Data monetization, at its simplest, is the process of turning data into bottom-line value for a company -- often through improving efficiency and/or customer experience, and building customer loyalty as a result.
This may sound simple, but in practice, it’s anything but. Good data, advanced analytics and real-time decision making are all required to monetize data successfully.
Three generations of analytics
Analytics has moved a long way from its early stages, with relatively small, structured data sources. Back then, creating models was a time-consuming business. Statisticians and modelers spent most of their time well away from business people. Most importantly, decisions didn’t depend on analytics, and nobody competed on analytics.
Fast forward to stage 2 of the development of analytics: The data sources got a lot larger and looser. At the same time, the capacity to deal with data also expanded exponentially, supported by the emergence of data scientists. Online firms began to (very tentatively) provide data-based services.
We’re now moving rapidly into and through stage 3 of the evolution of analytics. At this stage, analytics is integral to the business, and the ability to use analytics is seen as a strategic asset. Analytical tools are available to ordinary business users, at the point at which they’re needed, which means that insights can be delivered rapidly -- sometimes in real time.
However, more important even than ready availability is a cultural shift. Decisions have become data-based, not driven by intuition or gut feeling; the use of data is essential. Analytics is now embedded into company operations and decision making and has become the norm. People expect to use analytics to support their decisions and drawing on data is standard.
Businesses routinely create and sell data-based services and products, and analytics has become a key platform for competition and an important tool for creating competitive advantage. If stage one is marked by traditional analytics, and stage 2 by big data, then stage 3 is when analytics starts to have a genuine business impact on what we might call the data economy.
The emerging ecosystem for data monetization
Successful monetization is likely to depend heavily on collaboration with other organizations within the ecosystem, some smaller and some larger. Smaller brands and companies are likely to need to collaborate with each other on a relatively level basis: A partnership model.
Organizations will share data and work collaboratively, in return for getting relevant data from other organizations within the ecosystem. The types of organizations we’re talking about here include small banks and retailers, travel and entertainment providers, and even small local shops. None is big enough to create new business models with only their own customer data, although there are hundreds in any given country.
Bigger brands and companies are likely to end up leading ecosystems of their own. This will include big retail chains, large banks and insurance companies, and major consumer goods and lifestyle brands.
Travel-related ecosystems are already starting to emerge, including alliances of airlines and hotels. This type of model is also seen, for example, in automobile manufacturing, where there’s a leading brand (the car manufacturer), with supporting companies in the form of suppliers and customers. There are likely to be dozens of these ecosystems in any given country, but probably no more than 100 in any place.
There is, however, another possible ecosystem: The new hub ecosystem. Here, spin-offs broker information. These are focused on transformation, not on business as usual, and are all about disruption. The companies involved often started as joint ventures to focus on taking advantage of one particular opportunity, but with minimal risk to any particular parent company.
The successful innovators have, however, expanded enormously since they got started. For example, the banking hub ecosystem started from payment processing, but now threatens to disrupt traditional banking and financial services in many more ways than was ever envisioned as these companies take advantage of opportunities to monetize data.
For example, utilities historically had a paper-based relationship with customers; the introduction of smart meters threatens to disrupt traditional energy supply in many more ways than anyone imagined as these utilities take advantage of opportunities to monetize data.
Of course, that means the reliability of data is more important than ever before. Find out more about how utilities take advantage of IoT data in the white paper IoT Analytics in Practice, Blue Hill Research - Analyst Insight.
