In this piece we highlight the utility data monetization imperative and how utilities can build the right strategies to take advantage of this opportunity

1. Strategies to Monetize
Energy Data
How Utilities Can Increase Their
'Earnings per Byte'
2020 Indigo Insights Innovation Series
© Indigo Advisory Group 2020 | indigoadvisorygroup.com

2.
As we announced early this year, Indigo is focusing on 4 key research themes over the coming year.
Our first quarter research theme is focused on monetizing utility data, a largely untapped and huge
potential revenue stream for utilities. Our research builds on our insights from 2017 — “Monetizing
Utility Data — The Utility Data as a Service Opportunity”. In that piece we outlined our initial Utility
Data Monetization Framework of basic data and value-added data and explored fee based structures
for value-added data services, ultimately advancing the “UDaaS” opportunity. In this piece we want
to go a step further and highlight the maturity of the market over the past 3 years and to look
forward over the next decade at how utilities can capitalize on the growing data points they are
gathering both internally and externally.
Importantly, in our research, when we discuss utility data monetization, we are not referring to the
endless opportunities that data provides utilities to improve operations and productivity but rather
the opportunity for utilities to sell data sets, insights and value-added data services to customers and
partners. (For operational use cases check out our utility analytics and UtiliAPP offering). That said,
and as pointed out in the MIT Sloan Review these paths are not mutually exclusive, and some
companies accomplish both, as is the case with telecommunications companies such as Verizon,
Deutsche Telekom, and Telefónica. They’ve achieved internal monetization by using data to optimize
operations and client services, and they also leveraged that data, anonymized and aggregated, across
various use cases for their B2B clients and partners by offering. In that second offering these
companies focused on products such as geotargeting for retailers, traffic flow for city planners, fraud
detection for banking institutions, smart targeting for digital advertisers and IoT applications for a
variety of companies. It is this offering that we are researching and benchmarking for utilities.
Utility Data Monetization Market Potential
Frost & Sullivan (2019) believe that the Data Monetization Markets in the Power and Utilities industry
could be worth nearly $20 billion by 2020 with a CAGR of 12.2% globally and with the volume of data
created reaching 175 Zettabytes (ZB) by 2025.

3.
Without a doubt, this demand is a huge opportunity for energy organizations that can best harness
and maximize the value of their data. Indeed, this year at CES, we saw data monetization from
sensors and antennas as a cross-industry mega-trend. From the evolution of Connected Services,
Location-Based Commerce, new In-Car Payment techniques and a significant amount of work being
done around the collection, cleansing, and shaping of Data Exchange itself — the market has very
much evolved from a hardware play. Over the next decade the 4th Industrial revolution, spurred on
by the convergence of AI, Big Data, 5G, Distributed Ledger Technology and IoT will unleash a host a
revenue opportunities for utilities. To assess the opportunities for utilities it is useful to examine
what is occurring in other industries as very often these trends eventually translate into the power
sector. To that, in a cross-industry survey recently conducted by the German based Business
Application Research Center (BARC), they found the beginnings of a data monetization market across
multiple sectors.
These results are consistent with a recent McKinsey study on Data Monetization, where they found
that across industries, most respondents agree that the primary objective of their data-and-analytics
activities is to generate new revenue. Interestingly, in that study, they found that more than half of
the respondents in energy, say their companies have begun monetizing data. What’s more, these
efforts are

4.
also proving to be a source of differentiation. Most notably, data monetization seems to correlate
with industry-leading performance. All this being said, this is still a very nascent area for utilities with
many factors to assess such as market, regulatory and technology complexities. In the next section
we outline a staged process that utilities can employ as they move forward with this upcoming
revenue opportunity.
Starting the Utility Data Monetization
Journey
Although every company has the potential to earn revenue from the information it generates, in a
recent study of more than 400 companies in 34 countries, only 1 in 12 were monetizing their data to
its fullest extent. Modern data monetization strategies can help utilities open brand new revenue
streams. In the diagram below and in this section we highlight Indigo’s 7 step process to monetize
utility data.
In terms of step 1, completing a data inventory, this may include utility data from operational
systems (GIS, ADMS, OMS, DSMS, DERMS, EMS etc.), enterprise systems such ERP data and customer
data such as CIS data. In essence, this is a complete review of all of the available data across a utility
that is both structured and unstructured. As a utility moves to step 2 data can be organized into
various needs with and eye to step 3. For example:
• Grid Needs and Planned Investment Data (Grid Need Type, Location, Scale of Deficiency,
Planned Investment, Reserve Margin, Historical Data, Forecast Data and Expected
Forecast Error)

5. • Hosting Capacity Data (Circuit Model, Loading, Equipment Ratings and Settings)
• Locational Value Data (Energy + Losses, Generation, Transmission & Distribution Capacity,
Ancillary Services, Renewable Energy Compliance, Societal Benefits, Voltage and Power Quality,
Conservation Voltage Reduction, Equipment Life Extension, Reliability and Resiliency, Market
Price Suppression)
Across a utility some of the most common types of data or data services that could be monetized
may include packaged data product that is ready-to-consume and requires little or no analysis or
transformation. It may include data insights such as dashboards, metrics and indices, going further it
may include data enhancement where data sets have been augmented with customer data for
additional insights (e.g. zip codes etc.).
In step 3, Monetization Analysis, a utility must recognize that in the majority of jurisdictions utilities
are required to make some level of data available to customers and to third parties, at no cost.
However, in cases where customers request information that is more detailed and/or more frequent
than basic required data, utilities could supply this value-added data for a nominal fee. This second
type of service — additional data — would derive directly from the monopoly function and could be
treated as a platform service revenue. A third case for example could be where utilities perform
analysis of customer-specific data, and provide recommendations based on that analysis,
conditioned on utilities implementing tools to allow customers to easily share their usage data with
third-party vendors including firms providing data analysis. For example, EV’s are now able to
capture and share many types of data, including geolocation, vehicle performance, driver behavior,
energy data and biometrics data. In this case OEMs and utilities could explore a wide variety of data-
based products and service offerings, including user-based insurance, mobile commerce, mobility-as-
a-service (MaaS), behavioral, energy and geo-based advertising, infotainment, and personal health
monitoring. In general, however, the graph below we highlight how adding insights to data sets
increases its value to a utility.

