While data monetization is itself a fairly new practice, the fastest-growing companies today were not only anticipating it but have made considerable headway already, with some reporting success in such efforts. A recent McKinsey global survey revealed that these companies are thinking more critically than others about monetizing their data and are using data in a greater number of ways to create value for customers and the business. In particular, they are adding new services to existing offerings, developing new business models, and even directly selling data-based products or utilities.
The study pointed out that those companies established a strong foundation for analytics in a few ways: clear data-and-analytics strategies, better organizational design, and talent-management practices, and a greater emphasis on turning new data-related insights into action.
The foundations of successful data monetization
The successes of high-performing businesses in their data monetization efforts may seem enticing for other companies to initiate their own data monetization efforts. But for them to be successful in these efforts, it is crucial that they first establish the foundations of a successful data-and-analytics program. These are:
Strategy – Analytics leaders of high-performing businesses are nearly twice as likely as others to report enacting a long-term strategy to respond to changes in core business practices. That is why many businesses fail in their data monetization efforts, with the survey reporting that at least 61% of respondents say their companies have not made anything akin to a long-term strategy for analytics.
Organization and talent - While either a decentralized or centralized organizational model for data and analytics activities can work, a hybrid model incorporating elements of both is seen as much more common among analytics leaders of high-performing businesses. Talent is also a key factor although it is also one of the more challenging aspects, with nearly 60% of respondents in that survey saying it is harder to source talent for data and analytics roles.
Leadership and culture - Successful data and analytics programs also require real commitment from business leaders, along with a consistent message from senior leaders on the importance and priority of these efforts. The survey revealed that senior-management involvement in data and analytics activities is the key contributor to reaching their objectives.
The real score
Knowing now what makes some organizations succeed in their data monetization efforts, do other companies possess the elements that will help them succeed as well? Unfortunately, the survey indicates that it is not the case as senior-leader alignment on data and analytics initiatives is still not optimal at many companies. As an example, CEOs are much likelier than other senior executives (53%, vs 10%) to identify themselves as the leaders of their organizations’ data and analytics agenda. They are also more likely to report effectiveness at reaching data and analytics objectives and are less likely to view data scientists and engineers as a pressing talent need. As a result, while others may overwhelmingly cite a lack of senior leadership commitment to the data and analytics policies, CEOs are more likely to cite a lack of financial resources and uncertainty about which actions to take.
The survey revealed two key reasons why businesses are struggling with data monetization. One is the failure to make the wholesale changes required to enter new markets. Secondly, the lack of actual partnership between the business and IT for data monetization to occur in the first place.
The path forward
Getting data monetization right requires significant effort, but it’s becoming critical for staying ahead of traditional competitors and new disruptors. From the McKinsey research, it suggested some crucial steps executives can take to start their data-monetization efforts on the right foot:
Focus first internally - Before companies start monetizing their data, they should take the time to build and/or improve on their data foundations—strategy, design, and architecture—which will help them build the business case and technology platform they need to monetize data effectively. Putting their data to work for internal use cases, such as improving decision-making or optimizing operations, can also serve as a testing ground for their data foundations as well as for the data-monetization models of new data-based businesses.
Go beyond in seeking innovations – Innovation is important in effective data utilization and monetization. As such, business leaders should take the time to seek the most innovative solutions out there, even if such have to be sourced externally. Partnerships are key to achieving this and this could be manifested in different ways such as analytics companies supplementing the organization’s existing capabilities, platform providers hosting tools or solutions, or data providers helping the company gain access to unique data sets. Other possibilities include working with suppliers, customers, or their industry peers to augment and enrich existing data; they can then offer those data as unique add-ons to existing products or services, or even sell the data as part of an entirely new business.
Commit to an end-to-end transformation and get the business involved – Depending on the company’s structure, processes, and goals for their data monetization, the success of data monetization involves reconfiguring operating models and core business functions - from product development to marketing, worker-reskilling programs, and change-management programs aimed at shifting organizational culture, mindsets, and behaviors. These changes will require the full commitment from the C-suite leaders n both business units and technology centers as to why such is important and emphasize the need to dedicate adequate time, human capital, and financial resources to ensure the success of their data monetization efforts.
At its core, data monetization is more effective when they are business-led and focused on the most valuable use cases
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