#62 Practical start for a data monetization strategy

More and more companies are creating data strategies to match modern times and future needs. Often the data strategy is not only about data. It's more often bundling data and analytics together. The reason is that data alone has value, but when refined and taken into use it's even more valuable. In the majority of cases, AI or ML is not even needed in the beginning. Thus perhaps a more accurate name would be data analytics strategy. That would be accurate especially if your company is primarily looking for improving for example processes and customer understanding. But if your company is aiming to make money with the data, then perhaps the correct term for your strategy would be Data Monetization Strategy.


What is too often forgotten with data strategy implementations is lean thinking. The company might apply lean thinking in service development, but for some reason fails to do so with data and instead goes back to heavy planning and waterfall model.


Lean start to data monetization


Given that your approach is now the Data Monetization Strategy aka making revenue from your available data, my advice is to start with lean thinking. Instead of drafting word documents or long Confluence pages about hypothetical data products and service, start the design process from business perspective and test the product-market fit multiple times before doing anything else.


My standard steps to do with a company asking for my help in data monetization are:

  1. Get to know what is needed by your known customers. Keep your eye always on the customer! Do not start by evaluating your data. That is irrelevant at this stage. Focusing on your data distracts you from the paying customer and their needs. Of course, you need to have a data catalog but that's another story.

  2. Based on the customer needs, pick the most obvious data-driven solution idea that comes to your mind. Trust me, subconsciously you are going to think about a solution that you most likely already have data for.

  3. Iterate the customer needs and possible solution match with Data Product Value Proposition canvas. Spend an hour or two on this. Do this as a group if possible. Include tech people and analytics.

  4. Iterate product / service. Transfer value proposition and needed elements to the Data Product Canvas. Let the technical people discuss and iterate what would it require to build solution as has been sketched. You as Data Product Manager should now focus on iterating the product / service look and feel. By this, I mean pricing, description, value proposition, terms and conditions, brand. You know, the product features. I would do this in the market place which is going to be your number one selected channel of sales. Mock the data commodity in there! Step in the shoes of the customer and start building the offering from the customer point of view. Keep it in mock state, do not start implementing!

  5. Test it! Show the mock product to your team, own organisation and even to some reliable customer you know. Ask for feedback and act accordingly.

Now think about another approach to the problems of your customers. Pick another solution idea and repeat the above. Keep in mind that some customer needs are matched with data streams (API driven data as a service), some with data stories, while others with GUI driven analytics tools and dashboards.


Customer-approved Portfolio of candidates!

After a couple of rounds, you'll have a portfolio of customer-approved data product candidates in your hands. Then you can put those candidates in priority order (Data Product Canvas contains tools/boxes for that). Now you can show that to your board as you plan on how to monetize your data. Get the commitment from the board and start implementing and selling!