Fueling utility innovation through analytics
Utilities around the world
are making big investments in advanced analytics. Getting the full value,
however, requires rethinking their strategy, culture, and organization.
Advanced analytics can deliver enormous value for utilities and drive organizations to new frontiers of efficiency—
but only with the right approach. There’s little to be gained from just bolting
on a software solution. The real value comes from embedding data analytics as a
core capability in the organization and using it to detect pain points, design
solutions, and enable decision making. Conservative estimates supported by
rigorous use-case analysis suggest that advanced analytics can boost
profitability by 5 to 10 percent, while increasing satisfaction for customers
and improving health and safety for employees. But capturing impact on this
scale is no easy feat, and utilities often struggle with the same few
challenges, which undermines the success of an analytics transformation. Below,
we look at these challenges and show how they can be overcome.
Challenge 1: Developing an analytics strategy that’s
clear about what to prioritize and why
As new applications proliferate
across the energy value chain (see sidebar, “Applying advanced analytics at
utilities”), advanced analytics poses a strategic challenge. How do utilities
prioritize use cases and set appropriate aspirations for business impact?
Without clarity on these matters, companies can easily lay themselves open to
excessive influence from external vendors or get caught up in chasing the
latest viral use case. One US utility partnered with a technology supplier and
invested millions in wind-forecasting software only to discover that the effort
wouldn’t yield any returns. Another large utility spent years building in-house
analytics capabilities and developing more than a dozen use cases before
realizing it had yet to make any headway on the biggest and most valuable
opportunities.
Develop a comprehensive inventory
of use cases spanning the whole value chain, including operations and support processes.
Utilities often focus on customer or operational applications first, but smart
companies place equal emphasis on support functions such as human resources,
procurement, safety, and internal audit—all of which can drive just as much
bottom-line value.
Structure your use-case inventory
into groups of applications that resolve similar pain points or address the
same business processes. Applications focused on areas such
as asset maintenance, contractor productivity, employee safety, or reporting
are likely to span multiple business units and deliver value across the entire
enterprise, rather than within a single silo.
Using simple valuation methods,
quickly estimate the potential business impact for each application across all applicable
dimensions, including cost, revenue, safety, reliability, and employee
engagement. This requires close collaboration between business owners,
analytics specialists, and the financial planning and analysis team to ensure
consistency in quantification and overall approach.
Prioritize the applications using multiple criteria,
including value, feasibility, alignment with corporate strategy, and business
engagement. How much weight to give each factor depends on the stage a utility
has reached in its advanced analytics journey. When it is starting out,
business excitement and engagement are critical to achieving buy-in. At later
stages, value and feasibility become more important. By the end of the journey,
analytics is so critical that priorities are dictated by overall corporate strategy.
Working through these steps need
not take long. One utility took just a few weeks to develop a list of nearly
200 use cases, prioritize them based on feasibility and business impact, and
select a handful of products to start building immediately. In mature digital
organizations, the list of potential applications can be integrated into
product strategies and constantly updated and reprioritized against other
ideas. Relative newcomers often start with a simple yearly process to evaluate,
update, and reprioritize the list.
Do
·
Build a prioritized
roadmap of use cases to pursue, based on vetted value estimates and alignment
with the organizational strategy
Don’t
·
Chase after viral use
cases without assessing the value at stake
·
Leave it entirely to
business teams and departments to prioritize use cases
Challenge 2: Converting hype into measurable bottom-line
impact
Many utilities launch use cases but
struggle to capture tangible value. Success requires the coordination of a
complex series of steps, and the collective impact is only as good as the
weakest link. Common facets of this challenge include:
Not understanding the impact at
stake and the process changes needed to capture it. We’ve seen several companies
make large investments in analytics projects without a clear business case or a
monetization plan. Other utilities have developed promising predictive models
but failed to implement the associated process changes needed to foster
adoption.
Struggling to get access to data or
use all the data available. Many
energy companies have observations and maintenance tickets that could yield
valuable data for predictive maintenance, safety, and other use cases, yet all
too often this data is wasted because digital observation tools and text-mining
capabilities are lacking. Another great source of data is utilities’ vast
archives of recorded calls from call centers, which can be used—but seldom are—
to create insights using voice-mining analytics. External data sets such as
social-media and weather data are also commonly overlooked.
Being unable to deliver an
analytics solution that works well. Adoption often suffers because of a lack of
collaboration with business users during the development phase. Too often,
organizations rely on senior managers or subject-matter experts from the
business, but fail to involve the front-line crew members who will use the tool
on a day-to-day basis.
