nanocoai/analyst

Check that the scheduled data work behind the reports actually ran and produced something sensible: the daily reshaping scripts, the API pulls, the tables the reports read from. Use each morning before anyone opens a report, when a report looks wrong and nobody knows whether it is the data or the query, after a schema or API change, when a new audience or source is added to the pipeline, or when a scheduled job failed or finished suspiciously fast.

Write the SQL or MongoDB query that answers a question and keeps answering it: the right grain, no fan out, parameterised window, checked against something known. Use when a number is needed that no report carries, when an existing query returns something that looks wrong, when a query is slow or expensive, or when somebody asks for a one off pull.

Stand up reporting for a new audience — an external client, a business unit, a region, an executive team — end to end: what they need to see, whether the data exists, access and scoping, their place in the pipeline, the standard report set, and validation against numbers they can check themselves. Use when a new audience is onboarded, when an existing one adds a site, a region or a business unit, when their reporting is rebuilt after a change on their side, or when they say the numbers do not match their own records.

Turn a request for a report or a dashboard widget into something buildable: the question it answers, the metric and its definition, where the data comes from, the query shape, and what it will not answer. Use when a new report or dashboard is requested, when an existing one needs a metric added or changed, when a widget is asked for in a sentence and needs pinning down, or when two reports disagree about the same number.

Fix data that has gone wrong and change shape without breaking what reads it: duplicates, nulls, inconsistent document shapes, a field that means two things, a column that has to change. Use when a report cannot be built because the data is not in the right shape, when duplicates, nulls or inconsistent shapes are producing wrong numbers, when a field is needed or has to change, before a migration, or when onboarding data from a new source that does not match the standard shape.