IEEE Power & Energy Magazine - May/June 2018 - 69

customers and adding new incremental revenue is critical
for the long-term financial health of utilities. at the heart of
this challenge is the need to better understand customers. with
many new intermediaries emerging on the residential, commercial, and industrial end-user landscapes, utilities must
improve their customer relationships to remain viable and
prevent themselves from becoming a commoditized infrastructure service.

Energy Analytics Leaders

up a corporate analytics team involves breaking the silos
and changing some well-established mind-sets. sometimes,
the team leader has to fight against the existing corporate
hierarchies to set up the innovative team structure and offer
competitive compensation packages to talents.
analytics is a field where many disciplines interact, such as
statistics, operations research, and computer science. applying analytics to the energy sector would involve another set of
disciplines, such as engineering, economics, and public policy.
an interdisciplinary academic background is certainly a plus
to the analytics leader because it provides diverse skills and a
broad perspective to tackle the emerging business problems in
the power industry.

it will certainly take a village to dig the value out of data
using analytics. who can take the lead? chief data officer,
chief analytics officer, or head of data analytics? whatever
title you name, that is a person leading the analytics team,
a role that never existed in the power industry. according to Insights Engine
the 2017 Utility analytics survey, only 3% of the utilities are when the analytics team is lifted to the corporate level, the
holding the c-suite responsible for driving analytics initia- potential value it can generate from the data would be largely
tives, while 73% of the utilities have the individual business dependent upon the size of the organization. a large investorunits hold that responsibility.
owned utility may see millions of dollars of annual savings,
for those utilities that are trying to fill in a c-suite seat and a large analytics team would be justifiable. a small utility,
for the analytics officer, a natural move is to have the existing such as distribution co-ops and municipality-owned utilities,
staff pick up the new skills and then promote from within. The however, may have to limit the size of its analytics team to a
individual business units holding the analytics responsibilities, handful of people so that the cost does not surpass the benefits
such as energy forecasting, unit commitment, and energy trad- it brings.
ing, may contribute to the candidate pool for the corporate anawhether the analytics team is large or small, it can be viewed
lytics team leader. on the other hand, several business sectors as an insight engine of the entire organization. The staff that
adopted analytics many years earlier than the energy sector, forms the engine has to be able to vet and manage large data
such as high tech, financial services, and even retail and sup- sets, leverage enterprise analytic platforms, analyze data using
ply chain sectors. so another strategy is to hire someone from advanced analytics, write and diagnose code, communicate well
those sectors to lead the charge of energy analytics.
both verbally and in writing, work well in a collaborative team
each strategy has it pros and cons. in addition to being a environment, and adapt to both leadership and support roles as
master of one or more utility analytics areas, a good inter- needed. in addition to the usual skill sets of conventional data
nal candidate typically understands the internal politics and scientists, some utilities may also need staff economists and
culture, which is important in fusing the analytics functions regulatory specialists on the analytics team.
that are traditionally placed in silos. an analytics offiwhile the engine is producing world-class insights, the
cer coming from outside, on the other hand, is unlikely business may not be able to capture all the value from such
to be biased by an individual business unit when making insights. To bridge the gap, some companies, including the
business decisions. an external candidate could bring fresh ones in silicon Valley and on wall street, have platform evanideas and lessons learned from other analytics-savvy sec- gelists, or analytics evangelists, people who really are more
tors, which may effectively transform a utility company soft-skill oriented, with the ability to influence, rather than
into a modern 21st-century insight-driven enterprise.
having the hard-core technical skills. in this evangelist or
The internal candidate has to
acquire the knowledge and experHigh-Priority Applications
Highest-Priority Applications
tise that nobody at the company
possesses to take the challenges that
Energy Forecasting 52%
20%
are greenfield efforts. The external
Smart-Meter Analytics 50%
17%
Asset Management/Analytics 39%
14%
candidate has to learn about the
Grid Operations 38%
15%
utility industry and its business opCustomer Segmentation 35%
12%
erations to adapt and implement
Energy Trading 33%
6%
some disruptive ideas. regardCredit and Collections 24%
6%
less of whether from the outside or
Call Center Analytics 23%
6%
within, an ideal candidate has to be
Program Marketing 14%
4%
a lifelong learner.
another necessary character is figure 2. The priority of utility analytics application areas, according to a 2017
entrepreneurship, because setting Utility Analytics Survey.
may/june 2018

ieee power & energy magazine

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Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
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