Keeping Up With Data #107
5 minutes for 5 hours’ worth of reading
The topic of data monetisation is becoming increasingly hot. A week doesn’t go by for me without having at least one discussion on the matter.
But the term is somewhat misleading and often misunderstood.
Data monetisation is an umbrella term covering all different ways of turning data into economic value. And while there are companies (and people like the one in the picture) selling their own data, most of data monetisation activities are not that direct.
There are many internal data monetisation approaches (creating value through better decision making, automation, optimisation, etc.) as well as external ones. But even when it comes to external approaches, direct sale of data is an extreme case.
Firstly, because selling raw data decreases (or completely eliminates) ones competitive advantage. And secondly, selling ‘raw materials’ offers lower margin than providing finished products or services.
Which is why many external data monetisation examples are about selling insights or providing data-powered products. The buyers are happy to pay more for an answer to their question or solution to their problem without being required to get there on their own, i.e., not wasting time and resources. And sellers can monetise the same data again and again, thus exploring the re-usable nature of data.
When thinking about external data monetisation, start by identifying someone else’s problem that can be solved with your data, and then try to get as close to the solution as possible.
All while respecting relevant ethical, legal and reputational boundaries.
This week’s reading list looks at data creating competitive advantage, counting what counts, and using design thinking to increase the success rate of data science projects.
- When Data Creates Competitive Advantage: Data is an asset unlike any other. And it can create a significant competitive edge. But not always. When does it happen? “It all depends on whether the data offers high and lasting value, is proprietary, leads to improvements that can’t be easily imitated, or generates insights that can be quickly incorporated.” Data can be always used to make better decisions or improve the efficiency of operations. But in and of itself it won’t guarantee a competitive advantage. (HBR)
- We Need To Get Back To Counting What Counts: The article starts with a quote of Albert Einstein: “Not everything that can be counted counts and not everything that counts can be counted” and demonstrates that being data-driven can be counter-productive if we are focusing on the wrong metrics. “We’ve become finance-obsessed but lost track of economics. […] Economics should serve people, not the other way around.” In the race for profit, companies shouldn’t forget about their mission. (Greg Satell @ Medium)
- Design Thinking Improves Your Data Science: I often say that data science is about solving business problems with data and analytics. But in order to succeed, we first need to focus on the right problem. Failing at getting the problem right is imho the single most important root cause of unsuccessful data science initiatives. One way to mitigate the risk of focusing on a wrong problem is to use design thinking — “an iterative process in which you seek to understand your users, challenge assumptions, redefine problems and create innovative solutions which you can prototype and test.” (Taylor Jensen @ tds)
Enjoy the weekend!
