Selling data can be a major revenue opportunity. However, navigating the business ecosystem of data trading is not a catwalk. Which is the best option: monetizing directly or through brokers ? Beyond the revenue opportunity some key strategic considerations are to be analysed for data generators who want to monetize their data.
Data as a commodity, a major revenue opportunity
In Datanomics, data has three forms of value
- commodity: when data is bought or sold, either by data brokers or corporations
- lever: when data is used to improve the performance of an existing business model (reducing costs or increasing revenue)
- asset: when companies use the data they collect as one side of their two-sided business model or when they use them to increase their bargaining power within an industry or a value chain
The value of data as a commodity is growing bigger and bigger. More internet users spending more time on internet services grow the stock of data and hence the possibility of some platforms to monetize them. More importantly, the widespread adoption of connected equipment in manufacturing environments opens a new continent of data, generating new opportunities to monetize or aggregate this data. Consequently, companies established themselves on this value proposition, data brokers grow bigger and incumbents monetize the data of their users and clients. Accenture estimated in 2018 the value generated by Iot data marketplaces will be 3 000 B$ in 2030. If you want to know more about valuation methods you can look at this previous post
Options for monetization
The business ecosystem of data monetization is generally composed of 3 types of players. Data generators are the companies who have direct access to data, like telecommunication companies and banks. Data aggregators are companies which source data from data generators, aggregate and sell them. Some buy the data for reselling them and others act as platforms getting a fee on each transaction. Data analytics service providers use data they have access to or source on the market to offer a packaged service, for example digital marketing.
Hence, data generators have three choices to monetize their data:
- offer analytics services leveraging their proprietary data. Mastercard generates 25% of its revenue from leveraging its data for services that include marketing analytics
- sell their data to an aggregator. Vodafone generates a significant revenue trading their users’ data to aggregators.
- sell their data to an analytics service provider. Thasos sources data from telecommunication companies to provide real time customized indicators for hedge funds to help them make their investment decisions.
How to choose
I recently analysed several 10s of cases and here are some insights for data generators who consider monetizing their data.
Several options can be selected simultaneously. Forms of value are not exclusive, the same data can be used as a lever and traded as a commodity. It’s pretty common in retail and telecommunication. Companies use data for improving their operations and trade them for marketing purposes. Similarly, levels of integrations are not exclusive, the same data can be traded through one (or several) aggregators and through a data analytics service provider. Once again, telecommunication companies like Vodafone are good examples.
Balancing control vs cost + speed. Going direct and offering analytics services has a great advantage for data generators: more easily controlling the value capture (no intermediary). But it comes with the cost to develop a fully integrated digital product meeting end-users needs. As these competencies are not always present in data generators companies, the success is really not guaranteed and such moves require a big cultural shift. In addition, when going direct, the company supports all the market exploration costs which can be significant. Licensing data to other companies which would monetize them can be a smart option when the use cases are not already clear.
Identifying the scope relevant for the client. In the BtoB environment, it’s sometimes critical to aggregate data from other providers, which are direct competitors. The difficulty of Airbus to add other planes manufacturers in their platform Skywise is a clear limit to the value proposition of the platform to airlines which operate planes from several manufacturers. In that case, if the data generator cannot establish itself as the industry platform, selling to an aggregator can be a solution.
Defining the role data plays in the strategy. In most cases, data generators which develop a data trading activity have a significant legacy business beyond data trading (selling heavy equipment like Caterpillar for example). The choice of the monetization strategy will then depend on the role data plays in the strategy of the company. If it’s a marginal complementary revenue source, trading to an aggregator usually makes better sense, from a cost and time-to-market perspective. If data is a way to secure and support the legacy business (increasing the value proposition of the product or increasing the technical lock-in for example) a proprietary approach would make more sense. It’s even more the case when data is a component of a software and application offer (eg. Siemens Mindsphere).
Opening for higher value creation than revenue. Sometimes money is not the most valuable resource a company can get out of data. For example, with its Waze for Cities program, Waze trades data from its users with dynamic infrastructure data from the cities. With the exclusivity of the realtime information of road closures by cities, Waze navigation system can claim to have the more updated information, which has much more value for a free-to-use service than the revenue collected selling the data to cities (which are not known to pay high prices for these services).
Developing a flexible approach.As the value of data is captured when used for making a business decision, data monetization strategies are reversible. A lot of companies had their approach change over time because of market conditions changes. As the emergence of industry aggregators opens opportunities of trading for data generators, the launch of a data service by a competitor triggers a reaction.
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