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Utility Analytics Is No Longer about the Enterprise Platform – It’s about What You DO with the Analytics!

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Tom Martin's picture
Managing Director of Product Management, Energy & Utilities TROVE Predictive Data Science

Mr. Tom Martin is presently Managing Director of Product Management, Energy & Utilities at Trove - Predictive Data Science since Aug 2018 and is based out of Seattle. Trove is an information...

  • Member since 2018
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  • Jan 27, 2019 8:45 pm GMT

This item is part of the Special Issue - 2019-01 - Predictions & Trends, click here for more

As we look ahead to what 2019 has in store for utilities, there is a major Transmission & Distribution operations trend taking shape: utilities are beginning to put their data to use in high-powered use cases at a rate much faster than the trickle we’ve seen over the past several years.

For the past few years, utility analytics have been defined by the search for a magical self-serve analytics platform that can be deployed enterprise-wide and serve a huge multitude of utility needs. While an enterprise-level platform may eventually offer cost savings, the problem with this approach is that, as Winston Churchill said, “perfection is the enemy of progress.” Often these enterprise-level approaches spend too much time trying to get every single detail perfect before they begin to act on even the most straightforward use cases. What a lost opportunity!

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However, in 2019, utilities are ready to press Pause on the hype of a giant analytics platform and are instead investing significant attention to the ramp up of targeted individual analytics use cases. The promise of the magical platform has evolved into the more impactful question of “so what exactly should we be doing with analytics, and how is it going to help us and our customers?”

The interesting thing about this movement away from the platform focus to a use-case focus is that there are a lot of use cases utilities can deploy today – with no expensive changes to what data they collect, no major new processes, and no significant change management needed to operationalize the analytics. Some examples of these easy-to-implement use cases are:

  1. Instead of replacing assets or performing vegetation management with a cadence-based approach, using historic outage information and other existing utility information, utilities can use a data-driven risk score to replace the assets most likely to fail within the next year or identify the “danger trees” most likely to cause an outage.
  2. Utilities are using analytics to pre-stage crews in the areas that the analytics determine will have the highest concentration of outages during an upcoming storm and in doing so, reducing the cost of the storm restoration while also getting their customers back online faster.
  3. Instead of deploying new protection assets in an evenly distributed, “peanut butter spread” fashion, utilities are using data-driven approach to identify the specific locations that will return the best safety and reliability benefits for a given cost, allowing them to optimize their capital deployment.

The big trend for 2019 is that utilities have realized they don’t have to wait for the long, expensive deployment of a self-serve analytics platform and instead can act now to capture immediate value from the low-hanging fruit of data-driven use cases. In 2019, a large number of utilities will begin to turn to the quick wins that data science has to offer for their Transmission and Distribution use cases.

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