E-Handbook: Data modeling best practices power analytics, business apps Article 1 of 4

Data models need ongoing attention from IT teams, end users

Data models map the relationships between data entities and attributes in a way that "captures the business meaning of data," according to consultant Peter Aiken. And they need to change as the makeup of an organization's data changes, he said in a Dataversity webinar on data architecture and data modeling best practices.

Data models "are a living document; they should be evolving slowly over time," said Aiken, founding director of Data Blueprint. Keeping models up to date helps ensure that business data "will be useful as long as you're collecting it," he explained.

That applies whether you're using traditional data modeling tools or new platforms that enable data analysts to model data for BI uses. Also, business users should be involved in the modeling process, even if it means introducing them to concepts that "might seem too nerdy" at first, said Donna Burbank, managing director of consulting firm Global Data Strategy, in another Dataversity webinar.

This handbook provides more advice on managing data modeling projects. First, we examine the growing complexity of modeling and curating analytics data sets from various sources. Next, we detail tips on data modeling best practices from the webinar with Burbank and co-presenter Becky Russell, national lead for data standards at England's Environment Agency. We close with a look at common data modeling and curation challenges and how to overcome them.

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