Addressing the challenges of analytics transformation: key questions every utility needs to ask
- May 21, 2018
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Jim Harvey, Glen Mannering, and Marley Urdanick, energy and utilities experts at PA Consulting Group, discuss the challenges of an analytics transformation.
Today’s utilities are becoming much more sophisticated about analytics. They are establishing enterprise organizations to support analytics and are advancing their capabilities in data governance, data management and data science. Many are executing use cases at scale and moving beyond pilots and proofs of concept to demonstrate to their leadership that the investment in enterprise analytics is paying off. Despite these advances, many companies are still struggling to overcome challenges to truly transform into an organization that incorporates analytics as an integral part of their culture.
At the Utility Analytics Institute’s 7th annual Utility Analytics Summit in Irvine, California last month, energy and utility professionals gathered to exchange best practices and learn about the latest trends in analytics. We heard about the challenges utilities are encountering when setting up an enterprise analytics organization, as well as the common struggles to execute and demonstrate value. Out of that, we compiled a number of key questions each utility needs to ask to ensure their enterprise analytics transformation program yields results.
- Do you know the value of your initiative and how to measure it? If an enterprise analytics initiative or even a specific use case hypothesis does not specifically identify the value and overall contribution to the utility’s strategic goals, then it should not be pursued. Similarly, if a use case does not clearly identify the value and a mechanism for measuring that value, then it should not be included in an analytics use case prioritized roadmap. Analytics use case value can be quantified in various ways for utilities, including, for example, increased savings, increased revenue, increased safety, reduced risk, improved operational efficiency, and improved reliability metrics. Some organizations outside of the utility industry have demonstrated measurement success via a Value Management Office; an organization that can independently evaluate, track and report on the projected value for each hypothesized analytics use case.
- What capabilities does your organization need to support analytics? A business-driven capability-based approach remains at the core of successful analytics program. Capabilities may include such things as strategy and prioritization, resources and their skillsets, business processes, funding mechanisms, governance, data quality, tools and technology, and leadership support. These capabilities need to be defined and aligned for an analytics program to be successful.
- What is your technology roadmap? The analytics technology landscape is somewhat fragmented with only a few truly enterprise options, dozens of “packaged analytics solutions” that address certain business areas (e.g., customer analytics) and hundreds of point solutions that address a single use case or a limited number of use cases. Utilities face a decision regarding a “best of breed” approach or an enterprise solution that may only cover a portion of their analytics requirements. Additionally, the underlying architecture that supports analytics is evolving with some utilities exploring cloud or hybrid cloud architectures. The technology approach will likely be different for each utility and, ultimately, organizations should establish a thorough understanding of their business goals and requirements for their analytics program before making decisions regarding technology (“what before how”).
- What is your organization’s capacity for transformation? Are you ready to change your organizational culture, the very “DNA” that defines your organization? A true analytics transformation will require a well-defined change and communications management roadmap to the future state, top-down and bottom-up stakeholder engagement, strong executive leadership support, end-to-end organizational enablement and an accelerated approach to decision making.
- Are you prepared to transform your operating model to better support analytics? Organizational enablement may require the establishment of a dedicated analytics support function. This can take the form of an “analytics center of excellence” or a “hub and spoke model” or a hybrid approach. A center of excellence consolidates a team of experts that facilitate and promote analytics across the organization. The hub and spoke model provides centralized support for analytics best practices, standards and tools, and advanced analytics in the “hub” and embeds data analysts in the various business units and corporate functions (the “spoke”) to work collaboratively with decision makers in those areas. The organizational approach will likely be different for each utility – the most appropriate model may depend on culture, politics, senior leadership support, funding and a host of other factors.
- Are you prepared to become “evangelists” for analytics? The success of a utility enterprise analytics program will depend, in part, on building awareness throughout the organization, awareness of what the analytics organization’s goals and capabilities are, and how those goals and capabilities will potentially benefit the organization. As part of their change management program, the enterprise analytics team should plan to over-communicate, both horizontally and vertically, throughout the utility. Additionally, the analytics team should pay attention to feedback and be prepared to explain (repeatedly) how the analytics team is supporting the utility’s overall strategy, mission and vision.
- Are you prepared for the challenge of competing for analytics talent? Competition across industries is fierce as organizations ramp up their analytics capabilities. Utilities that need to increase staffing in their analytics organization will have to work closely with their human resources department to create appropriate career paths, job categories and training programs specific to analytics and data scientists. This investment will pay dividends when talking to potential recruits. Additionally, utilities may discover “citizen data scientists” elsewhere in their own organization; these are analytics experts stuck in other roles that don’t necessarily reward their capabilities or provide advancement opportunities. These resources will be relieved to have a clear career path and opportunities to exercise their natural skillsets. Utilities should also consider partnering with local universities for recruiting and internships, and be prepared to invest in the development of those resources.
- How do you get started, build momentum, and get results? Some organizations may not have the support to deploy a full enterprise analytics program with a big bang approach. An evolutionary transformation might be in order. For example, your deployment roadmap could provide for a “crawl-walk-run” approach that can help you score some quick wins and build credibility. Start with a proof of concept and execute one use case or a logical grouping of use cases with low complexity and high value potential. After that success, proceed to a pilot, perhaps rolling out the analytics program to one business unit or functional area (e.g., customer operations). Remember to track, measure and evangelize your successes. At this point, other business units will likely be coming to you!
Utilities that tackle these challenges head-on when building their analytics organization will be poised to cultivate a culture of analytics that supports a broader analytics and digital transformation journey, and delivers promised value.
Jim Harvey, Glen Mannering, and Marley Urdanick are analytics and digital transformation experts at PA Consulting Group. PA provides analytics advisory services across industries; helping clients establish enterprise organizations that support analytics, developing the appropriate operating model for the analytics organizational structure, evaluating and evolving organizational capabilities, developing change management and communications plans for cultivating a culture of value-driven enterprise analytics, building models to evaluate potential use case value, and providing domain expertise for executing use case portfolios.