15 October 2020

Data Value Mapping: What Value Lies Beyond Your Product?

A customer buys your product for a particular application. But does that application also define the limits of the value your business can create? For me, this is where an interesting question begins. Understanding customer needs remains essential, but looking only at current use may prevent you from seeing what other insights and opportunities lie within the product or process.

That is why I also like to look at the world from the perspective of the product and the process. Not to lose sight of the customer, but to look beyond the first application. What does the product observe? What conditions does it encounter? What knowledge emerges during use? This shifts the focus from what we sell today to the value we could make possible through it.

With this in mind, I began using the Empathy Mapping tool differently. Usually, the user is at the centre. I placed the product in the middle and asked similar questions: what does it ‘see’, what does it ‘feel’ and what does it do? Not literally, but as a way to explore which conditions sensors, measuring instruments and process data could reveal.

When I ask what a smart product or process means, the conversation often starts with benefits for the direct customer. But who else in the ecosystem could benefit from those insights? Think of a maintenance partner seeking a better understanding of wear, or a process partner looking to recognise recurring deviations. These are not automatic new revenue streams, but potential value propositions worth exploring.

I call this exercise Data Value Mapping. You map not just potential data, but above all the connection between observation, insight and value: what can we measure, what can we learn from it, who is it relevant to and which decision does it improve? A measurement is not yet a useful insight, and a useful insight is not yet a viable business model.

Imagine a vehicle mapping a road network. During the same journey, additional measuring instruments could also collect information about the road surface. The interesting question is not how many sensors you can add, but who needs that information and what they can do with it. Are the measurements reliable enough? Can you deliver the insights repeatedly without starting a separate bespoke project each time? And are the costs justified by the value to the recipient?

Collecting more data is not automatically better. Start with a relevant question and explore which data are needed to answer it. Privacy, consent where required, usage rights, security and ethical implications should be part of that assessment from the outset. The same applies to algorithms and artificial intelligence: they become relevant when they enable a useful insight or a better decision, not simply because a lot of data happen to be available.

For me, this is why Data Value Mapping connects with Scalendi Growth Logic: look beyond the current product to the value you create, the capabilities that make it possible and the conditions under which you can deliver that value repeatedly. Data are then not an end in themselves, but a potential building block for growth. The question is not just ‘How do we make this product smarter?’ but above all ‘What value can we build from this product that remains relevant beyond its first application?’