Moving on to the next use case
before value has been captured. We’ve seen utilities have success with an initial
pilot but then be too quick to redeploy resources and funding before the effort
has properly bedded in. The result is lackluster front-line engagement, limited
adoption, and forfeited value.
Not having the necessary
capabilities and talent. That
doesn’t just mean data scientists, but all the roles involved in capturing
business value, such as the designers and product managers who act as “translators” between data engineers, data scientists, and the core
business. Other roles often
overlooked include DevOps experts and the data architects who enable access to
clean data. Though expensive, these capabilities can save money in the long run
by simplifying data curation and processing needs in future.
Leaders in analytics avoid these
pitfalls by:
Involving actual users in the
solution design, not only during
pilots but from the planning stage through to implementation. This is the
easiest, most cost-effective way to capture valuable feedback, build
engagement, and ensure adoption.
Following a business-centric
approach that starts with
developing a solid understanding of the performance of an entire work flow,
such as plant outage management, asset maintenance, or record-to-report in the
back office. From this understanding, an organization can identify all the
levers available to drive a faster, safer, and more productive way to do
business. Rather than focusing only on pain points in the current process,
utilities should instead map out a fully reimagined three- to five-year vision
for the whole work flow as well as a prioritized set of the technical solutions
required to realize the vision.
Building a strong
product-management capability that is structured around business processes
rather than technical solutions. Product managers have full visibility and
ownership of an end-to-end business process, drive the development of the
future vision for it, identify which data sources and technology solutions are
needed to achieve the vision, and manage rollout and the training of end users.
Developing a set of key performance
indicators (KPI) that measure
progress at every stage from model development and testing to user adoption and
value capture. This ensures that lessons are learned from experience and errors
are quickly corrected.
Developing an inventory of required
capabilities by translating
planned use cases into a roadmap for talent that includes all the
skills—technical and nontechnical—needed to deliver an analytics project. This
effort should also include defining a framework for assessing “make or buy”
decisions based on technology complexity, use-case criticality, the scale and
pace of the use-case rollout, and the utility’s long-term analytics strategy.
For example, a utility aspiring to become a leader in renewables may prefer to
develop an asset-maintenance solution internally to create a competitive
advantage over competitors that use off-the-shelf products from vendors.
Do
·
Involve users in
solution design from the planning stage onward, not just during pilots
·
Follow a
business-centric approach linking analytics solutions to a clear plan for
process optimization and monetization
·
Create a standard
framework to measure, track, and report on impact until full run-rate value has
been captured
·
Allocate time in
project schedules for change management and end-user training
·
Build a strong
product-management capability with end-to-end responsibility for work
processes, not technologies
·
Look for talent beyond
data scientists and hire translators, DevOps experts, cloud specialists, and
data engineers as well
Don’t
·
Deliver a solution to
end users and then immediately switch all resources to the next project
·
Think analytics talent
= data scientists
Challenge 3: Making data enable productivity, not inhibit
it
In our experience, analytics
organizations often struggle to develop the data-governance and platform
practices they need to deliver value. When a clear data strategy is lacking,
the data ecosystem will be underdeveloped, making the development of new use
cases costly and slow. Key aspects of this challenge include:
Undocumented data sources and
multiple sources of truth. Alarmingly,
organizational surveys often report that data users don’t believe their company
has a clear data-ownership structure or feel confident that data objects are
precisely defined or accurate, particularly when it comes to similar objects
from different sources. In many cases, organizations have trouble simply establishing
whether data exists. It’s also common for the business to have little trust in
new data systems, and little comfort in using them.
Unclear access rights and
privileges. It’s not unusual
for some parts of an organization to limit or block access to data that’s
critical for decision making or analytics, often citing data confidentiality or
cybersecurity as the reason.
Insufficient tools and capabilities
for preparing data for analysis. Many utilities struggle to ingest and curate data
efficiently, which increases the time and difficulty of developing new digital
tools. When building an analytics product, industry leaders spend no more than
20 to 30 percent of the development time on data cleaning, preparation, and
blending—tasks that may take 60 to 80 percent of the time for laggards.
A “build it and they will come”
mind-set. Some utilities
commit to major projects in building data platforms or datastorage
infrastructure in the belief that once data is available, the business will
want to use it. But they can end up investing tens of millions of dollars in
new systems without any real business benefits to show for it.
Best-in-class data-governance
practices allow industry leaders to fast-track value capture by:
Defining a target data structure
that is aligned with the organization’s needs,enforces standardization, and
serves as a catalog providing a sound basis for key use cases. To manage this
data structure, organizations need to align on clear dataownership and
governance policies—who has access to what data—that are respected and enforced
across the organization.
