Public Utilities Reports

PUR Guide 2012 Fully Updated Version

Available NOW!
PUR Guide

This comprehensive self-study certification course is designed to teach the novice or pro everything they need to understand and succeed in every phase of the public utilities business.

Order Now

Data-Driven Transformation

Building a business case around smart grid data.

Fortnightly Magazine - January 2011

grid transformation. Data shouldn’t be examined in isolation. It should be examined on equal footing with technology, process, and organization.

Smart grid architecture can create a paradigm shift in the role of data and can bring new and additional data in several ways. Smart grid greatly increases data sources and collection points and brings enormous increases in data volume. Data flows will also change, as data communications that were typically unidirectional and periodic will evolve to become bidirectional and nearly real time.

As a result of these changes, many distribution management operations can be automated and streamlined. Opportunities exist to improve field service operations, asset management, new service marketing, grid reliability, outage management, and rate design. Data can be elevated from merely a process input to a source of new capabilities.

Value from Data

In a typical business transformation, data requirements are identified as part of a process design or systems implementation. By supplementing this with a more deliberate approach that involves identifying the available data up front, utilities can use that data to drive opportunities rather than limiting the implementation to only what a process or system needs. While data is closely intertwined with technology and process, it doesn’t provide value automatically. The potential benefits of data must be deliberately identified and developed. A simple three-step process can help utilities use data to drive value in a smart grid transformation ( see Figure 3 ).

In the first step, “identifying available data,” the process focuses on assessing current systems; understanding data capabilities; and identifying new data that’s available but either isn’t being captured or is captured but not used. For instance, smart meters typically are capable of recording dozens of data elements regarding condition status, event monitoring, and usage, but most utilities capture and use only a fraction of those data elements in order to feed legacy processes. A transformational approach won’t look at data through the filter of existing business operations and systems, but instead will assess the entire set of available data and look for new possibilities.

The second step, “assessing potential value,” includes comparing potential data against planned usage, and searching for additional opportunities outside the planned solution set and current requirements. Looking beyond legacy processes for ways to develop new capabilities and optimize existing capabilities will help the company to define the value proposition for transformation and begin building a business case that aligns with strategic objectives.

In the final step, “developing the transformation,” the utility will map the identified opportunities to its enterprise model; identify relevant stakeholders, and what the new data elements will mean to them; and design transformational objectives. This includes defining new processes or changes to existing processes that use the data; identifying requirements for systems and integration; and estimating the effect on performance. A detailed business case and cost-benefit model will define the quantitative and qualitative benefits. And an implementation road map will demonstrate how the data-driven transformation will occur—and just as importantly, how it will integrate into the overall smart grid program.

Figure 3 illustrates how this approach can be applied