Data monetization might sound simple and yes the basics of it are pretty simple after you learn the principles. What principles? That is a good question. A couple of nights ago I stopped and started to think what would be the four items/principles/concepts I would choose if someone would ask what are the cornerstones of data monetization. Based on the training I've done so far in a couple of dozen companies, I picked:
Distribution (architecture)
Servitization (value creation)
Subscription (business model)
Self-service (scaling)
Distribution - architecture
Currently a major trend is distribution. It does not concern just data, but almost everything. In this discussion of distribution DATA MESH is at the center of it.
Data mesh is a new approach based on a modern, distributed architecture for analytical data management. It enables end users to easily access and query data where it lives without first transporting it to a data lake or data warehouse. The decentralized strategy of data mesh distributes data ownership to domain-specific teams that manage, own, and serve the data as a product.
decentralized strategy of data mesh distributes data
In short, data mesh concept is built on top of again four principles: Data Ownership by Domain, Data as a Product, Data is Available Everywhere, Self Serve, and Data is Governed Wherever it is.
Distribution and data mesh approach is said to tackle at least some of the traditional centralized data storage problems:
Centralizing all data onto one platform becomes problematic for large enterprises that have an extensive and rapidly changing variety of data sources and use cases.
responding to new needs requires changes in the whole data pipeline, which makes it difficult to stay agile and responsive.
the centralized solving of data requests leads to long response times due to disconnected teams that cannot understand the needs of business or other teams needs. Long lead times may suffocate innovative prototyping and learning.
data experts become too specialized in their area of expertise, and may create platform level bottlenecks due to the difficulty of finding specific data engineering talent.
I encourage you to read more about data mesh, discuss with your tech people and give it a chance. Data mesh is relatively new concept and thus long track record of proven benefits are hard to find in large scale. Don't expect it it solve your problems alone, but offer one tool in the stack.
Servitization - Value creation
The movies as products did not disappear when we started to use Netflix. What changed was what we are paying for and product became fully digital. We used to pay to own the physical DVD. Now we pay for the 24/7 access to the movies and tv series (digital products). We don’t need or want to own the movies since we can access the content, again and again, every day and every hour. Owning has become obsolete. We are witnessing the same phenomenon with data commodities now.
We are now entering the data servitization period. The more traditional data product-driven paradigm was about productizing data, datasets, ownership, data integration, and customer pull. The new Data as a Service paradigm is about servitization, data streams, access and rights to data, subscription, and pushing changes to the customer.
The rule of thumb can be summarized in two sentences. First, productize data for efficient internal reuse and sales as well as feeding the partner value chain. Secondly, servitize data for maximum revenue and direct value for customers.
Subscription - business model
I touched this already in the above in the Netflix example. We subscribe to Netflix. We pay monthly fees. In other services we might have annual payment schedules as well, but that is still subscription - just a longer billing period.
What we are witnessing is the Subscription Economy. The Subscription Economy is a phrase, coined by Zuora, describing the new business landscape in which traditional pay-per-product (or service) companies are moving toward subscription-based business models.
Benefits of subscription-based pricing in a nutshell:
Multi-Tier Opportunities
Personalization Potential.
Persistent Service.
Immediate Access to New Features.
Better Budgeting.
Builds Relationships.
Exceptional Reliability.
In the Software as a Service world, in particular, the subscription model is used almost exclusively to both fund and provide ongoing service to customers. Instead of buying an item or product outright, customers pay a monthly or annual fee to retain access.
We are moving away from purchasing datasets and going towards byuing access to data streams or services.
The same is going to happen to data as well. We are moving away from purchasing datasets and going towards byuing access to data streams or services. Payment model is not one time payment as is with products, but subscription which generates recurring revenue to provider.
Self-service - scaling
According to the Salesforce “State of the Connected Customer” report, 59% of consumers and 71% of business buyers say self-service availability impacts their loyalty. A self-service model is a great way to automate your SaaS sales funnel and provides multiple benefits to increase revenue and achieve exponential growth.
Self-service is any point of sales or services in which a customer doesn’t interact with a human being. From automated password recovery and app downloads to complex support bots and extensive documentation wikis. If it’s something that customers can do by themselves without any human assistance then it’s self-service. Plain and simple.
In data economy self-service is translated to customer's capability to take data products and services into use without your human-driven (support/sales staff) intervention. Customers onboard to your data platform, find your data commodities in the market places, find technical details on how to use your data service, and so on. All that and more by themselves!
Without self-service your data commodity sales is going to take a deep dive - it will not prosper and generate revenue.