Demystifying Data Monetization

Companies have figured out that data can be used in day-to-day operations to reduce costs and grow revenue.

Reading Time: 9 min 


Every company operating today is a data company. Most have access to an array of data on their supply chains, operations, strategic partners, customers, and competitors. Yet most companies are leaving money on the table, with only one in 12 monetizing data to its fullest extent. Data on its own has value, but insights derived from data substantially increase that value. These insights can be used to direct activities as varied as customer segmentation, demand and churn prediction, pricing optimization, retention marketing, and cost management — and they can also command even greater margins when sold externally.

There are two primary paths to data monetization. The first is internal and focuses on leveraging data to improve a company’s operations, productivity, and products and services, and also enable ongoing, personalized dialogs with customers. The second path is external and involves creating new revenue streams by making data available to customers and partners.

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:

  • Geotargeting and geofencing for retailers and tourism.
  • Traffic flow and density planning for ad agencies, government agencies, public transportation companies, city planners, and health care organizations.
  • Fraud detection for financial institutions and credit card companies.
  • Smart targeting and click-stream insights for brands and digital advertisers.
  • Location, layout, and staff planning for retail stores.
  • Internet of things (IoT) applications for a variety of companies.

Successful data monetization requires a careful approach that focuses on the highest-value opportunities that are consistent with a company’s overall business strategy.

Preparing for Data Monetization

John Deere is one company that has created a new source of revenue for itself and value for its farmer customers through data. They did this through a partnership with Cornell University, using Ag-Analytics, Cornell’s data platform that syncs with John Deere’s operation center to access and analyze farm data. Farmers can access analytics tools such as estimators for crop insurance and forecasts for yield and risk management. The platform integrates public data sources, including soil type and weather.

Combined with detailed agricultural data from John Deere, farmers can get estimates on USDA Risk Management Agency insurance, satellite vegetation imagery, and real-time feeds on field conditions. Additionally, the Environmental Defense Fund is partnering with Cornell to investigate the potential for an agricultural sustainability data hub, which would guide farmers in regard to implementing conservation practices. Integrating with John Deere’s Operations Center was an important first step in this process. Longer-term, anonymized, and aggregated data could be used to show larger, regional improvements such as nitrate runoff or nitrous oxide emissions. This data hub would be important for boosting supply-chain transparency between major food companies as a way to quantify progress and verify the practices farmers implement on the ground.

Increasing the value of data. Not all data is valuable or saleable in its raw form — it typically requires harmonization with other sources (for example, market share, meteorological or satellite imagery, or commodity markets) to yield valuable insights. Most companies that enter the data monetization business identify partners who can enhance internally farmed data.

Sizing the market. It is important to not only understand the full array of potential customers (both internal and external), but also all the ways in which they might use and benefit from a company’s data. These use cases include a wide range of possibilities, such as leveraging data from one business unit for use by another part of the company to optimize a broader system or helping an external client with the ability to make better decisions by tapping into data-based solutions packaged by the company.

Maximizing value potential. How much is a company’s data worth? The answer depends on such factors as market demand, the data’s shelf life, competitive offerings, and how potential customers will use that data. To reach maximum value potential, companies need to identify trends and insights from the data that are not easily replicated or available from competitors. Automating the process of generating insights using advanced data analytics (business intelligence) will enhance value.

Pathways to Data Monetization

Internally, there are two main ways companies can monetize their data:

  • Cost reduction — using data to increase productivity or reduce consumption and waste (either of raw materials or low-value activities).
  • Revenue growth — using data to improve sales performance or reduce customer attrition.

Companies are monetizing data to optimize their operations by both reducing operating costs and increasing productivity. As an example, energy and process industry companies (think refineries, hydroelectric dams, and other power-generating facilities) are urgently reengineering to outperform competitors and increase productivity and profitability. As these companies work to meet ever-increasing global demands for energy, external challenges often arise from their operating environment. Rising production costs challenge their bottom lines, and fluctuating prices challenge the top lines. These tightly squeezed margins increase the companies’ reliance on the power of data and analytics. Using data, operators can proactively make better decisions, machines and equipment can be monitored digitally, and analytics can predict and diagnose issues early.

Digital-native disruptors, such as Amazon, Netflix, and Airbnb, and digitally transformed (and savvy) players, such as Capital One and Disney, monetize data internally by gaining an intimate understanding of their customers. They look at things such as demographics, special needs, historical purchases and interactions, shopping behaviors, and pivotal events, offering highly personalized products and services within an end-to-end experience, delighting customers at every touch point — from discovery and purchase to post-purchase and reengagement. This customer-centricity allows internal data monetization, creating competitive advantage.

The external path to data monetization actually offers the greatest opportunities. The three primary external data monetization business models include data as a service, insight as a service, and analytics-enabled platform as a service. The three vary according to revenue potential, value to customers, and degree of data analytics and business sophistication required, as “Business Model Spectrum” illustrates.

