Predictive Analytics—Beyond the Hype
BY NICHOLAS ABI-SAMRA, CLAUDE GODIN AND CURT D. PUCKETT, DNV GL
On the operational side, utilities often
face little reinvention. The analytical processes and algorithms for data have been
in place for some time, but now the information is better and available in real or
near-real time, so better decisions can be
made more quickly.
On the customer behavior side, it’s a
bit fuzzier. Customers are harder to predict. Although there is a lot of buildup
in the marketplace, most utilities have a
long way to go before realizing the data
analytics dream the way, say, Google does.
Now it’s more like a nightmare: seeing
enormous dumps of data but not knowing
what to do with them.
In many instances, the operational side
drives the utility business case for data
analytics investment, with customer analytics’ providing additional, smaller benefits and supplemental ROI. Some utilities
embrace the analytics as they look for
ways to engage customers in better grid
management. Many believe they will get
something from all that customer data, but
they aren’t sure what—perhaps improved
customer engagement strategies or new
ways to increase customer satisfaction. A
more compelling question is how much
customer engagement do utilities want?
On the level of Google or a retail store?
Probably not. Some regulated utilities are
It is important to understand and align
your capabilities with your information
and operations technology functions.
Creating a comprehensive road map
focused on high-return predictive analyt-
ics with clear destinations and achievable
milestones is the starting point for better
understanding customers, their behavior
and their impact on grid operations.
THE ENTICEMENT OF TOMORROW
Much excitement surrounds the ben-
efits to utilities from manipulating data
collected from the grid and customers,
such as new operational efficiencies, new
products, novel ways to serve, and fresh
revenue streams. For executives and engi-
neers, many of the operational benefits are
easy to define and evaluate; improved data
analytics lead to improved outage man-
agement, voltage optimization and pre-
dictive asset management, which all lead
to reduced truck rolls and other quan-
tifiable results. Customer benefits, how-
ever, where the greatest ROI might come
from revenue protection, improved load
forecasting and detailed customer seg-
mentation leading to increased demand
response program enrollments, are entic-
ing but harder to quantify.
Nicholas Abi-Samra is senior vice president of electricity transmission and distribution at DNV GL.
He has served as general chair and technical program coordinator for the IEEE General Meeting of
2012 and is a professional engineer.
Claude Godin is director of energy data analytics at DNV GL and has 35 years of experience in
the energy market, specializing in design and delivery of large-scale projects, meter data acquisition/
management and energy analytics.
Curt D. Puckett is senior vice president of sustainable use consulting at DNV GL, where he
is responsible for overseeing North America’s eastern operations, which includes offices in the
Northeast, Midwest, Mid-Atlantic, Mid-South and Southeast regions.
FOUR STAGES OF ANALYTIC READINESS 1