Ask the supplier how they will decide what the model needs to predict and what users need to see in the predictive analytics dashboard. Some suppliers may expect you, as the client, to define this. A stronger approach is to start with the business decision the prediction needs to support. That gives the model and dashboard a clear purpose, rather than producing large amounts of data without a clear link to action.

Gartner’s research on decision intelligence platforms also argues that “data-driven” is the wrong starting point. Their recommendation is to become decision-centric instead: define the decision first, then build the data and analytics around it, rather than building dashboards and hoping better decisions follow. Gartner projects that by 2030, explicitly modelled business decisions will be five times more trusted and 80% faster than unmapped decisions.

At Eunoia, we start by mapping valuable decisions and the data available to support them. That gives the technical work a clear purpose and delivers concrete business outcomes.

The supplier should examine the quality and completeness of your data, as well as whether the historical data reflects the conditions you want the model to predict.

For example, imagine a churn model is trained only on customer data from a period when a major promotion was running. Customer behaviour during that period may be very different from normal behaviour. Once the promotion ends, the model could interpret perfectly normal changes in retention as signs of churn. Understanding the business context behind the data is therefore just as important as assessing its technical quality.

One approach we use to address this is testing the model on a representative sample of the client’s own data before committing to full delivery. This can reveal data-quality and feasibility issues that are difficult to identify through a high-level assessment alone.

Ask for an example of the documentation sent after delivery. It should explain the model’s intended use, assumptions, limitations and evaluation results in language your team can understand.

AWS’s model-card guidance provides a useful reference for documenting these details throughout a model’s life. A clear proposal should specify the documents you receive and who updates them when the model changes. “Documentation included” leaves too much room for interpretation.

Ask who will be able to access the model, how changes will be recorded and whether your team will be able to trace which data was used to train it. Databricks documents these capabilities through model permissions, versions and data lineage in Unity Catalog.

Your analytics partner should explain how they will configure controls for your organisation, rather than simply listing the technology available. If predictions influence business decisions, ask how people will review the outputs and investigate incorrect or unexpected predictions. Azure Machine Learning offers interpretability and fairness-assessment capabilities. The proposal should explain which controls and checks are relevant to your particular use case.

Get specific about what your team will be able to operate, troubleshoot and change once the project is complete. Ask who needs training, what knowledge will be transferred and which issues your team will handle itself versus escalating to the supplier.

Our handover approach includes documentation and knowledge transfer meetings so clients can support the solution themselves if they choose, with Eunoia providing support for more complex issues. Forecasting can run automatically, with agreed thresholds used to trigger retraining when needed. Your proposal should make that operating arrangement clear. Separately, clarify contractual rights to the delivered code and model components before signing.

A useful reference should explain the client’s problem, the constraints involved and how the solution was ultimately used by people in the business. Look for enough detail to judge whether the experience is relevant to your organisation.

For Alf Mizzi & Sons Marketing Group, Eunoia built a Databricks forecasting platform integrated with the existing SQL Server analytics environment. Forecasts appeared in familiar dashboards, and a forecasting framework was established within three months. The published case describes delivery and operational benefits without claiming a quantified accuracy uplift. Ask every supplier to distinguish measured results from project ambitions.

Request a breakdown covering discovery, data preparation, model development, integration and ongoing support. Check which platform running costs sit outside the consultancy fee and what happens if scope changes.

One red flag is a supplier offering both a fixed project price and a specific accuracy level before profiling your data. The issue is not the fixed price itself, but whether those commitments are based on evidence or on assumptions that have not yet been tested.

Ask who monitors performance, who receives alerts and who decides what action to take when predictions deteriorate. Agree how results will be compared with actual outcomes.

Microsoft’s monitoring guidance covers changing input data, prediction patterns and model performance, with configurable thresholds. Your partner should translate those capabilities into a practical support plan. That means specifying monitoring frequency, responsibilities and the circumstances that trigger investigation or retraining. Establish these arrangements before the project is signed off.

Use this table during proposal reviews as a checklist.

Be cautious when a proposal promises a specific accuracy percentage without explaining how accuracy will be measured or what the model will be compared against. Ask what happens if the model does not reach the agreed performance level.

Our advice is to establish that benchmark early. A supplier should help you understand how improvement will be measured, what different types of errors mean for the business and which level of performance would make the solution worthwhile. Without those answers, it is difficult to judge the value of the investment, however convincing the demonstration may look.

Look for relevant delivery experience, explicit responsibilities and credentials you can verify. Eunoia holds the Analytics on Microsoft Azure Specialisation, which includes an independent audit of analytics delivery practices.

Use credentials alongside the proposal’s evidence: a realistic feasibility process, clear handover and a plan for operating the model. Ask the people delivering the work to explain those commitments.

Our predictive analytics services cover business-question definition, data feasibility, model validation, operational deployment and governance. Bring these eight questions to the conversation so we can discuss how they apply to your decisions, data and delivery requirements.