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Defining Your Data: Driving Enterprise Analytics

By in Analytics | June 11, 2018

If you consider the word “apple,” what is the first thing that comes to mind? What are the attributes? I have asked this very question in numerous presentations and meetings. Did you think of iOS, your iPad your iPhone? Perhaps you thought of a red delicious apple or a granny smith apple? When surveying the audience, the typical response is half-and-half, iOS and the actual apple you eat. Both answers are correct. With just having the word “apple” and providing no contextual meaning, it is up to the individual for interpretation. This is the very reason why enterprise analytics are such a challenge within the utility industry and repeatedly custom solutions are developed.

From current research of clients and industry input, a utility spends about 80% of the analytic “project” doing data “janitoring” and only 20% analysis. I believe it is safe to say that of that 80% of “janitoring” of the data is just trying to interpret the meaning and find it.

What is the solution?

A utility needs to define the meaning of its data constantly throughout the organization by establishing what we at Xtensible refer to as an Enterprise Semantic Model (ESM).

Consider the definition of the word “semantic,” which is the meaning within a language. Now consider the definition of a semantic model; it is to structure data in order to represent it in a specific logical way. It is a conceptual data model that includes semantic information that adds a basic meaning to the data and the relationships that lie between them. In simpler terms, it is defining the meaning and “connecting the dots.”

If an organization defines what a work order is or an electric asset throughout the origination, this will support not only integrations between systems but also support enterprise analytics. This results in less wasted time “janitoring” the data, more productive time and sound investment on acting on the insights.

That is why we say that smart grids are paved with smart data; the only way to have smart data is to know and understand your data. This is accomplished by using the semantic model to drive integration and analytics, and governed through an effective Enterprise Information Management (EIM) program.

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