Data monetization is a hot topic. And like the early days of ‘big data,’ there is uncertainty about what the term means and the opportunities it creates.
Nashville’s data community came together a couple of weeks ago to push the data monetization discussion forward. My friend Lydia Jones, founder of InSage, put on the second annual Data Monetization Workshop. She gathered industry leaders from as far away as Australia for a half-day event that shared real-world experiences and raised important questions about how to think about your data as an asset.
I was happy to participate in a panel called “The Arc of the Data Product” — a familiar topic as we work with companies launching data products on our Juicebox platform. Here’s how I like to frame the undercurrent for data products:
Whether it is Fitbit’s personal health dashboard, smart routers, or an analytical dashboard for a SaaS product, data products are about enhancing your existing products to make customers smarter, more engaged, and (hopefully) more loyal.
Creating data products is seldom a linear process — but for the sake of discussion, I laid out the common steps involved with bringing a data product to market.
The discussion on our panel — and the remainder of the workshop — was wide-ranging. Here are a few of the important takeaways from the conversation:
- We need to consider both the direct and indirect business models for data monetization. Direct — selling your data to other organizations, through brokers or marketplaces — is still an emerging model. Nevertheless, several people at the workshop expressed interest in how this data would be valued. Indirect data monetization — creating new products and features from the data — seems to me a more established path in part because it sidesteps challenging questions about data ownership. Like the oil industry, there will be those who make money through the raw materials and those that add value along the many steps in the value chain.
- The hard work is in getting your data right. Many organizations are tempted to race ahead to building data products without realizing they are building on an unstable foundation. Any issues involved with gathering, cleaning, or validating your data will inevitably be revealed in the process of launching a data product.
- How do you deliver value from your data early, so you can buy time to get to long term solutions? This was a common refrain from the data professionals who had been stung by executive teams impatient for results. However, in the eagerness to deliver value from data, I reflected on the inevitable: if you build it, you own it. Even the smallest data report can become an albatross around your neck if customers come to depend on it.
- Data products come in many forms: an insightful report for your customers, a feature that recommends useful actions, or a stand-alone analytical solution that transforms how your customers make decisions. Regardless of the form, they are products that need to be researched, tested, marketed, sold, supported, and refined.
To learn more about our experience with building and launching data products, here are some other resources:
Data Product Resources
How to Build Better Data Products: Getting Started
Data is the Bacon of Business: Lessons on Launching Data Products