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A data warehouse tells you what happened. It consolidates historical data into reports and dashboards that someone has to open, read, and interpret. Predictive analytics goes further: governed machine learning models forecast what is likely to happen next and recommend what to do about it – built on the same data foundation, not a replacement for it.
Already have a data warehouse or data lake? GEM builds predictive models directly on top of your existing data foundation, so nothing you have invested is thrown away
Consolidates historical data for reporting and dashboards Answers what happened, not what happens next Models, where they exist, are built ad hoc and rarely monitored Thresholds and rules are static, set once and rarely revisited Value depends on someone opening a report and interpreting it
Builds governed ML models on top of your data foundation to forecast outcomes Answers what is likely to happen next, and what to do about it Models are monitored continuously for drift and retrained on a governed cadence Thresholds and rules adapt as new data and outcomes arrive Predictions connect directly to workflows, with human approval on consequential decisions
Rather than replacing your data warehouse or data lake, GEM’s ML & Predictive Analytics Services add a governed modeling and prediction layer on top of your existing data foundation. Models are trained, evaluated, monitored, and connected to business workflows through a consistent governance framework.
Evaluate data readiness, identify the highest-value prediction use case, and define success metrics.
Build and validate the first predictive model against real data and real business decisions.
Deploy to production, monitor for drift, and extend model by model across the business.
Governed ML models replace manual forecasting and intuition-based decisions.
Predictions connect directly to workflows, reducing time from insight to action.
Streamlined management of data from multiple sources reduces complexity and increases efficiency.
Scalable data and model infrastructure lets the business expand predictive use cases without compromising performance.
Advanced analytics and ML together extract actionable, forward-looking insights from real-time and historical data.
Robust security measures and governance protocols protect data and ensure models remain compliant and auditable.
Traditional BI reporting summarizes what already happened. ML and predictive analytics builds models that forecast what is likely to happen next and recommend what to do about it, connected directly to your business workflows.
A governed data foundation helps, but is not always required upfront. GEM can build on your existing data warehouse or data lake, or start with the specific data needed for the first predictive use case and expand from there.
GEM selects the algorithm and framework based on the use case and data, commonly working with libraries such as scikit-learn, XGBoost, and TensorFlow, rather than defaulting to one technique for every problem.
Deployed models are monitored continuously against production data, with retraining triggered when performance falls outside agreed thresholds.
Yes. Predictions can be delivered into CRM, ERP, planning tools, or other line-of-business systems through governed APIs and integrations.
Model evaluation includes checks for fairness and bias across relevant groups, following governance practices aligned to references such as the NIST AI Risk Management Framework, with findings reviewed before a model goes into production.
Accuracy, drift, and cost are monitored continuously through the GEM Enterprise AI Governance Framework, with reporting reviewed on an agreed cadence.
New predictive use cases are added incrementally, reusing the data foundation, governance, and monitoring already established for earlier models.
GEM defines KPIs during the Assess phase, such as forecast accuracy, decision speed, and cost avoided, and measures results against a pre-agreed business case.
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