Got GIS? You'll need it to maximize many analytics applications
- Posted on November 7, 2012
- 12 views
As many of our readers, clients and partners have noticed, here at the Institute we view the utility analytics market primarily through two lenses: that of customer analytics and grid analytics. (Yes, we also cover the business infrastructure, too.) In our coverage of these market segments we have learned about what applications have moved to the forefront and which ones are still percolating for future use. One common thread woven across many of the applications that we look at -- be they in the grid or customer market segments -- is the role of geospatial technology, a.k.a., geospatial information systems or GIS.
In fact, the genesis of this article is that in many of the research interviews conducted by our staff in recent months, GIS kept coming up -- more so than we expected. In grid optimization, this was usually around the critical role of the connectivity model; in asset optimization this was usually around using GIS either as the asset repository or a hub for all things assets. On the customer side of the world, locational analysis for endless customer applications creates a significantly more valuable proposition for utility staff and managers working to improve customer service and engagement.
For industry perspectives for this installment of our analytics leadership articles, I called on Bill Meehan, ESRI's director of utility solutions. Bill's 30-plus year career in the utility space includes senior management roles in operations and engineering at a large investor-owned utility prior to joining ESRI, so he speaks from a depth of experience with many of the issues facing today's utility leaders.
As noted above, the connectivity model, while already critical, has arguably reached a greater level of importance with the advent of analytics. In Institute research the connectivity model is often cited as one of the hurdles in deploying the applications that fall into the Institute's grid optimization segment. "One of the challenges that we see at utilities trying to leverage their connectivity models is rooted in the legacy of how their GIS was developed," explained Bill. "Much of the legacy around GIS is that it is still viewed as a mapping tool, when it is really a decision support tool with a very strong visual component."
Bill is passionate about the role of GIS in utility operations; accordingly, he continued: "Looking at the overall GIS world, mapping is probably the legacy of what people thought GIS was supposed to be in the first place. With a migration from hand-drawn mapping systems to computer-generated mapping systems, this legacy has the thinking around the production of maps. The shift that needs to happen is that there needs to be a realization that GIS is not just a visualization of the map, but is more about discovery and analysis -- it is a decision support tool, not just a mapping tool. Because of this, utilities do not always capture the connectivity or the phasing on the grid."
Looking deeper into some of the analytics application areas across the grid and customer market segments reveals the critical roles that integration and data quality will play in the successful implementation of so many of these solutions that leverage GIS. For example, in asset management, utilities need to have a clear vision of the role that each system plays in the overall scheme of managing millions or even billions of dollars of assets in the field that keep the lights on for millions of customers. The GIS provides the locational data and in some cases is the repository for the asset data, while in other cases the asset data resides in another system, like SAP or Maximo. Factor in inventory store and crew dispatch functionality and the requirement for clean integration becomes readily apparent. Here's a new driver to the asset management world: the proliferation of sensors across the grid creates more opportunities to manage those assets predictively, and with an integrated GIS, spatial analysis of the repair versus replace decision can identify trends and streamline maintenance processes.
On the customer service and engagement side of the utility, the potential roles of GIS are too many to list here, but, for example, spatial analysis can be integrated with third-party demographic data for debt collection and improvement. Or companies can integrate third-party demographic and even income data to profile and target customers for energy efficiency or demand response programs. Also, everybody who has a smart phone is a source of customer intelligence with a utility's ability to spatially analyze the unstructured data from Twitter tweets for a variety of customer service applications, like outage reporting and bill payment.
When I get someone with Bill's experience and wisdom on the line, I always like to throw out a crystal ball type of question, which in this case was about what benefits might come out of the analytics era that we aren't necessarily thinking about right now. Bill didn't miss a beat and jumped right on this: "I think and am hoping that smart meter data will eliminate many of the bad surprises that I have experienced over the years in utility operations. Especially things blowing up -- like when transformers overload and blow up. Smart meter data will enable better monitoring of the system health and well-being of their assets. Also, with the smart grid there will be more prediction of failure than actual failure itself. This will also enable the system to run more effectively, and this might even enable utility staff to learn more about the system that they never knew. With more data available, patterns will emerge and intelligence will result."
The analytics market continues to be a field rich in opportunity for improvement of utility grid and customer operations, and GIS can and will be a key piece of utilities realizing the full potential of their investments in analytics.
Mike Smith is a vice president with the Utility Analytics Institute.
Explore Related Topics:
No discussions yet. Start a discussion below.