Data Monetization as a Growing Business Revenue Stream: Issues and Challenges

AbstractThe idea that data is an asset is decades old. Today, many companies are cognizant of the critical importance and revenue potential of the data that they generate, process, and analyze. Data is a means to gain deeper... [ view full abstract ]

Abstract

The idea that data is an asset is decades old. Today, many companies are cognizant of the critical importance and revenue potential of the data that they generate, process, and analyze. Data is a means to gain deeper insights and dominance in the marketplace and thereby increase revenues, decrease costs, and grow and engage customers. With Big Data emerging as a powerful competitive tool, organizations are harnessing their abilities and capacities not only to leverage data to enhance organizational performance, but also to monetize huge troves of data through meaning-making of such data to external constituents and partners. Data monetization is a rising tide and organizations that ride this wave will achieve a significant competitive edge in the marketplace. This paper explores the fundamentals of data monetization, its potential and challenges, and identifies steps for success for early adopters.

Introduction:

The role and value of data has shifted from that of an asset that is primarily deployed to strengthen and grow internal assets, such as products, services, business processes and talent, make better internal and external decisions, strengthen the efficiency of business processes, increase profits, decrease costs, deliver higher value to customers, and achieve market dominance to becoming an invaluable asset in its own right. Data has now become a stand-alone asset, product or service that in its own capacity and right can deliver innovation, trigger the development of new products or add value to existing products and services, and capture insights of great value to customers. Data is now a steady and reliable primary source of revenue to forward-thinking and innovative organizations. Since data as a product or service can be monetized it now competes directly with other products and services for limited resources. The ability to monetize data is a strategic issue because of its direct impact and potential on the profits, growth, market power and dominance of an organization.

Data monetization is not a technology issue; it is a business issue.  In other words, it is true that technology and technological skill sets, infrastructure, and capabilities are at the heart of data monetization. However, the vision and strategy to leverage technology in an efficient, timely and innovative way to generate sustainable revenues and market competencies is a strategic issue that deserves the full attention of the C-suite. Senior executives are responsible for carefully defining, establishing and communicating the benefits of monetizing data to the entire organization and to key stakeholders. Monetizing data demands an integrated, well-coordinated, organizational-wide strategy and execution framework and is the foundation for long-term success.

Data as an Ecosystem

Data must also be viewed as a living organism within the enterprise. The entire ecosystem of data within the organization should be carefully and frequently studied and monitored for its shelf life, external value, strengths, weaknesses and revenue potential. Every data-driven interaction, both within and outside the organization, including the interactions among external entities that directly or indirectly influences or impacts the data (for example, two suppliers interacting and collaborating to meet a particular need for the organization), deserve care, attention and thoughtful analysis in order to make a preliminary go or no-go decision as to whether such data has the potential to become a revenue source for the organization. This is because although all organizations generate large volumes of data, not all data is useful enough to create monetary value. These preliminary decisions about the monetary value of a given piece of data are not permanent decisions, but highly dynamic that changes with the shifting needs of the marketplace. Data appreciates and depreciates in value and is time-dependent. Data that is valuable and rare today can easily become trite and copiously available tomorrow.

By viewing data as a living ecosystem that is constantly changing wherein some elements will generate more value than others over time creates a paradigm shift. Data is no longer viewed as just a byproduct or a resource to craft decisions, but instead as a dynamic revenue source and platform for adding strategic value to customers to help them achieve their business objectives as well as for the larger social good. In other words, organizations must frequently and faithfully ask and answer the question, “What value, if any, is this data to anyone, internal or external, to the organization? This approach to data is well summarized as follows: “An enterprise that does not effectively utilize its data assets can be compared to a living entity with a broken nervous and sensory system. A living enterprise that suffers from compromised abilities to hear, feel, see, and smell the dangers and opportunities around is vulnerable and disabled (KPMG – Framing a winning DM strategy)

