In recent years, the conversation around utility industry analytics has changed considerably. At first, it centered around how to capture, process and store all the new data that was flowing in.
Then came the question: “What are we going to do with all this data?”
That conversation evolved into one around defining the use cases for big data and analytics: “Where can I actually use AMI (advanced metering infrastructure) data to help me with customer situations?” or “How do I use SCADA (supervisory control and data acquisition) data to improve some of the activities going on in my distribution network?” Or, on a macro perspective: “Where does this data get used, and what is the value of it, either from a quantitative or a qualitative perspective?”
But the conversation around big data soon shifted to action. The utility industry has been quite innovative in its use of big data–meter, sensor, legacy, enterprise and more–and in testing many new concepts with it. Now, the conversation is focused on how to create lasting value with big data analytics, and the strategy behind it: “How do we stop think about analytics as a dashboard or a port–things that are traditionally thought of when you think about business intelligence–and start thinking about analytics as a means to change the way we deliver new products and services to our customers, and change how our employees work?”
There are four important tenets to developing an analytics strategy within any utility:
· Make the new sources of data and information available to all areas of the utility.
· Create a mindset that analytics is an iterative process.
· Recognize the benefits to enterprise collaboration.
· Integrate analytics throughout the operation.
Let’s look at each of these more closely.
Make data and information available enterprise-wise
There have been many industry discussions over the years about the “silos” that sometime exist between different parts of the utility enterprise, for example, between distribution operations and customer service. Analytics is one of the common threads that are helping to break down some of those barriers; it’s creating a unique opportunity for different utility groups who were able to operate autonomously before, with data held in silos within each of their teams, to collaborate to have mutual benefit for each of the functions for which they are responsible. This collaboration allows true innovation to bloom.
For example, while meter data was initially only used for billing, it is now also a key piece of operational information around asset health and infrastructure maintenance, with meter data analysis providing information on consumption trends and how those are affecting other assets (such as transformers). As well, meter data analytics has allowed utilities to drive shifts in customer behavior in order to positively impact the distribution grid by flattening the usage curve and lessen the strain on specific assets.
Create a mindset that analytics is an iterative process
Analytics is not a report that is put in place and remains static forever. Analytics is a process that allows you to get to the root cause of an issue or opportunity, and once that issue or opportunity is acted upon, new issues or opportunities will begin to surface.
It is important to establish and support a data culture across the utility organization, one that gives rise to an agile, continuous, iterative exploration in order to discover just where utility data can deliver new insights.
Recognize the benefits to enterprise collaboration
There are critically important benefits to having different utility departments working together and becoming more collaborative. Here are a few examples:
Data from different business functions can enhance options in other parts of the organization. For example, meter level data and field inputs from meter-to-bill operations can help distinguish distribution-level issues. The utility might see from a meter data perspective, for instance, that there is a growth in the number of electric vehicles on a regional level, which affects load on various substations upstream. In this case, meter-level information has the ability to provide predictive forecasting services for distribution planning and operations.
The utility departments overseeing energy efficiency and demand response program implementation can collaborate with utility customer care or customer service departments for the mutual benefit of both, and for the ultimate benefit of the customer. For example, customer information data might flag a billing issues problem within a certain segment of the utility’s customer population. This data can then drive targeted conservation programs designed to help those customers reduce their bills.
Integrate analytics throughout the organization
Approaching an analytics strategy successfully means operational integration: viewing analytics as a way to change the way in which the utility works, using analytics to drive different employee behaviors, and leveraging existing investments in technology.
Analytics is more than a clean graph or chart or report–that’s just the presentation of data. The biggest differentiator for the most successful utilities–the ones that are taking that data and using it to change the way they perform–is that they are driving results back into their systems. For example, in the past, field crews were rolling trucks and investigating potential distribution issues only to find that they were investigating more false positives than real problems. When analytics drive the work, those same crews are finding that eight out of 10 of the issues they investigate are real issues. It’s a matter of working smarter, rather than harder.
Moving forward with an analytics strategy
It’s important to think about approaching analytics in two ways: One is the strategy for how they think about it, and the other is the architecture, or the technology platform, that they’ll use to get there.
On the strategy front, the conversation should really focus on the points detailed above. First, make these new sources of information available to everyone. Don’t let them sit with a few data scientists or a few groups within the utility who have the tools and the know-how, but really make the data usable and available to everyone at the utility in order to allow innovation to bloom.
Second, encourage different departments to collaborate on new solutions backed by the available data. Otherwise, the traditional silos will continue to exist. Finally, viewing analytics as a way to change the way utilities work, leveraging existing investments in technology, and using analytics to drive different behaviors in employees, will truly integrate analytics in utility operations. It’s a matter of making analytics the way we work, rather than just another bank of information that’s out there.
The approach to architecture is a little simpler: make sure you pick the right platforms, the right tools, and the right teams to lead the analytics charge, to make sure you get the most out of the work you are doing.
About the Author: Creighton Oyler is Vice President, Strategic Growth Markets, for Oracle Utilities