Four ways to engage one practitioner.
Most engagements start as a conversation rather than a service line, and most clients draw on more than one.
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Forecasting and capacity planning
Demand, capacity and FTE forecasting for operational functions, with the Power BI reporting that turns a forecast into a plan leadership will act on.
Engagement
Usually a fixed-scope build of the models and pipeline, then a retained arrangement through the first planning cycles so the forecast is reviewed by the person who built it.
Forecasting in detail -
Data and analytics engineering
Analytics engineering in dbt, legacy-to-cloud migrations with reconciliation, and the automation and integrations that keep pipelines running unattended.
Engagement
Typically fixed-scope with a defined end point, for example a migration or an integration, followed by a short retained period while the team takes ownership.
Data engineering in detail -
Advisory and embedded leadership
Retained or embedded senior data capacity for teams that need experience rather than headcount: model review, analyst mentoring and operational planning.
Engagement
Usually a retained arrangement with a standing number of days per month, reviewed at agreed points. Embedded arrangements, where I sit inside the team for a defined period, are also common.
Advisory in detail -
AI code and output review
Independent review of AI-generated SQL, Python and R before it reaches production or a decision. Fluent enough to use it, sceptical enough to catch errors.
Engagement
Often bundled into an advisory arrangement as a standing review cadence. One-off reviews of a specific model, pipeline or report are the lowest-commitment way to start.
AI review in detail
Next step
Not sure which service line applies?
Describe the problem in a few lines, and I will say which of these fits, or that none of them does, and what I would suggest instead.