The system dating dictionary

Reports among teams might show different numbers due to inconsistent business logic.

Teams may even argue about the correct definition and defend their turf, perhaps because their definition makes their numbers look better. Once you have a data dictionary, it is a document that all staff can reference and be on the same page, it makes onboarding new staff easier, and the business intelligence (BI) team have crystal clear requirements for implementation of those metrics.

To be clear, here, we are not considering raw database table documentation although that is important too, but a higher-level list of business terms and metrics.

How does the business as a whole think of “user”, “revenue”, or “cost of acquisition”?

The list is probably not as long as you might expect.

That is because teams tend to have a relatively small set of metrics that they are trying to track and optimize with a relatively small set of levers at their disposal — for instance, online marketing might focus on a few key facets such as campaign, channel, spend, and segment.

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This is by no means the only process that will work but it has at least worked for me.

A data dictionary is a list of key terms and metrics with definitions, a business glossary.

While it is sounds simple, almost trivial, its ability to align the business and remove confusion can be profound.

While this sounds daunting, one approach is to go business team by business team and examine a sample of all their standard reports and dashboards.

List out all the axis labels from charts, column headers from report tables, and the dimensions of how data is pivoted.

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