Strategies to Monetize Energy Data — How Utilities can Increase their ‘Earnings per Byte’
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.
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 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)
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.
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 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.
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.
Data Sharing and Regulatory Matters
Most utilities have more data than they know what to do with. Forrester reported that 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.
For further details on the digital utility and emerging energy technologies visit indigoadvisorygroup.com/indigoinsights