Although the idea is simple, execution is an enormous challenge that digs into the very core of an organization’s core competencies. The success of data monetization depends on one’s ability to deliver value that the customer thinks is worth paying for over the long-term. In order for data acquisition and monetization efforts to have a positive impact on the bottom line, it must be backed by thoughtful strategies and sound business models that are not cast in stone, but that are incipient and emerging. In other words, data monetization strategies are core to long-term organizational success, the absence of which can result in leaders becoming blind to opportunities or pursuing dead-ends that are not in alignment with the nucleus capacities of their organization. Data monetization should therefore, be an essential part of an organization’s strategy and execution framework and senior leaders must make it a fundamental part of all investments and discussions relating to data.

What is data monetization?

The MIT Center for Information Systems Research (MIT CISR) defines data monetization (DM) as “the act of exchanging information-based offerings for legal tender or something of perceived equivalent value.” (B.H. Wixom, “Cashing In on Your Data,” MIT Sloan CISR Research Briefing, Vol. XIV, No. 8, August 2014.) Data, within the context of monetization, is viewed as an economic good and the data sharing activity an economic transaction. (Bataineh). The Wikipedia defines data monetization as “instituting the discovery, capture, storage, analysis, dissemination, and use of data.” Such data may be created or aggregated in a multitude of ways from internal or external data sources, or as the result of merging internal data with proprietary data sources, or data is streamed or generate from sensors and mobile devices, or any other means. When raw data is used as input to generate timely knowledge and insights about business functions, processes, customer preferences, industry trends and market dynamics in the form of data-derived products and services it is referred to as data monetization. In other words, anything that generates enough value and utility that an external entity is willing to pay for it falls under the umbrella of data monetization.

Why the sudden interest in data monetization? Today, thanks to the extraordinary and highly affordable computing power at one’s fingertips, data storage, information processing, and knowledge management have become affordable and within the reach for more organizations than at any other time in human history. A specific category of information systems and applications that can leverage the advances in computing power and sophisticated analytical software to help organizations achieve a competitive edge in the marketplace is Big Data (McGuire, Manyika, & Chui, 2012). With the increasing prominence and push for “Big Data” as a must-have competitive tool (Bell, 2013) (Datskovsky, 2013) (Bhadani & Kotkar, 2015) (Davenport, 2014) (E. Prescott, 2014) it becomes essential for the entire organization and its associated business units to examine, analyze, and determine the role of Big Data in achieving competitive advantage. Big Data holds the potential to “transform the entire business process” (Wamba, Akter, Edwards, & Chopin, 2015) by changing the way companies innovate, compete, sell, grow, and survive. The effective and timely application of Big Data holds the power to alter and enhance “corporate ecosystems” (Maniyka, 2011), solve complex problems that hitherto may have been beyond reach (Yadav & Kumar, 2015), and deliver exponential value and growth (Brown, Court, & Willmott, 2013).  In other words, what is evident from the emerging strengths of Big Data as a competitive weapon is that no business, regardless of its size, location or industry, can any longer afford to ignore Big Data. This is not to say that existing technology resources are no longer of value and must be replaced with Big Data technologies and techniques. Quite to the contrary, the greatest bang for Big Data may come from aligning Big Data technologies effectively with existing technologies and then carefully identifying where the valuable “stretch” opportunities may be hidden. In addition, the exponential increase in the number of mobile devices and sensors within the larger context of the Internet of Things has highlighted the potential for data as a revenue generator.

Those that play in the data monetization arena may be data generators, aggregators, consumers or all of the above, with the sole intent to cut costs and/or generate revenue by selling or exchanging data or both.  The opportunity to leverage data commercialization is abundant across industries. While financial and credit card companies surface to the top, it is hard to name an industry that does not consume, generate, or aggregate some data that is worthy of monetization. An organization may choose to go broad or deep or both in terms of how it leverages its ability to monetize data. In other words, an organization may do a deep dive in one specific or specialized area, such as supply chain, social media, financial fraud, attracting technical talent, or it may deploy its resources and capacity broadly across a range of processes, functions and decision-making within a given industry or even across industries. This choice of approaches to data monetization is referred to as the information offerings consumption path (Buff et.al) to reflect the gamut of choices that faces an organization as it enters the data monetization market.

