Contributed by Michael Juchno, Ernst & Young LLP
Utilities widely acknowledge the importance of modernizing the grid, investing approximately $51 billion annually, according to Statista. Yet, a matching investment in data has been slower to materialize. To realize the return on these grid modernization efforts, a strong data strategy leveraging the strengths of people, process, technology, and data must be prioritized.
Data should be viewed as a strategic asset that fulfills regulatory requirements and enhances business outcomes, driving value across an entire organization. The most recent EY Power and Utilities Data Governance Survey found that 75% of responding US utility executives cited adoption and cultural buy-in as their biggest challenge in building a data governance plan. Utilities that can approach this effort with a sense of urgency should have financial support to back up their work. Fortunately, a forthcoming infusion of capital into the sector through federal grants, credits and other legislative incentives will help utilities address immediate needs and build models that leverage the power of data.
Slower progress toward data maturity
The EY survey found that, while 75% of respondents are committed to improving data governance in their organizations, less than half report a “developing” level of data maturity (43%) or have a formal data strategy (42%). By harnessing the benefits of an improved data strategy, utilities could improve worker productivity, increase operational efficiencies, lower operational expenditures, and innovate new products and services.
Early adoption of data foundations is one way to do this, establishing a framework to enable building blocks of data strategy and governance that is led by both business and IT leaders, as well as broader stakeholders.
The few utilities leading with, and focusing on, data are reaping benefits ahead of their peers. Some have created data enablement programs or have set up comprehensive data governance frameworks for their organizations. These typically comprise data policies, procedures, standards, and responsibilities that align with the company’s business strategy and goals. These utilities not only collect data, but they also have a strategy in place to manage that data effectively. They promote data literacy across their organization and are moving in the direction of a data marketplace where data products can be created using formal processes and shared across the enterprise.
Data enablement is about creating business value to drive measurable business outcomes. It’s about simplicity, reusability, and accountability. When done right, it can lead to improved data that will reliably feed better, smarter, and faster models powered by artificial intelligence (AI).
Follow these seven steps to get started with a data enablement framework:
- Identify critical data elements (CDEs).
- Identify the system of record for the CDEs.
- Identify the chain of responsibility (data domain lead, data steward, etc.).
- Define data standards.
- Conduct initial data quality checks.
- Create a business glossary of key terms.
- Make the data/dataset available to users (data catalog).
Pursue data maturity with a sense of urgency
The increased adoption of disruptive technology, such as AI and generative AI (GenAI); the urgency to decarbonize and electrify; customer expectations; and soaring global temperatures have escalated the need for utilities to look for new insights and new ways to improve. Data and technology are critical to helping utilities maintain a reliable and safe grid. In addition, they can provide support in managing evolving climate change issues while also preparing both the utilities workforce and the sector’s key stakeholders, including utility customers, for all that lies ahead in the energy transition.
Alongside the creation of a data enablement program, focus on these four tenets to close the data maturity gap:
- Identify a data leader.
According to the EY survey, none of the utility executives indicated having an overall data leader or Chief Data Officer (CDO). However, 13% said that they have someone acting in that capacity. This person should weave data literacy into the culture and embed its functionality into every aspect of how decisions are made. For an organization to become truly data-driven, data needs to be enabled and prioritized with a leader who is responsible for shaping data strategy, someone who can drive change through advocacy and collaboration with other leaders across the enterprise. Ideally, the CDO should collaborate with other leaders whose functions touch data, such as the General Counsel Office governing data privacy and the Chief Information Security Officer governing data protection. These leaders can work together to facilitate early adoption and data enablement programs at the utility.
- Establish the protocols and infrastructure for reliable data.
An indicator of an immature data framework is having multiple systems that manage assets with different data models, which can limit strategic and compliant data flow across the business. This will raise concerns about data quality, and it showcases a lack of data standards. To overcome this, utilities need to focus on data accuracy by building protocols and infrastructure to provide consistent, timely, relevant and trusted data delivery across the entire enterprise.
- Build a data governance roadmap.
There are countless cases of corporations that have established data trust as part of their corporate vision only to succumb to the pitfalls of not properly developing a data strategy and governance roadmap. With data standards in place, the next step is organizing the data. A common data ontology creates a consistent method for creating and managing data objects across all business functions. It also gives the utility the ability to rapidly adapt to a changing business environment, such as absorbing a new business, offering new client services, managing potential disruption or rapidly pivoting to new value creation opportunities.
- Align the workforce to embrace and protect data.
Creating true data trust and reliability takes a transformative, enterprise-wide effort and must be integrated into every aspect of the organizational culture. As data analytics continues to evolve, it’s important to hire and retain the right talent and ensure that everyone in the organization is speaking the same language. When different data points need to be evaluated, or new metrics need to be created, a data dictionary can help teams get up to speed more quickly.
Quality data is critical to helping utilities forecast power demand, anticipate disruptions, and drive grid reliability and security for their customers. Building or changing toward a reliable, data-driven culture requires commitment, diligence, and continuous communication across all levels of the organization. The fast emergence of GenAI can help utilities improve performance through detailed maintenance strategies and even shape response protocols to volatile weather through the use of predictive analytics, but only if the data feeding that technology is reliable.
The time is now for utilities to activate reliable data strategies, and aggressively hold each other accountable for progress. There is too much at stake to approach it any other way.
This article is the first in a three-part series written by professionals in the EY Americas Power and Utilities sector. The second and third articles in the series will focus on gaining consumer trust through data security and operationalizing data in the face of disruptive technology.
About the author
Michael Juchno is a principal at Ernst & Young LLP and leads EY Americas teams in deploying AI and data projects in the Power and Utilities sector. Mike has more than 25 years of experience advising utility companies in the full data lifecycle, from data strategy, quality and governance, and execution of use cases, to increasing the consumption of data analytics across the utility value chain. He has presented to Edison Electric Institute, Utility Analytics Institute, and DISTRIBUTECH International® conferences.
The views reflected in this article are the views of the author(s) and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.
