Executive update: make data monetization strategies real
Combine best of both worlds!
A few years ago most data strategies were focused on 3 pillars:
- Efficiency, e.g. by robotics for automated data flows or better reporting based on good data quality.
- Compliance for data-driven regulations, e.g. for financial solvency, privacy and medical devices.
- Innovation, by monetizing new products based on (analytics) insights.
However, companies find that the relevance of data strategies is slowly decreasing, partially due the ever extending growth of available — good & compliant — data as well as data already build in as efficiency. Therefore companies have looked into the possibility to extract value out of data by generating (new) insights out of data through data scientists & AI specialists. The steep rise of these latter has already led to an insight ‘tsunami’, also decreasing this potential value out of data. Companies are now looking into ways to go beyond these more traditional values and look towards new ways for value, i.e, data monetization.
“…. it seems that the value of data is decreasing. This is not correct. The value of data is shifting”
So, although it seems that the value of data is decreasing. This is not correct. The value of data is shifting. And the shift is towards the ‘old-skool’ product manufacturing, but this time it will include data into the product to drive new value.
This has a clear advantage. Manufactured products makes the value of data more tangible, more recognizable for consumers and more relevant for B2B buyers. Making the value of data better understandable from multiple perspectives, e.g., consumers, sales directors or chief innovation officers.
How to create new value out of data is best understood by two practical examples of products that are integrated with data. Take a car or toothbrush that is integrated with data. That data is valuable due to its link to the product. The car captures data regarding driving behavior which can be shared or sold (as insight or otherwise sufficiently anonymized!) to insurance companies. Thereby monetizing the value of data. The toothbrush captures data which enables personalized dental guidance to consumers, increasing the value for consumers, which will increase sales and again monetizes the value of data. So it is the connection product & data that adds more value than the more traditional data analytics insights products that have been the main focus of most companies. There are multiple causes for this increased value, e.g., personalisation is an extensive driver for B2C sales and personalisation is to a large part based on product&data, based on the product requirements, relevant data can be targeted early in the MVP phase which then shortens time-to-market of the new product and combining multiple data types into a product enhances the product features making it more desirable for clients.
……data monetization connects two completely different worlds, i.e., seeking opportunities and mitigating risks.
Nothing is ever easy. There is always a challenge. In this case, it is the fact that data monetization connects two completely different worlds, i.e., seeking opportunities and mitigating risks.
It links together the world of fast moving sales activities & seizing value opportunities and the risk-averse & structured world of data. Understanding & integrating the value of these opposites is key for successful data monetization.
Integration of both worlds depends on understanding and integrating two topics: 1) value types and 2) data types*.
Value types
There are multiple value types with regard to data. The commercial use is the most obvious, e.g. selling**. Data-as-a-Service in accordance with legal requirements as well as ethical guidelines of the company. Or cross-selling products by offering personalized experience with additional products.
Also take into account value for the organisation, e.g. because the products enables further data integration helping the company to become more data driven. Or organizing a hackathon for the new product, thereby communicating that your organisation is very much a data-driven company, building trust to your clients.
There are many value type possibilities, that have a minor or major potential for your organisation. Start with a broad focus and then prioritize base on feasibility, relevance, impact on the organisation (e.g. technological, governance, changing processes) and the estimated commercial value. The impact can be used to plan and prioritize a plan of approach for your data monetization strategy as well as the product life cycle — from MVP up until a full product suite.
In my experience, the topic of data is often not very known within a commercial environment. It is treated as a given. A genuine understanding how data will benefit the (new) product will strengthen commercial activities.
Data types
Different data types have different impact on possibilities to drive value out of data. And combining data types will also enhance the relevance for value propositions.
Take a broad perspective when looking to data types. Identify if your value cased includes e.g., personal data, external data, technical data (e.g. for app usage), sensor data and/or GPS data. Each data type will add its specific value. For instance with personal data, a personalized experience can be generated. Personalized experience is often preferred by consumers, thereby increasing the value of your product. GPS data adds to that personalized experience. By using external data for trusted partners, the synergy of your own data and external data is further increasing the value of the product.
Identifying different data types — including relevant combinations — gives advantageous insights into the potential value of data. Here also, the impact on effort capturing, providing & governing the data types will affect the roll-out planning for data monetization.
Most companies have data stewards in place to enable good and consistent data types. Please note that in most cases, data stewards are focused on the more traditional value of efficiency, compliance and innovation by data insights. The impact of data monetization is often an additional set of capabilities and expertise for data stewards. In my view, data stewardship should be further development to be able to support data monetization.
Plan of approach based on value types and data types
For the implementation of data value products, in practice there is a differentiation between MVP, initial products and the further development of a complete product suite. This is an additional factor where value types and data types will have an affect. The affect works both ways. For example, in the MVP phase only a limited set of value & data types is usually applicable, and this extends per phase. And based on lessons learned from MVP phase and forward will make additional value types and relevant data types more visible and tangible. Increasing the development of new product use-cases.
Final note, any innovation driven data strategy should be supported by a trusted environment where data can safely be captured, stored, shared, processed, tested and ultimately made into a product that is in accordance with the clients expectations. For such a trusted environment, data management measures (hence the more structural world of data — as described above) should be in place, i.e., data quality controls, data entry controls, identity&access management, data life cycle management, consent for usage, provenance and consistency controls for data definitions. The company must have this trusted environment ready and available to facilitate the (fast moving — as described above) sales & product innovation world.
Within Philips Product Engineering, data driven consulting has build a methodology to support data monetization strategies. For information you can contact me at my Linkedin profile
* please note: here I do not refer to are not the (master) data objects such as finance data or HR data.
** it can not be stressed enough, selling data always requires a strict governance regime and should always be safe, compliant and ethical. In my experience this should always include at least: good data quality, sufficient risk & control — related to regulation such as GDPR, AVG, HIPAA, FDA, EU-MDR, controlled integration, processes, responsibilities & ownership and (audit) trails & provenance.