Using an agile approach to build
the data platform and defining a minimum viable
product (MVP) that delivers just enough functionality to allow the first few
products to be developed. An MVP often relies on quickly deployable open-source
technologies, easily obtained data, and tools such as Tableau and Alteryx that
speed up the production of a proof of concept. More advanced or specialized
components and data are often added iteratively in future releases.
Do
·
Define
organization-wide processes and rules for data curation, documentation,
sharing, and ownership
·
Develop and maintain a
central catalog of data
·
Develop a data strategy
that supports the wider analytics strategy
Don’t
·
Have unclear data rights
and governance
·
Skimp on investments in
data-cleansing, processing, and visualization capabilities
·
Invest heavily in
collecting and cleaning data before starting to develop individual use cases
Challenge 4: Embedding analytics transformation in your culture
and organization
A successful advanced analytics
transformation depends on the right culture and organization. That means
cross-functional teams working through short, iterative, test-and-learn
cycles—an unfamiliar prospect for utilities accustomed to long development
timelines. Navigating this transition will involve:
Creating an environment conducive
to experimentation and learning while taking care not to jeopardize strategic pillars
such as reliability and customer satisfaction. Adopting agile practices and launching short sprints to test new ideas in
the real world (rather than debating them in theory) can often feel
uncomfortable at first—but falling back on rigid waterfall processes will
result in endless planning iterations, blown deadlines, persisting pain points,
and a failure to create value.
Working out what kind of structure
will best support the analytics transformation: centralized, decentralized,
or hybrid. Many utilities are too decentralized, leaving them unable to reap
the benefits of standardization and best-practice sharing. All too often, a
utility deploys different solutions from different vendors to build what is
essentially the same product in different business units. Lessons learned
aren’t shared, and scale benefits aren’t captured. On the other hand, a fully
centralized model is seldom the answer. We’ve seen utilities where the analytics
organization and the business work at arm’s length, at the cost of misaligned
priorities and—worse—the development of products that disappoint the end user.
Securing senior management
commitment and appointing the right leader to act as a bridge between the CEO, the
analytics team, and other parts of the organization. In some utilities, senior
executives in charge of analytics are hidden three or four levels down in the
organization, leaving them powerless to remove any roadblocks that arise. In other
companies, their responsibilities are too broad and unfocused.
In our experience, most analytics
leaders have a good grasp of the organizational model best suited to their
company. This enables them to:
Establish the right culture, starting with top executives
who are curious to explore new analytics solutions, have a bias to action, and
strike a good balance between delegation and control. This starts at the top,
with the CEO and senior team emphasizing the importance of analytics, providing
the right incentives, and role modeling desired behavior. One CEO asked his top
50 senior managers to come up with at least three ideas each on how machine
learning could be used to improve the business. In doing so, he not only
created a vast array of use cases in a short time but also role modeled the
intellectual curiosity and bias toward business impact that he expected from
his leaders.
Shape a digital and analytics
organization that fits the
company’s governance model, maturity, and potential for standardization and
best-practice sharing. This includes ensuring that the executive driving the
analytics transformation has direct lines of communication to the CEO, even if
the role doesn’t always report there. Most analytics teams adopt a hybrid model,
with data governance, tools, and standards defined centrally; a close-knit
community of data scientists working both centrally and within the business;
and clear roles for product owners, who form cross-functional teams to drive
the day-to-day execution of use cases and have direct ties to business
executives.
Do
·
Align your analytics
organizatonal structure with your overall business strategy, governance model,
and level of maturity
·
Develop a structure
that ensures best practices are shared enterprise-wide yet enables business
units to be closely involved in solution development
·
Embed critical
analytics capabilities across the whole organization, not just in an analytics
center of excellence
Don’t
·
Limit your analytics
transformation to new software development and overlook culture and project
management
·
Expect the analytics
teams to develop their own mandate for driving change across the organization
Advanced analytics is transforming
industries worldwide and enabling organizations to achieve unprecedented levels
of productivity. For utilities, which lag other industries in digital maturity,
the value at stake from such a transformation is substantial. However, making
the leap is far from easy, and many utilities place big bets only to fall short
of their objectives. By adopting best practices and defining the strategy,
culture, and organization they need to achieve their analytics aspirations,
utilities can maximize their odds of capturing the step-change improvements
that industry leaders already enjoy.
By Marcus Braun, Eelco de Jong, Alfonso Encinas, and Tim Kniker
https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/fueling-utility-innovation-through-analytics?cid=other-eml-alt-mip-mck-oth-1804&hlkid=a46f3a312fa747bdbec0404144bfa31f&hctky=1627601&hdpid=1b37fd38-e3e4-4472-aa76-c46ccb0e6c5a
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