The system dating dictionary

Ask the business team to take a look over the list, especially their section and add any terms that are missing.If they have a robust set of dashboards and reports, you probably have a comprehensive list.Organize and group the list by business function, such as financial metrics, marketing metrics, customer service metrics etc.You may also want to break out really generic dimensions (“year”, “product_id”, “country” etc) that span across many teams to their own section too.In this post, I’ll detail some best practices surrounding data dictionaries and process of how to create one.This is by no means the only process that will work but it has at least worked for me.Thus, a report showing revenue by territory (for some time period) yields two key terms: “revenue” and “territory”.

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The BI team should now make a first pass at trying to collate or create definitions.Dating terms were once packaged in a palpable set that included “pinning,” “necking” and the like.Over the years, this collection has expanded to what it is today: A dizzying, infinite scroll of words like “fuckboy” and “thirst trap.” Clearly, things have gotten more complicated — and a bit more crass.In fact, a data dictionary is possibly one of the most valuable artifacts that a data team can deliver to the business.Most businesses have at least one concept, term, or metric that is used or interpreted differently among teams. Decision makers may disagree about what the data show and what actions to take.

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