Data as a service. Also known as data syndication, this is the simplest of the three business models. Anonymized and aggregated data are sold either to intermediate companies or end customers who mine the data for insights. For example, telecommunications companies provide aggregated and anonymized customer geolocation data to local governments, allowing city planners to design more effective traffic management systems and officials to better establish “smart city” technology solutions. Customers can also be the downstream or upstream players in a company’s value chain: Grocery retailer Kroger captures shopping data generated by its rewards card and sells it to consumer packaged-goods companies thirsty for a deeper understanding of their customers’ shopping habits and evolving tastes and preferences.

Insight as a service. Companies also can combine internal and external data sources, applying advanced analytics to provide actionable insights. AkzoNobel has created a decision-support model for ship operators to enable fuel and CO2 savings. They make available to ship operators and owners an advanced analytics-enabled mobile iOS app that provides continuous performance prediction of coating technologies. This approach empowers vessel operators by allowing financial and performance benefit analysis of coating choices, thus optimizing important investment decisions.

Analytics-enabled platform as a service. This is the most complex of the three business models, and it offers the greatest value to customers. Companies use sophisticated and proprietary algorithms to generate enriched, highly transformed, customized real-time data delivered to customers via cloud-based, self-service platforms. The model allows access to new markets, sometimes building an entirely new business. One example, GE’s Predix platform, provides additional value to customers through data-based services that increase the efficiency of its machines. GE delivers integrated and technology-enabled energy management systems (EMS) for lighting and energy to commercial, industrial, and municipal customers, such as San Diego, California and Jacksonville, Florida. They combine the capabilities of GE’s energy-efficient LEDs, cutting-edge sensors, cloud-based software, and advanced analytical models. Through Predix, GE makes predictive and prescriptive analysis available to its customers around energy use, maintenance, and other outcomes, allowing cost-reduction decisions by simplifying energy processes, leading to automation and operational efficiencies.

Setting up a Data Factory

To maximize the potential for internal and external monetization, companies should set up a “data factory” that automates the process of collecting, enriching, transforming, and deriving insights from data. It’s a complex undertaking requiring a set of design principles that touch on design thinking, lean startup, and agile methodologies for success.

Create a data platform. The architecture and technology stack that support a data monetization business model typically involve a robust enterprise data strategy, and a “data platform” with an intuitive interface to allow analysis, synthesis, modeling, and interaction with the data at a higher, more visual level. The goal is to create a “single source of truth” via data storage, harmonization, and processing. This enables data to be used by internal and external parties. Building the right data platform can require large-scale, multiparty data sharing and scalable computing, typically enabled by public or private cloud options. The four options for creating such platforms — build, buy, lease, or partner — are outlined in the “Data Platform Options” table:

Enable analytics, insights, and outcome. The platform architecture should provide interactive self-service analytics, interactive user interfaces, and data visualization. For instance, the Nielsen Connected Partner Program opened its data pipelines, enabling partner companies and clients to find each other and collaborate in an open-data ecosystem. As a result, Nielsen clients and partners have been able to get better results from more efficient and accurate analytics.

Adopt an operating model. There is no one-size-fits-all organizational structure for driving a data monetization strategy. The most successful companies adapt the structure for each phase of the journey. Regardless of which business model a company adopts, all operating models must address the full range of data monetization operational requirements, including technology, infrastructure, analytics, and platforms, as well as management oversight, organizational structure, key performance indicators, and of course, profit.

Prepare for governance and compliance. Operationalizing a data monetization strategy requires a robust governance model that considers appropriate standards, guidelines, and compliance policies across teams. Companies need to be particularly responsive to outside compliance requirements driven by government regulators or outside partners who contribute to the data monetization business model. In both cases, legal and technical counsels need to shape policy and ensure compliance.

Demonstrate cybersecurity and privacy. While leaders point to cybersecurity as one of their biggest concerns, it is often an afterthought when it comes to solution design. As companies shift their business models and set up data factories, cybersecurity must become one of their core competencies, and they need to be able to demonstrate that sensitive data is being adequately protected. If data monetization operations involve enriching, transforming, and selling data contributed by an external party, companies will also need to comply with the requirements of their external data suppliers.

Turning Data Into a Strategic Asset

All companies are data companies, and most have a substantial amount of untapped, underutilized data, which could unlock tremendous financial value.

There are three key components to complementing and even transforming a business model with a data factory:

  • Identify potential internal and external monetization opportunities.
  • Appraise your data, identify any hidden opportunities to enrich it, and increase insight value.
  • Develop a strong monetization strategy and assess business opportunities, dependencies, and capability gaps.

Companies can increase their “earnings per byte” by reimagining a future where they not only maximize value creation internally, but also create a market for their highly valuable data and insights. This approach will mean they are not only changing the playing field, but reinventing the game, and securing market dominance early on.



The authors would like to acknowledge and thank Eric Stettler, Chris Hagen, and Sally Quan for their valuable contributions.

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