Data Science & Integrated Analytics

Yes, when the existing data can address the question. We begin with the decision you need to make, the outcome to understand, and an inventory of available sources. Survey files, customer records, transactions, engagement data, and open-ended feedback may each contribute different evidence. We review how they were collected, whom they cover, and what the fields mean before recommending an analysis. New research may still be needed if the available information omits a relevant audience, motivation, or part of the decision.

The number of records is only part of the assessment. We review missing values, duplicates, inconsistent definitions, time coverage, and whether the outcome and relevant groups are represented adequately. For a predictive model, the data must also contain information that would be available when a future prediction is made. Missing information may require a narrower question, additional data, or an explicitly qualified estimate. Filling gaps or adding model complexity does not resolve a mismatch between the data and the decision.

Sometimes, but the type of connection determines what can be concluded. A reliable shared identifier may support record-level linkage when the permitted uses allow it. Without one, sources may still be compared by a compatible period, geography, or audience group. Statistical matching may be considered when common variables and assumptions support it, but estimated relationships must remain distinguishable from observed records. We assess the connection before treating separate sources as evidence about the same person or event.

First, check whether they describe the same audience, behavior, and period. A survey may ask about category purchases while an account record covers only purchases from your business; an intention and a completed action are also different measures. We examine wording, coverage, recall, timing, and any record-linkage limitations before interpreting the discrepancy. Some differences reveal useful questions about customer behavior. Others reflect incompatible definitions or missing coverage. The analysis should explain the difference rather than force the sources to agree.

Driver analysis can identify factors associated with an outcome and help prioritize explanations to investigate. Those relationships do not by themselves establish what would happen if the business changed a factor. Other influences, differences between groups, or the timing of measurement may explain part of the relationship. If the decision requires evidence of an effect, we assess what experimental or comparison design is feasible. A before-and-after change or a strong predictor alone is not sufficient to establish causation.

Validation should reflect the model's intended use. For predictive work, that includes evaluating performance on data that did not guide model fitting or selection, with separation by time or customer where the application requires it. We also examine errors, performance across relevant groups, and sensitivity to analytical choices. A good fit to the original records is not enough. The readout should explain where the model performs adequately, where it struggles, and what still needs checking before it informs ongoing decisions.

The explanation should connect the business question to the data, method, assumptions, and result. We provide the diagnostics and sensitivity checks appropriate to the analysis, alongside a plain-language account of what the evidence supports. Technical detail can sit in an appendix while the main readout focuses on the decision and its uncertainties. We distinguish stable findings from conclusions that depend on a particular definition, model choice, or scenario, so your team can judge how much reliance to place on each recommendation.

That should be planned during scoping. A one-time decision may need documented scenario tables and a readout; repeated use may call for a scoring file, classification routine, simulator, or dashboard. We agree what inputs the output requires, who will maintain it, and what checks should accompany an update. Changing source definitions, customer behavior, or market conditions can affect its usefulness. Producing a model or tool does not by itself establish a maintained connection to your operational systems; that implementation needs its own agreed scope.