Data Monetization: Deriving Value from Information
Nowadays, organizations are faced with the constant production, streaming, and processing of data. But this can become overwhelming, and decision makers often don’t know where to begin in digesting the data and gleaning insights from it. In response, enterprises have started to identify innovative and nontraditional methods to utilize this data and one of the most promising methods is data monetization.
Data monetization is the process of increasing the income of the organization by extracting the value of data. It involves the top-line and/or bottom-line generation of an enterprise from the journey of data to information to knowledge and then wisdom through actions and decisions. This could come through internal utilization of the data to improve process or identify new opportunities and even sell the data to third-parties or create new data-related services. This opens up the flood-gates of potential new channels with revenue generation or cost savings.
There are two types of data monetization that we will illustrate below.
Direct Monetization
Direct monetization is an external facing strategy that involves both proprietary and public data to create product offerings. It includes data as a product, data as a service, and data platform as a service. Direct monetization results in directly transforming data into income-generating assets. Some examples include
· Data as a product: The company sells data, whether in raw, semi-processed or encrypted form. In this case, the buyer receives the data, usually in a file, as is, and then processes it and derives insights. Hosting, processing, and maintenance are within the responsibilities of the buyer.
· Data as a service: The main difference from the previous example is that, in this case, the buyer doesn’t have to bother with storage and can use and stop the subscription when needed. They can also request data for specific needs. Data as a service utilizes modern technologies for the exchange of data, including APIs.
· Data platform as a service: This format of data selling takes it to the next level — the seller provides a unified platform where the buyer can ingest, process, manage, monitor, and analyze data to draw insights. Here the responsibility of security, governance, and privacy is taken care of by the seller as part of the services provided. This format makes it easier for the buyer to consume data.
Indirect monetization
Indirect monetization is an inward facing strategy using data that the company has already captured, and then leveraging the data across business units to improve current offerings or develop new ones. This allows the business to retrospect on the data collected and then uncover opportunities for additional revenue channels, such as new products or services, and/or boost operational performance and profitability. Examples include:
· Data-driven business models: Companies have detailed customer data including demographics, purchase data, and even data streams of customer activities on both digital and physical channels. Using this, they often come up with opportunities such as product recommendations, marketing campaigns, and even business models like hyper-localization. These opportunities often involve utilization of prediction analytics to derive insights.
· Data-driven performance optimization: This involves analyzing, re-engineering, and even automating business processes. Enterprises identify redundancies or opportunities of productivity optimization using more internal-facing data like call center performance data and IoT data.
Modes of Data Value Generation
The key step towards developing a data monetization strategy is to align business with data teams so that they can collaborate to identify these deep insights and recognize the opportunities that can be monetized. Business teams often don’t speak the language of data. Hence, there is a need for more interaction between the two teams.
Business teams and data teams find common ground through specific data value modes. These are specific business themes by which data insights-based opportunities can be classified. Examples of such data value modes include:
1. Improved competitive position
2. New and Improved products
3. Informationalization (building data into products and services)
4. Improved human capabilities
5. Improved risk management
These specific value modes should facilitate disciplined thinking, help narrow a team’s focus, and drive the right conversations during the process of ideating the best mechanisms of data monetization.
Strategies for Monetizing Data Use
Once the modes of data monetization are determined, the next step is to determine the specific strategy by which data monetization should take place. Across the globe companies broadly follow three strategies for monetizing data use:
1. Make data available to users: These can be in the form of data products and data as a service, either internal or external. They can improve productivity, processes, and outcomes, and can create new business for products or services.
2. Trade data: This includes data-as-a-service/insights-as-a-service and an insights platform for business units, teams, partners, and/or other organizations.
3. Sell data: This includes data-as-a-service/insights-as-a-service and an insights platform for clients.
The commercial value of data modes is dependent on the target customer (internal vs. external) and has a relative impact on decision making (data vs insights/ML models and insights capabilities).
A data monetization strategy clearly elucidates:
1. Who will leverage the data, including external or internal users?
2. Where will data come from, which can be proprietary data, public data, and/or a mix of both?
3. What the value proposition is, which can include data as a service, insight as a service, or platform as a service?
The above points can lead us to choose one of the data monetization strategies mentioned above.
Capabilities Needed to Execute Data Monetization
Once the enterprise determines the strategy needed for data monetization, it needs five enterprise capabilities to execute on data monetization strategies:
1. Data assets: A conscious effort to dam the data flow and maintain quality of the data.
2. A data platform: A scalable solution allowing for transformation and eventual harvesting of data insights leading to monetization.
3. Data science capabilities: Resources necessary to take data analysis to a higher level yielding robust insights.
4. Acceptable data use cases: Unless it is clear what the data is to be used for, it is not useful in its nascent form.
5. Customer understanding: Use cases arise only when the enterprise spends enough time to understand what its customers (identified or yet to be identified) need to bridge the gap.
It is imperative that organizations assess their current capabilities, map their target capabilities, and chart out the strategy for data monetization. Following these steps will help truly generate direct or indirect value through data, which is a critical goal for most industries today.
