Challenges for data sharing in the outcome economy and how startups tend to address them (part 1 — technical challenges)
When you think of yourself from a perspective of consumer you will most definitively agree that data sharing among providers of different goods and services will significantly increase the value. If you are taking your kids in a self-driving car to an amusement park you will love get there using the fastest route, directly arrive at the vacant parking lot nearest to the park, get your tickets automatically uploaded and charged for as you enter the gates and get suggestions which rides to take based on your kids interests and age. All this requires data exchange among several service providers and seems perfectly possible, at least in theory.
As a service provider, say an amusement park, you also benefit — you provide better experience for your customers and increase engagement. You can use the data for planning and product development. The data that you aggregate may even help you improve the business model (for example, if you only charge customers who look happy with the experience based on facial recognition data). The data may be valuable to other business actors and open up new business opportunities.
So, if data sharing may create win-win situations, why don’t we see much data sharing? And more importantly, if I am start-up, how do I minimise those challenges for my product/service?
The number of possible reasons will be covered in the article series.
A large number of data-driven solutions are designed having some specific infrastructure of hardware or software in mind, like communication protocols, data exchange formats or platforms used.
The common strategy to overcome those is to stick to specific standards used by prospective clients or partners.
Another useful strategy would be to design the product based not only on the direct value your product generates, but also on the outcome. In other words, you need to consider what other products/services your customers use together with your product to reach their goal.
For example, you are developing an app for automated diagnostics of certain medical conditions. The end user for the app would be the private medical practitioners. The value for them would be to reduce the time required for diagnostics, thus they can free up some time to help more patients, potentially helping to increase revenues. Another valuable outcome would be to make diagnostics more precise, thus reducing the risks for relapse, lowering the potential societal costs. This value proposition would be your number one argument for public healthcare organisations.
Having the outcome in mind your product design needs to address the following questions:
- Which other systems my customers use now (like EHR, medical database software, other types of diagnosis software for accompanying diseases, health tracking apps, hospital management software, etc.)?
- Which types of technical standards are employed by those systems?
- How can we design our product to enable data synchronisation across multiple systems?
- How can we open up for data synchronisation from other providers to improve our own product?
Next issues to cover
- Regulatory compliance issues
- Legal frameworks for data sharing