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Terms like “business intelligence” and “data analytics” mean different things in different contexts and the only way to hack through the Forest of Jargon is with a Machete of Specificity. Whether you’re building an analytics tool, shopping for a business intelligence application, or just looking to get a better handle on IT terms, it’s useful to be familiar with the analytics spectrum. In this article, we’re going to focus on disambiguating the term data analytics by breaking it down into types and aligning those with business objectives.
Data analytics is the process of extracting, transforming, loading, modelling, and drawing conclusions from data to make decisions. It’s the “drawing conclusions” bit that BI tools are most concerned with, as the extracting, transforming, and loading steps generally happen at the database level. There are four ways of making sense out of data once it’s been formatted for reporting, and these are descriptive, diagnostic, predictive, and prescriptive analytics.
Descriptive and diagnostic analytics help you construct a narrative of the past while predictive and prescriptive analytics help you envision a possible future. The above diagram shows examples of features that would fall into each of the four categories, along with the types of questions those features are designed to help answer.
Descriptive analytics comprise your reporting …
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