps practices, so business teams receive current predictions rather than a one-time study.

What you get:

Forecasting models for volumes, sales and demand over the horizons your planning cycle uses.
Risk scoring that ranks customers, orders, accounts or operational events by likelihood.
Demand models that explain and anticipate how demand reacts to commercial decisions.
Production operations: deployment, monitoring and retraining handled as an engineering discipline, not a manual rerun.

Who is predictive analysis for?

It is built for enterprise data teams and the business functions they serve. Typical users are CPG and FMCG manufacturers, retail and e-commerce organizations, and data-intensive platforms in areas such as global trade intelligence, healthcare and telecommunications. Sponsors are usually a CDO, CTO or head of data and AI.

The solution fits best when:

Demand is volatile, product life cycles are short and supply chains are complex, as in FMCG and retail.
Category, assortment, pricing and promotion decisions are still made without predictive support.
Analytics work exists but models stall between pilot and production.
A central data team has to serve several commercial teams with the same predictions.

What changes once models run on production-grade MLOps?

Predictions stop depending on individual analysts and manual refreshes. Models are versioned, deployed and monitored, so the business sees the same numbers on a predictable schedule. Data teams spend their time on model quality and new use cases instead of rebuilding notebooks and reconciling results every planning cycle.

Operating a predictive model as a product means:

A defined pipeline from source data to a scored output.
Monitoring of model behaviour and data drift.
Retraining and promotion handled with MLOps practices.
Governance over how models are approved and used, supported by AI model governance.

What we deliver

Capabilities included in the predictive analysis solution

Each capability is delivered as a working model plus the pipeline and operations that keep it current.

Forecasting models

Volume, sales and demand forecasts aligned to your planning horizon
Forecasts delivered into the reporting and planning tools teams already use
Extends naturally into [sales forecasting](/solution/sales-forecasting) for commercial planning

Risk scoring

Scores that rank customers, orders or operational events by likelihood
Clear model outputs that a business owner can interpret and act on
Scheduled scoring runs with monitored inputs and outputs

Demand models

Models of how demand responds to commercial levers
Support for category, assortment, pricing and promotion decisions
Connects to [promotion analytics](/solution/promotion-analytics) and [category management analysis](/solution/category-management-analysis)

Production-grade MLOps

Deployment, monitoring and retraining of each model in your environment
Pipelines built with [machine learning pipeline](/services/machine-learning-pipeline) and [ML monitoring](/services/ml-monitoring) practices
Technology-agnostic delivery on your existing cloud and data platform

Data foundation for prediction

Feature and training data served from governed pipelines
Data quality checks before data reaches a model, supported by [AI data quality](/services/ai-data-quality)
Reuse of your current [data engineering](/services/data-engineering) stack wherever possible

Frequently asked questions about predictive analysis

Is this a platform or a delivery engagement?

It is a delivered capability. DS Stream builds the predictive models your use case needs, wires them into pipelines and operates them with MLOps practices. There is no separate product to license: the forecasting, risk scoring or demand models run inside your own cloud and data environment.

Which cloud and data platforms do you work with?

DS Stream takes a technology-agnostic approach and builds on the platform you already run. Where a client standardises on a specific stack, the solution is implemented there; for lakehouse-centred programmes it pairs with our Databricks and cloud data work.

What data do we need before starting?

You need the business history the prediction depends on, such as sales, demand, customer or operational records, plus access to the systems that hold it. Where quality or pipelines are the blocker, AI data quality and data pipeline architecture are addressed as part of the work.

How is this different from your data science service?

Data Science describes how DS Stream runs analytics work across an enterprise. This page describes a specific outcome: forecasting, risk scoring and demand models in production. Many clients buy the solution and draw on the service team to extend it to further use cases.

Who owns and runs the models afterwards?

The models, pipelines and operational setup live in your environment, so your data team owns them. DS Stream can continue to operate or extend them, using MLOps implementation and ML monitoring practices, depending on how much you want to run in-house.

Can predictive analysis support more than one business team?

Yes. One predictive foundation usually serves several teams: demand planning, category and commercial analytics, and marketing. Outputs can be routed into dedicated solutions such as revenue analytics, performance analytics or business intelligence reporting.