3 reasons why utilities need to improve their approach to analytics—now

Offering Manager, Utility Analytics, IBM

Utilities need to start working on an enterprise-wide analytics strategy—and they need to start now.

Many utilities have been exploring analytics strategies for years, but—incredibly—quite a few still approach analytics just as they did 15 years ago. IDC Energy Insights surveyed 50 North American utilities that have initiatives in big data and analytics, and not a single one attained to the most advanced level of the IDC big data and analytics maturity model.

By contrast, other utilities are light-years ahead. IBM has helped many utilities, including some in the UK, the Netherlands and California, define, prioritize and implement enterprise-wide analytics roadmaps. But some utilities still have trouble seeing the importance of advancing the analytics maturity model.

It is high time that utilities approach coordinated enterprise-wide analytics as a “must-implement.” And there are at least three reasons why, each of them compelling.

Rationale #1: The utilities business model is being challenged

The utilities industry has undergone a transformational shift—one that has made effective use of analytics across the enterprise crucial for successful adaptation. The explosion of big data is one of the most visible aspects of this transformation. Just look at synchrophasors and smart meters, together likely to start generating hundreds of terabytes of data every year—or look at unstructured text data compiled from maintenance records and Twitter feeds. The accuracy, breadth and depth of these new data points presents new opportunities for utilities that are prepared to take advantage of them.

The IBM white paper The future of energy and utilities: An IBM point of view discusses other aspects of industry transformation and game-changing technologies, proposing three strategic imperatives to help utilities successfully tackle these new industry challenges. Particularly noteworthy is the call to take advantage of the big data explosion: “Disruptively innovate business processes through analytics-driven operational excellence.” Read the entire white paper for an in-depth look at this imperative explaining how an analytics strategy can help in adapting to industry changes.

Put differently, this call to action says that data is the new gold. Utilities need to leverage big data analytics to help them translate data into actionable insights, enabling better operational decisions. Tangible examples include predictive maintenance, power quality optimization and demand response analytics—among many others.

Rationale #2: Utilities have much room for growth in analytics

According to IDC Energy Insights, utilities have long used data analytics to address use cases such as demand forecasting or energy trading, but “given the number of other issues competing for the attention of utilities, many utilities are still in the early stages of understanding the full potential for analytics.”

The IDC Energy Insights, "Business Strategy: IDC MaturityScape Benchmark — Big Data and Analytics in Utilities in North America," defines five maturity stages, ranging from the “ad hoc” stage—the lowest stage, in which “use of analytics is experimental and used in silos”—to the “optimized” stage, which features “coordinated and optimized used of analytics across business processes.” More than four times as many utilities are in the lowest two stages of maturity (“ad hoc” and “opportunistic”) as are in the second-highest stage (“managed”), whereas nearly two-thirds of utilities are in the middle stage (“repeatable”).

In February 2015, the IDC Vertical IT and Communications Survey revealed that 47 percent of surveyed utilities had not yet pursued deployment of big data and analytics initiatives. Subsequent discussions with utilities have tended to confirm these findings. Utilities are only now discovering the value of applying analytics cross-enterprise, with much untapped potential yet to be realized. Indeed, this is where the concept of an enterprise-wide analytics roadmap comes in—everyone must start somewhere, and defining a clear vision to start an analytics journey is the first step to reaching the optimized stage.

Rationale #3: A coordinated cross-enterprise vision can help reduce IT costs

Utilities have acquired a significant number of analytics solutions over the past few decades, broadening an increasingly heterogeneous IT landscape. For example, IBM recently started working with a multistate US utility. Although other utilities had focused on a business-driven analytics strategy, this utility’s interest in an enterprise-wide analytics roadmap was largely influenced by the utility’s desire to address an IT challenge.

The utility used about 25 different point solutions, each addressing a limited set of independent business challenges by solving one or a few specific analytics use cases: analytics components of EAM, ERP, specialized industry applications, generic statistical engines and the like. All the tools were useful, each serving a specific purpose, but scaling and supporting 25 different analytical products was very challenging for an IT organization, the utility admitted: “Growing skills, providing support, extending capabilities ... It’s just not something that we see ourselves sustainably doing in the future.”

I found the utility’s frank admission very telling. Its analytics had been adopted ad hoc over the years, but using coordinated, planned analytics technology deployment to reduce costs and improve agility was becoming an IT imperative even apart from the business advantage that doing so would offer.

A number of perspectives are converging as more and more industry participants consider the importance of building an enterprise-wide analytics roadmap. The question I get more than any other after conversations with utilities is: “Can you give an example of a utility that successfully started an analytics journey?” And I can. In my next post, I’ll discuss how to get your organization started on the path to analytics maturity.

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