Data science consulting: turn business data into useful decisions
Data science consulting connects the meaning of your data to a decision, analysis or predictive model that a team can use. A useful project connects the prediction to a feasible response: ordering stock, checking a supplier, scheduling capacity or contacting a customer. We choose the target, prediction horizon and operating threshold before selecting the model.
Data science starts with the meaning of the decision
Our manufacturing purchase-order work starts with a familiar operating problem: a buyer needs to identify delayed orders early enough to intervene. The published project describes vendor history, logistics, seasonality and other relevant signals. A separate manufacturing case compares products through bills of materials and material descriptions, helping teams investigate similar products across sites. The clients remain confidential.
These are different data-science questions. Delay prediction requires a clear event and a reliable historical snapshot. Product similarity requires a representation that respects product meaning; a cluster is useful only when an engineer can interpret the comparison. Our work therefore joins data preparation, domain review and model evaluation rather than handing a cleaned spreadsheet to an isolated algorithm exercise.
Compare model families on a common task
The ESA account of EGNOS performance-prediction research describes comparisons including XGBoost, LightGBM, CatBoost and neural approaches; LightGBM was selected for the stated prediction tasks after considering performance and training time. Our company portfolio describes our modelling contribution within the Integricom-coordinated project. This is a specific project decision, not a universal ranking of those tools.
Choose the method against sample size, missingness, error costs and operating constraints; complexity needs a useful advantage.
Choose the approach that fits the decision
| Option | Useful when | Trade-off to test |
|---|---|---|
| Forecasting | A team needs quantities over a defined horizon | Accuracy at the level where decisions are made |
| Classification or ranking | A limited team must prioritise cases | Missed cases versus unnecessary interventions |
| Rules and statistical baseline | The process has stable, simple relationships | Whether added model complexity changes decisions |
| Similarity and clustering | Comparable products or records need investigation | Meaningful representation and expert interpretation |
| Reporting and data quality | Definitions or records prevent a reliable decision | Reconciliation, lineage and usable measures |
Define the target before assembling the dataset
For delayed orders, agree when an order becomes late and which snapshot a buyer would see. For BOM similarity, agree what makes two products meaningfully comparable. Include known changes in data capture and operating policy; those definitions can change the analysis more than an algorithm parameter.
From raw records to a defensible analytical result
- Reconstruct the decision-time dataset and document exclusions, missingness and target meaning.
- Create a practical reference analysis before exploring richer features or model families.
- Compare alternatives on separated periods, sites or entities that reflect intended use.
- Deliver interpretable error cases and a decision recommendation, including when further data work is the better next step.
Measure the decision your analysis supports
For a delay queue, useful warnings within buyer capacity matter alongside missed critical orders. For a forecast, measure error at the planning horizon and operating level. For product clustering, have engineers confirm useful comparisons. These measures make analytical quality reviewable without assigning fictional revenue to a model score.
Sample: an illustrative evaluation plan
Sample — illustrative scenario, not a client result. For illustration, use 12 months for initial development and a later three-month period for evaluation, if the data history supports that split. Compare a seasonal reference forecast with a model using only information available at the forecast date. Report peak-period error, stock constraints and the decision's cost. These periods are Sample design choices, not a claim that every business has enough data.
For the wider choice of model and delivery partner, see our AI development guide. Its agency-selection questions help compare complete proposals.
What you receive
A data dictionary, target definition, reproducible preparation workflow, baseline comparison and decision-focused evaluation report. Include relevant segment or time-period results, known limitations, model or analytical outputs and a handover plan. Data science can conclude that a process or data repair is more valuable than another model.
Questions buyers ask
- Is high accuracy enough?
- No. Value depends on error costs and whether the business can use the prediction.
- Can a model explain its recommendation?
- We select explanation methods suited to the model and audience, and test whether they support review. An explanation is not proof of causation.
- How often should we retrain?
- Use observed performance, data changes and business events. A fixed calendar alone may miss drift or trigger unnecessary work.
Our reference portfolio includes manufacturing delay prediction and customer modelling. Each case has its own scope and evidence. Explore relevant industry applications or our AI development approach.
Discuss your data science project. Bring a short, non-confidential description of the decision, current process and available data.