NOTE: This is a two part series exploring the opportunities presented by data integration in utilities and why a blueprint is needed to guide the utility’s vision during implementation.

Why do utilities need data integration?
Data is a source of value. In fact, it’s the precursor of knowledge. Data must be assimilated and analyzed in relation to hypotheses to reveal the insights that, when acted upon, become knowledge-driven decisions. Unfortunately, due to a historic lack of visibility into data, utility companies tend to dismiss its true business value.

As smart meters, smart devices, and other smart technologies are deployed in electric utility companies, the massive data they invite must be treated “smartly”—as a corporate asset—rather than a data storage initiative. This requires a blueprint; in other words, a vision for the role that data can play in driving business objectives for the utility.

The need for “a single view of the truth” has been bandied around for quite a while as data has multiplied across systems and industries around the globe. But the lack of data integration deployed to deliver on this mission has shown that the value and role of data is sorely misunderstood. Before the idea of building a blueprint for utility data can become foundational to business transformation, a few questions must be answered.

What’s the point of integrating data?
Data that is used by various departments is often stored more than once in siloed systems. As soon as the source data is manipulated in relation to a department hypothesis or question, its integrity for other uses is compromised. Imagine if this manipulated data is then passed on to another department as source and applied to a new hypothesis. The information derived could be very different than had the data source been in its original form.

The point of integrating data is to relate it to other data to find irregularities and diagnose issues that jeopardize the utility’s ability to meet objectives, such as complying with regulatory mandates, improving reliability, and ensuring quality of service. Another reason is to avoid duplication that can skew results, or introduce doubt in the data’s veracity.

Let’s take, for example, a transformer that’s coming close to end of life. If the utility’s weather data isn’t integrated with load management to show that the heat wave coming tomorrow will overload its capacity, an outage could occur with restoration requiring a full equipment replacement that could take hours—or even days. With integration, analytics using the integrated data could have identified the issue and preparations made to reroute the load on the feeders, or a crew could have been dispatched to replace the transformer in advance of the event.

There are many such scenarios to illustrate the business value of integrating data. Asking the question, how do we know if billing is accurate if we cannot correlate it to energy use?, is another possibility that could keep most utilities busy for quite awhile.

What other opportunities are you seeing from data integration at your utility?

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