- Industry: Energy & Utilities
The American Recovery and Reinvestment Act of 2009 propelled rapid growth of the smart grid industry. Since then, utilities have struggled to develop a successful approach for extracting meaningful insights from the massive amounts of meter and sensor data they collect daily.
Over the years, many utilities developed a bottom-up approach to data analytics—one focused on key assets (e.g., generation plants, substation transformers, etc.), and key functions like fraud detection and load forecasting. Other utilities tried to jumpstart their data analytics programs by purchasing third-party software and services to see if they can realize the business value that vendors are attempting to deliver. Both of these approaches fail, though, when the utility does not have a clear vision or roadmap for how the solution will improve its ability to make either daily or longer-term strategic decisions that improve its profitability.
Top-down, rather than bottom-up
West Monroe Partners has found it more effective to use a top-down analytics approach that focuses on identifying the key business decisions that are core to a utility’s operations and strategic investments and can be improved with the support of data analytics. This business-driven, rather than bottom-up, approach to analytics delivers the business case that a CFO needs to see, the business and operational results that the CEO wants to see, and the business requirements that IT and the business as a whole need to define and implement a data analytics program.
The approach consists of five stages:
- Prioritize decisions by functional area
- Assess current analytics
- Assess future analytics vision
- Analyze gap(s)
- Delivery an analytics roadmap and business case
Stage 1 begins with identifying key decisions that the utility makes to guide the ongoing business operations. This approach uses a detailed decision matrix to analyze decisions across all functional areas of the business, such as:
- Asset management
- Customer service
- Energy supply/generation
- Finance and accounting
- Human resources
- IT and OT
- Legal and regulatory
- Transmission/distribution/substation engineering
The decisions made periodically by the utility (i.e., annually, quarterly, monthly, weekly, daily, hourly, automatically, or in a reactionary/event driven matter) across each functional area are analyzed and prioritized. In particular, it isolates high-priority decisions that have a significant impact on the utility and can be improved with better data analytics.
Stage 2 then determines how the utility currently uses analytics to enhance these high-priority decisions. This assessment is important because it establishes and documents a baseline for the utility’s analytics program. Stage 3 involves a deeper dive into the utility’s future vision for its analytics program and how it will optimize high-priority decisions. This analysis is instrumental in uncovering differing opinions that exist regarding the utility’s future vision as well as current challenges that need to be addressed to realize that vision. Stage 4 highlights gaps between the utility’s current and intended future vision for analytics.
With an organizational vision for analytics established and potential gaps identified, Stage 5 begins—focusing on the projects necessary to deliver on the data analytics roadmap and return on investment outlined in the business case. The roadmap will include a list of key objectives, activities, and deliverables needed for the utility to realize its analytics vision, along with the expected benefits. The business case will detail the specific costs and benefits of each of the components of the analytics roadmap. This business case will also provide the utility with a clear understanding of the expenditures (e.g., dollars, people hours, etc.) and gains (e.g., efficiencies, increased revenue, and business insights, etc.) associated with completing its analytics roadmap.
This top-down analytics approach provides confidence for moving forward, and it prioritizes the specific activities necessary to implement an effective and successful analytics program.