For years, utility companies have been investing in data analytics to improve operational efficiency and reliability, reduce costs, and enhance customer experience. With additional advantages, such as better prediction of grid demand and improved sustainability measures, realizing the full potential of data has become a core priority.
Despite the enormous benefits, data initiatives have often been lackluster. For organizations that have invested heavily in data and analytics—and those that have made do with limited budgets—the results are largely the same: Many are stuck, with little to show for their efforts.
This is not just the result of funding and data challenges. Many organizations do not know how to utilize data analytics effectively, with several persistent myths hindering progress and frustrating teams and leadership.
Here, we debunk five (and a half) common misconceptions around data in utilities.
Myth 1: It is IT’s job to make data meaningful
Too often, generating value from data falls squarely on the shoulders of the IT function. But, while IT plays a vital role, it is not the only group responsible for unlocking the full potential of data. True data-driven transformation requires collaboration across business leadership, data teams, and IT:
- Business leadership defines the overarching vision for how data and analytics can be used to achieve business objectives. Without the commitment of directors, executives, and management to make lasting operational changes to bring meaningful results, data-driven initiatives can lack direction and fail to deliver.
- Data teams are responsible for establishing governance, promoting data literacy, and enforcing quality standards set out by the utility. In some organizations, data teams fall under the umbrella of IT, but regardless of organizational structure, a dedicated team is vital for ensuring robust data governance.
- IT provides the technical infrastructure and tools for secure data access, supporting connectivity beyond isolated business units.
Actions to take: Business leaders can make three key shifts to improve the impact of data: spending regular time on data analytics by sponsoring key initiatives; assigning the best managers to lead these efforts; and committing to realizing tangible value through intentional processes that unlock the power of these analytics efforts.
Myth 2: Our data systems are ready to deliver insights
Many organizations mistakenly believe that stitching together their core systems—such as enterprise resource planning (ERP), work and asset management (WAM), customer information systems (CIS), and geographic information systems (GIS)—is enough to create a robust data strategy. Executives then become disappointed to learn that these individual systems do not automatically add up to a silver bullet solution.
The reality is far more nuanced. While core systems are important and contain valuable data, holistic enterprise analytics requires integrating data from multiple systems and making that information accessible across the entire business. This bringing together of data from across the organization needs to be guided by a strategic approach.
Actions to take: To transform data into meaningful insights, organizations must move beyond isolated integrations. By establishing a cohesive and reusable framework, they can integrate critical components from disparate systems into curated data sets, supporting various analytic applications. For instance, robust, AI-driven predictive maintenance models often utilize data from five or more applications to deliver comprehensive insights and optimal recommendations.
Myth 3: We have a data lake; now insights will flow
Utility companies sometimes center their data strategy on efforts to pull all key data into one place without enough thought as to what data they need and how to use it.
This can lead to utilities building a data lake—a centralized repository for storing huge volumes of data from multiple sources in various formats and levels of governance. But without business context and active stewardship, this large, unstructured collection of data has little practical value and can lead to technical debt from expensive rework and duplicative data sets.
Actions to take: Key personnel need to engage early in the data-sourcing process to clearly define the objectives of data initiatives, and the data integrated in the lake must serve a defined purpose and be well-documented and easily discoverable. Drawing data from a well-organized ecosystem supports efficient cross-system analytics use cases. For instance, AI-enabled schedule optimization can enhance frontline crew productivity by 20 percent, while preventative maintenance can bolster grid resiliency and reliability by up to 25 percent.
Myth 4: Our data is not yet good enough
Frequently, utilities think of data quality as a barrier to establishing analytics programs—sometimes postponing initiatives for years because they think that the quality gaps in their data are too significant to drive meaningful business outcomes. In reality, data does not have to be of the highest quality to be useful, and utilities should avoid the trap of waiting for “perfect data.” A more practical approach is to first launch transformative initiatives and then focus on data quality.
For example, a distribution asset analytics program can be launched even with incomplete data, as well as vegetation analytics. Similarly, work management analytics does not require complete data from the outset. These quality issues can be fixed during these programs.
Today’s powerful data science models are designed to overcome gaps and imperfections, so missing or flawed data should not prevent utilities from getting started. Often, models can estimate missing values, identify alternative proxies, or utilize the absence of data as a helpful insight itself. For instance, in asset management, the age of many distribution poles can serve as a proxy for other characteristics. And even if data on age is missing, the information can offer insights to the model by signaling predictable patterns related to historical record keeping, such as records predating the GIS.
Actions to take: Instead of waiting for flawless data, utilities can launch high-value use cases that have the basic data available. When encountering data issues, they can use a curated set of assumptions and data science rigor to overcome these gaps, while identifying situations where missing data could significantly affect outcomes. Experimenting and refining different approaches can be a more effective strategy than waiting for data perfection and allows utilities to unlock tangible value right away.
Myth 5: It is faster to outsource data capabilities
Utilities wouldn’t consider outsourcing critical functions, such as substation design, gas safety rules, or grid capacity planning. Yet many hand over their data capabilities—even though these are as strategically important as other core, high-value engineering functions.
The drivers behind this outsourcing are twofold. First, data is often not recognized as a strategic priority within utilities, making it a “safe” option to outsource to avoid rapid headcount growth. Second, an emphasis on measuring data costs over analytical value can result in unfavorable benchmarks, prompting short-term actions that favor variable lower-cost contractors.
However, outsourcing ultimately limits data capabilities. Some key roles are best served by employees who understand the business objectives and ensure the data approach is tailored for sustained impact. These employees include subject matter experts, data stewards, and data owners; strategic data professionals such as data scientists, governance leads, and data architects; and technical leaders in the IT team, including solutions and enterprise architects. The explosion of data needs offers an opportunity to further develop in-house talent. Upskilling talent to better drive data initiatives allows utilities to keep key knowledge in-house in strategic roles while contributing to their overall job satisfaction and professional growth.
Actions to take: Intentional focus and investment are needed to retain and develop critical talent in-house. Utilities can prioritize strategic data capability building to deliver more value and keep valuable talent engaged and motivated. This can ensure a strong future in data-driven ventures and a lasting competitive advantage in an evolving market.
Myth 5 1/2: It is best to start small and scale later
When implementing an organization-wide analytics program, utilities can fall into the trap of starting small, with the goal of expanding in the future. While this approach can limit risk and create some early excitement (the half-truth to the myth), starting too small can also slow progress and reduce impact, limiting the potential analytics value on which utilities could be capitalizing. The most successful road maps often blend short-term initiatives with longer-term investments.
Actions to take: Utilities can consider combining two short-term projects (lasting two to three months) that deliver quick, visible value, with two larger initiatives designed to deliver significant, transformative value over time. With larger efforts, measurable milestones are needed to showcase progress and prove impact along the way, unlocking further momentum and investment.
Navigating beyond these common myths calls for an integrated approach—grounded in business needs, executed by empowered teams, and reinforced by diligent governance. By challenging these misconceptions, utilities can turn frustration into progress and investment into value.