A few critical strategic questions that help to shape preliminary discussions as to whether an organization should enter the data monetization domain include the following:

• What data does my enterprise own, generate or process that can be useful to my customers, supply chain partners, and other touch points in my industry or related industries?
• What is the detailed profile of my potential customers, both in my industry and outside, that can benefit from this data?
• Why is this data valuable and how does it align with the business objectives and goals of my customers, present and future?
• What is the nature of the data that can be monetized including its shelf life, complexity, timeliness, direct impact, strategic nature, and deep insights?
• Is my data an independent, stand-alone product or service or can it be embedded into our current products and services to strengthen brand loyalty and upselling?
• What processes, resources and talent does the organization need in the short run and in the long run to sustain the monetization stream? What internal competencies exist to achieve these goals?
• Who are my potential competitors now and in the future?
• What is my business model(s)? How does it compare with that of my competitors?
• Who are potential partners that can strengthen my existing or emerging data monetization capabilities and if so, how?

These are a few preliminary questions an organization must explore before it makes the decision to embark on monetizing its data.

Pathways to Monetization

There are three important paths to make money, according to CISR

• Improve internal processes and generate more profits: UPS gives drivers critical geo information; hospitals that reduce infections; increase sales and strengthen customer satisfaction. Evidence-based organizations – change habits and mindsets – reward what is valued – mandate the use of data – strengthen performance evaluations -
• Wrapping – word coined by CISR – to show information about a product or service can help the customer – eg. FedEx tracking of packages; customers can log into their health data – tracking and monitoring health issues over a period of time. In isolation, it may not add value – but it increases customer loyalty, brings down the pain threshold, perceived value of product goes up – the customers insights increases – Oh! I did not know that!
• Sell data – solve other people’s problems with data that you own and have. This requires deep domain expertise, strong customer engagement, a wish list, clarity around pain points that customers have –

The Consumption Path is made up of three focus areas:

Data (Raw or Processed)

Data raw or processed requires several steps: acquiring, cleaning, sorting, managing etc. This requires heavy investments in databases, data acquisitions strategies and cleaning and updating the data. Even if a company is focusing on raw or processed data, which is input to a customer’s operational or insight-seeking processes, it is essential for the company to have a clear understanding of how the customer will use the data. The nature and availability of raw data determines the profit margins. Is the data readily available? Is it clean or unclean? Is it from public or proprietary databases?

If a company is in the business of monetizing data by converting it from raw to processed, then it needs to understand what the customer needs and get as close to meeting that need as possible. This involves data conversion processes “which often draw upon deep domain expertise to develop taxonomies, dictionaries and business rules, which increase in accuracy, effectiveness and value over time.”

Insights (Reporting or Analytics)

Reports and analytics form the basis of insights. This implies that the monetization process offers knowledge and information that exceeds standard run-of-the-mill reporting. In other words, the company has to deliver value that exceeds what the customer can do on his or her own. Reporting is built on sophisticated data packing technologies. “visualization and dashboard technologies, graphic and interface design expertise, and user training and customer service” are all services that can generate value. “Subscription and self-service pricing models are common for insights offerings.” Analytics is different from reporting because it is predictive and prescriptive.

Action (Process Design or Process Execution)

You have the insights. Now what? What actions should you take? How does one go about identifying the steps to be taken? The services that fall in this area where data and insights are leveraged to action and thereby revenue include consulting, strengthening of processes, outsourcing processes, market analysis to find blue ocean space, leadership development, building internal data science centers and think tanks, and so on.

Sections on revenue generation and implementation challenges will be added if the abstract is accepted. 

Thank you!