Data sits at the heart of digital products and platforms and is a critical component of every other trend discussed here. Data is essential to value creation as an asset—but often is not treated as one.
Utilities are being buffeted with escalating demands from seemingly every stakeholder due to changing technological, regulatory, environmental, and societal factors. These compounding demands complicate traditional investment and operational decision-making, which requires more data and improved information across varying stakeholder groups. This would be challenging even if data and information were commonly understood and broadly available. For most utilities, that's not the case.
In response, many utilities are updating and cloud-enabling data platforms and hiring data scientists to address initial priority use cases such as preventative maintenance or vegetation management. These are important steps, and progress is being made on many fronts using a variety of technological capabilities and platforms—but these steps are insufficient for driving sustained value in a digital world.
Utilities applying advanced analytics face technological debt, decades of paper field records, and inadequate processes for managing data. It is commonly cited that more than 80% of "analytics" time is spent understanding (and often debating) the data itself, including its definition, quality, lineage, availability, and variance. Roles and responsibilities surrounding data stewardship are often misplaced or ill-defined. These two factors alone increase the time spent gathering, reconciling, and preparing data—which further inhibits the pace of analysis and action. The value creation opportunities range from the strategic, such as forecasting EV demand and introducing hydrogen supply, to the operational, such as better ticket association for improved outage management and customer propensity for low income or alternative energy programs.
What's happening in the industry
Three major trends escalate the need for enhanced utility analytics: (1) continued pace of technological advancement, (2) navigating the clean-energy transition, and (3) regulatory reporting requirements. There are also trends shaping analytics across other industries from an analytics enablement perspective, including the need for a chief data officer (CDO) or chief analytics officer (CAO) roles, need for formal data-fluency programs, and a sound and reasoned data governance structure.
Data processing and cybersecurity capabilities, combined with the capabilities inherent with today's sensors and meters, create the potential for a truly digital enterprise. Utilities are busy building the people, process, and technological capability to achieve this potential and to bridge the IT/ OT divide. But they still often approach it from a siloed traditional functional perspective, not yet treating data as an enterprise asset. As an example, meter data in the past was considered primarily for operations. Now, external third parties and internal customer experience teams are asking for it as well. From a technological and data perspective, analytic data products and accelerators are an increasing part of that landscape, from predictive analysis to consent management and disaggregation tools.
With the increase in clean energy demands, utilities have pressing needs for real-time monitoring and load balancing as well as long-term forecasting changes.
Third-party alternative energy providers are demanding more data to support their own marketing, planning, and data needs. Several states, such as New York, are mandating data exchanges whereby utilities must provide consistent data to be used by all stakeholders, from residential customers to alternative energy suppliers or regulators. They must also ensure security and proper consent management on these platforms. This is driving the need for improved data management and consistent definitions.
The newest emerging trend is with respect to regulatory demands. Pending SEC regulations call for all public firms to report their greenhouse gas impact. With other climate change regulations and the pending Build Back Better Act, the potential exists for a National Green Bank. This will create new financing vehicle opportunities, such as sustainability linked bonds. Unlike the traditional green bond, these funding vehicles require a specified level of operational performance in exchange for a lower interest rate. These regulatory trends will require reliable data with a defensible data lineage. Utilities have only begun to consider these broader future requirements that will put more pressure on their need for data savvy and data management.
Regardless of the individual driving factors, one truth is clear: Making sense of the emerging utility landscape requires a well-understood and well-managed set of data assets for the enterprise to enable analytics. Data fluency is the ability to cultivate and use data responsibly and effectively to guide decisions and actions to achieve business goals. The investments in enterprise data and analytic enablement require upskilling of employees from customer service to field operations.
In response, many utilities have formed analytics centers of excellence, though they vary in structure and approach. Many have not yet addressed the operating model and data governance components synergistically with their technology and data scientist investments to ensure sustained value creation. We have seen a four-fold increase in the number of firms requesting formal data governance program assistance with master data management across all industries. The collective requirement for creating analytics enablement is driving the rise of CDOs and CAOs. 73.7 percent of large firms across industries have designated CDOs or CAOs, both combined into one role. We expect this trend to gain traction this year in utilities.
How your utility can take action
Enabling analytics long term requires a concerted strategy and approach that is executive-driven, multi-disciplined, and cross-functional. Utilities can take a note from other industries and create a three-to-five-year data and analytic strategy that encompasses the business strategy based on the macroeconomic forces at play.
Four components required to enable analytic maturity for sustained value creation:
- Operating model: Consider a CDO to elevate the role of data to the status of enterprise asset. Define a data literacy or fluency program to upskill workers. At an MIT CDO event, data fluency was the most frequently raised discussion topic and most common barrier to value realization.17 Without employees' ability to understand, make sense, and work with the data, technological investments fall flat.
- Data governance: Formalize it. Long-term analytics enablement is not possible without significant improvement in data management so that employees can have confidence in the decision-making process.
- Analytics and insights: Prioritize your use cases and select a few to start that have high synergy with business strategy, create clear value, and where the data is believed to be of sufficient quality and availability to achieve success and build confidence.
- Modern data platform: Ensure that your platform is cloud-enabled, secure, and capable of real-time data streaming and effectively serving data at the speed required based on the use case. Mature your capabilities of serving data to third parties and ensuring they have the security controls to share data with you.
A well-defined road map with an incremental approach to enabling analytics capability is key to sustained value creation. Successful utilities recognize that the change demands enabling data as an enterprise asset at the heart of digital.
If you are interested in a personalized discussion regarding your data program, please feel free to contact author Penny Wand.