AI Consulting for Data Analytics

No — and waiting for one is the most common reason predictive analytics never starts. A first model needs one well-understood dataset with enough history and a clearly defined outcome variable. That can come from a warehouse, but it can just as easily come from an ERP export, a transactional database replica, or a well-maintained set of operational tables.

What a warehouse genuinely helps with is the second and third model, and industrialisation. So the honest sequence is: prove value where the data is already usable, then let the demonstrated value justify the platform investment. Where the foundation truly is the blocker, we say so and start with data strategy instead.

Usually not a blocker, but it does set the ceiling. It helps to be precise about which problem you have. Missing values and inconsistent formatting are routine and handled in preprocessing. Structural problems are harder: no reliable entity key across systems, outcomes recorded inconsistently over time, or a definition of the target that changed two years ago and was never backfilled.

The second category has to be addressed before modelling, because a model trained on an incoherent target learns the incoherence. Our data assessment names which category you are in before any development is committed, so you are not paying to discover it mid-project.

Our working assumption is build on it. Warehouses, BI tools, and semantic layers represent years of institutional definitions, and replacing them is a large, disruptive programme that rarely improves prediction quality by itself.

We are capable of implementing across common data platforms, orchestration tools, and BI front ends, and we design predictive outputs to land as tables, columns, or API responses that your existing tools consume natively. If we do think a component is a genuine constraint on what you want to do, we will explain the specific limitation and what changing it would buy you — rather than making replacement a precondition for working together.

This is where most analytics AI work quietly fails, so we scope it from the start. In practice, three delivery patterns cover nearly everything.

• Write back into the system of action. Scores land as fields in the CRM, ERP, or planning tool, so the user sees them where the decision is made and never visits a separate tool.
• Materialise into the BI layer. Forecasts become tables the existing semantic model reads, appearing alongside actuals in dashboards people already open.
• Trigger a workflow. An anomaly or threshold crossing creates a ticket, alert, or queue entry with the context needed to act.

We also define the human decision the output is meant to change. If nobody can name the decision, the model should not be built yet.

Whichever arrangement you choose, it is decided explicitly before deployment rather than discovered later. There are three common setups: your team owns operations after we train them; we retain a defined support and retraining role; or a split where your team handles routine monitoring and we handle substantive model changes.

Regardless of which applies, handover includes documented feature definitions, the retraining procedure and cadence, monitoring thresholds with a named owner, and a written description of when the model should be distrusted. A model without a maintenance owner degrades silently — that is the failure mode we design against.

It depends far more on your data model than on the language model. Over a curated semantic layer with unambiguous table and column names, documented metric definitions, and a bounded question scope, it is reliable enough for business users to self-serve routine questions. Over a raw schema with cryptic column names, several plausible revenue definitions, and undocumented join logic, it produces answers that look confident and are wrong.

So we treat it as a data-modelling project with an interface, not as a chatbot project. We also design for verifiability: the generated query is visible, results are traceable, and the assistant is scoped to defined metrics rather than being allowed to improvise across the whole warehouse. For genuinely ambiguous questions, the correct behaviour is to ask for clarification, not to guess.

Predictive analytics estimates a quantity or likelihood: this account has a high probability of churning, demand next month is likely to fall in this range. A human then decides what to do about it.

Prescriptive analytics goes further and recommends or takes the action — which retention offer to make, how much stock to order, which case to route where — usually by combining a predictive model with business constraints and an optimisation or rules layer. Prescriptive systems only work responsibly when the predictions underneath them are already trusted and monitored, which is why we treat it as the rung after predictive rather than a parallel option.

For seasonal business forecasting, several complete cycles of history is the practical starting point, because a model cannot learn an annual pattern it has only seen once. For classification problems such as churn or propensity, what matters more is the number of positive examples than the number of rows — a large table with very few recorded outcomes is a harder problem than a smaller, well-labelled one.

There are also cases where less data is workable: transfer learning, pre-trained models, and pooling across similar entities can all reduce requirements. The assessment gives you a direct answer for your data rather than a general rule, and that answer is sometimes that the honest move is to start collecting properly now.

That is usually the better outcome, and it is the arrangement we prefer. Analysts who already know your data, your definitions, and your business context have the hardest part covered — the modelling techniques are the teachable part.

Through the AI Academy we run practical training on feature engineering, validation design, model evaluation, and the failure modes that catch experienced SQL practitioners out, such as target leakage and inappropriate train/test splits on time-series data. In many engagements we build the first model jointly with your analysts specifically so the second one can be theirs.

By agreeing the comparison before building anything. Every predictive engagement starts by defining the current method — last year plus a growth factor, a manual rule, an experienced planner's judgement — and measuring its performance on historical data. That becomes the baseline.

The model is then evaluated on the same held-out periods with a metric chosen for the business consequence, not for how good it looks, and time-based splits rather than random ones where the data is temporal. If the model does not clearly beat the baseline, that is a legitimate proof-of-concept outcome and we report it as one. You get an evidence-based go or no-go, which is exactly what the fixed-price PoC stage exists to produce.