As more and more organizations decide to strategically enter the data economy they start to have a lot of questions about the execution of doing so:
- How should we select the correct data to sell?
- How do we “productize” our data?
- What price should we charge?
- What is the process to securely transfer the data?
In order to help organizations commercialize data and join the data economy, DataStreamX created a five-part framework for navigating this exhilarating process called, “The Visionary Leader’s Guide to Data Monetization.”
The first and most introspective step of the journey is determining if data has any value to others. Given the amount of data organizations collect, this is no easy task.
In order to help simplify this assessment, DataStreamX advises organizations to ask themselves three questions in order to establish “product-market fit.”
- What data do we currently collect/could we collect?
- What value can the data provide to external users?
- What alternatives to our data exist?
What data do you currently collect/could we collect?
The goal of asking this question is to start to recognize what data could be valueable. In order to answer this question, it is essential to take an inventory of the general category of data types that the organization produces. Some examples of data types include: geo-positioning, point-of-sale, demographics, usage, sentiment, trigger events, etc.
Raw vs. Segmented Data
Along with the category of data that your organization produces, you will also want to be aware of the “condition” of the data, essentially: is it raw or segmented data?
As we have previously discussed (link to first article), raw format data is often times the most valuable, even though it might have false positives and inconsistencies. However, this isn't always the case.
In other circumstances, datasets are more valuable when segmented into smaller and more focused data products, which can then appeal to different user groups. These focused datasets can take the form of analysed and enriched data such as insights, indices, patterns and associations.
For more information on selecting and structuring data, check out the whitepaper, "The Practical Guide to Selecting and Structuring Data Products."
What value can the data provide to external users?
The goal of this question is to understand the use cases and the market potential of your data. This question is all about conducting a market assessment. Generally, prospective data vendors can take two approaches when deciding what data are valuable:
- Talk to potential buyers and external experts to define the scope of data
- Test the market directly and offer what the vendor thinks is the best product
The first approach would yield the most clarity and would help with pricing. Common buyers of data include management and technology consultants, multinational corporations, academics, and data analytics firms. Speaking with these buyer groups would help clarify what appetite exists for certain data. In addition, external experts such as channel partners, could provide insight into reference prices and an objective understanding of the market opportunity.
The second approach is a bit risky and could have drastic consequences. Without a concrete understanding of the data market economy, a pricing hunch is nothing more than a high stakes guess. You might get lucky and get it right, or you could miss the board completely and have little insight into where you went wrong.
For more information on assessing the value of your data, check out the whitepaper, "The Practical Guide to Pricing Data Products."
What alternatives to our data exist?
The goal of this question is to understand the competitive positioning of your data and determine the price premium you could command. In order to properly conduct this assesment, data producers should consider:
- Similar datasets/those produced by competitors
- Alternatives datasets that could be used to solve the same problem
- If data is needed at all
Upon answering these three questions, data producers should have an understanding of what data is valuable and how it can address a market need.
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