6.
Step 4 entails examining the end customer for the data or data services. Part of this step is to create
a market forecast by data type and ultimately a “total addressable market” number. This will help
the creation of a business case that will result from future steps. It will also help in further refining
the product by customer type. Step 5 entails creating a price point for the data. There are two
methods a utility or energy company could apply here. Firstly they could look at cost pricing, which
involves adding a percentage to actual costs for data collection, storage, preparation, and
transformation. Secondly, they could look at value pricing involves charging for the value your data
will bring a customer. In the first instance, cost pricing involves understanding your costs for data
collection, storage, preparation, transformation, and sharing so you can add a percentage margin as
you price your data above your costs. To inform that business case three major elements should be
examined: the cost of data sourcing, the cost of data packaging and the cost of data sharing. That
said, it also may be that your goal is not to maximize data revenue, but rather to use the offering as a
customer acquisition tool, for example a DER or DR product. If so, you might price your data at or
below cost as a loss leader, or even give some of it away for free. The size of the discount might then
depend on the value of the new business sought and the expected conversion rate of prospects into
clients. Value pricing on the other hand, involves looking at your data from a customer’s perspective
and identifying the value it will bring. With this pricing strategy, utilities should consider elements
such as the uniqueness of data, access restrictions, technology and expertise, market alternatives
(e.g. Green Button), analysis and most importantly business value. In this scenario, reducing the cost
of customer acquisition for a DER

7. provider (which can run into excess of 30% of a providers operating cost) would be priced according
to business value of the data.
A useful exercise at this point is to plot these elements on a quadrant like the one shown below to
help guide internal discussions around pricing. On the y-axis, plot the level of insights the data offers.
On the x-axis, plot the range and level of proprietary data.
In terms of Step 5, determining the ‘price’, Snowflake Computing recommends a tiered pricing
structure. This type of plan can help attract new users with lower costs for data access only, while
ensuring that your existing customers get the data and services they need, at a cost that best fits
their needs and budget. Utilities will also need to decide whether to sell data by the set or by
subscription, perhaps monthly or annually, or if they want to charge based on usage of the data.
When you plot the different attributes of your data and the elements that comprise value for
customers, utilities can create a matrix like the one below to help identify the different packages you
can offer.

8.
Step 6 and 7 involve packaging the product and selling it. While a direct data transfer to customers
cuts out intermediaries and may give a utility more control over the final product. the downside is
that a power company will have do all the work, often with standards such as FTP and APIs. This
method can include storage, security and ETL costs for both parties. Additionally, while a data broker
can help market your data and will sometimes also control pricing they offer limited opportunities for
promotion and incomplete control over the presentation. As such, we recommend a data exchange,
similar to the “Trust Portal” we defined with the Joint Utilities of NY here. At this stage a distributed
ledger solution is both elegant and efficient. As highlighted below, conceptually a utility data
marketplace or ‘data factory’ defines a standard data model and interfaces for buyers and sellers to
exchange data.

9.
Data Sharing and Regulatory Matters
Most utilities have more data than they know what to do with. Forrester reported than on average,
between 60% to 73% of all data within an enterprise goes unused, and indeed we have pointed out
before that up to 80% of utility data maybe unstructured. However, new tools and technologies have
made it easier to mine and process huge amounts of raw data into insights. These insights could serve
as timely intelligence to those in other sectors, like economists, analysts or investors looking to
identify patterns and trends. However, a constantly evolving data privacy environment needs to part
of any utility data monetization planning effort. For example, a landmark privacy rights bill took effect
Jan. 1, 2020 in California and will have broad implications for U.S. consumers and businesses. The
California Consumer Privacy Act (CCPA) mandates strict requirements for companies to notify users
about how their user data will be used and monetized along with giving them straightforward tools
for opting out. The law is designed to thwart incidents similar to the Cambridge Analytica scandal and
others. We expect in the coming years a federal law to follow suit. With the approach outlined in this
piece in mind, an initial overview of the types of data and what could be monetized by utilities is
emerging, the more difficult question however, lies in determining the ‘real’ value for that data and
the fee-based structure that is needed for utilities to monetize utility data resources. Overall,
however, utilities across the country should begin to explore alternative means of utilizing fee-based
structures that are endorsed by their regulators for value-added data services.
Looking Ahead - Increasing a Utilities
'Earnings per Byte'
To maximize the potential for internal and external data monetization, utilities should set up a “data
factory” that automates the process of collecting, enriching, transforming, and deriving insights from
data. While this is a complex undertaking requiring clear design principles, the payoff is becoming
evident. By plotting utilities data strategy in relation to build, buy, lease or partner options could
result in sophisticated market ready products. Overall however, we believe that over the decade
utilities can turn data into a strategic asset and truly increase their “earnings per byte” by applying
the steps outlined in this piece.
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