How is IT’s role adapting to lead a utility analytics culture?

How is IT’s role adapting to lead a utility analytics culture?

Last week I was a panelist at the Utility Analytics Summit 2018 in Irvine, CA. This conference brought together IT and business leaders to share trends, insights, and challenges as they leverage data insights in their organizations. Below, I share some specific areas where the IT role is evolving in this exciting area.

  1. From retrospective data reports and metrics, toward forward-looking actionable data insights.
    What I heard: “We’ve started calling it ‘Applied’ Analytics internally, because nothing we do matters if it doesn’t get into operational use.” 

    Stemming from a culture of high-reliability operations and complex engineering roots, distribution utilities have an insatiable appetite for data reports and performance metrics. In practice, these insights are meant to prioritize and manage decisions around resources and work orders. Unfortunately, this information only answers the question of “where have we been?” in hope that the staff can infer trends, trace root cause issues, and seek to improve operations looking ahead.Building from the foundation, utilities are now deploying data analytics solutions that aim to prescribe and predict issues leveraging historical information, multiple data sources, and statistical models. By attempting to answer the question of “what should I do next?”, utilities reduce the guesswork involved with issue identification, and can focus on the solutions. Utilities taking this predictive approach are justifying the investments through improved operational efficiencies (reduced truck-rolls) and performance metrics (SAIDI/SAIFI) as they look to optimize their asset/resources.

  2. From big-bang deployments, toward smaller and more flexible proof-of-concepts that solve specific problems.
    What I heard: “Waterfall is dead when it comes to analytics project deployments at our utility. Agile is the new norm.” 

    The standard practice in technology deployments is to establish the design requirements upfront, then development, and finally deployment to the users. This approach serves well to maintain scope and budget controls, reducing risk, and maintaining expectations, but can prematurely lock project teams into requirements or functionality that may no longer be relevant.However, the industry trend is toward agile development lifecycles for utility analytic deployments. With an agile mindset, IT works closer to the stakeholders to deconstruct their problems into root cause conditions and leading indicators that can be applied to the datasets. This flexibly allows utilities to test and validate successful outcomes to carry the momentum and justify the roll-out, such as with the data itself (backcasting) and small-scale deployments. By adapting and course-correcting along the deployment path, utilities can avoid time and costs building and maintaining irrelevant functionality and tools.

  3. From localized analytics pockets across teams, toward a centralized enterprise-wide position in analytics deployments.
    What I heard: “We had hidden data factories in all corners of our organization, and realized that we needed to drive analytics from the top to be successful.”

    The natural extension of deploying grid modernization operational technologies is for teams to leverage the datasets to drive insights. Over time, this tends to create technology – and team -specific knowledge within the business teams that, for the best of intentions, are not shared with the broader organization.To combat this trend, utilities’ IT leaders are now assuming a central position in the analytics use cases, data, and vendors. The IT benefits of this approach are maintaining data governance, maximizing economies of scale in shared assets, and prioritizing the analytic use cases that deliver the most value. In name, utilities are often referring to these central groups as an Analytics Center of Excellence (CoE).

Conclusion

Overall, I think it’s safe to say that the utilities industry is universally shifting toward a data analytics driven mindset. The value and financial benefits from this shift are becoming widely accepted by the industry and regulatory stakeholders. This is a unique trend for an industry that tends to be extremely fragmented, and regional when it comes to technology adoption (think: smart grid, DERs adoption). At the forefront, utility IT teams have a critical role in leading the utility analytics culture as a partner to the business teams!

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