The world of Formula 1 is interesting; originally, one would think it is just a bunch of cars circling until one finishes first. However, once you look closely, you can see that things are different from what they appear to be and Formula 1 is one of the most data-driven sport. However, all the technological...
Data sharing is a complex business ecosystem where data generators, aggregators and services providers compete to capture the value of data produced by smartphones, cars, public transport and micro mobility shared services. What insights can we gain through the analysis of 37 companies? How connected is the information market to the transport service market?
As data and AI are major value creation drivers, mastering the related resources and capabilities leads to competitive advantage. With the usage growth of neural networks, a new resource is critical: electricity. That’s probably why the battle on AI will be also a battle on energy.
Selling data can be a major revenue opportunity. However, navigating the business ecosystem of data trading is not a catwalk. Which is the best option: monetizing directly or through brokers ? Beyond the revenue opportunity some key strategic considerations are to be analysed for data generators who want to monetize their data.
The European Commission released recently a proposal for a regulation on Artificial Intelligence (AI). Though there is still a long process to take place before the regulation is enforced, it will impact Datanomics, i.e. value creation opportunities and competition dynamics related to data and analytics. What would change should the regulation implemented as described? What...
As data is associated with numerous value creation opportunities, protecting them and making sure others cannot access them seems a smart move to control the value capture. Though counter-intuitive at first, there is a clear case for value capture while sharing data. However, sharing doesn’t always mean the same and several options are possible. Why...
When it comes to offering data-services, selling data assets, entering into partnerships and data-sharing agreements, agreeing on the value of data is critical. What are the methods to discover the value of data? Which strategic implications?
Recommendations systems are powerful revenue engines: the better the system, the longer the users stay on the platform or the more the clients spend on the website. But systems are numerous and some perform better than others. In particular, some systems are automated and based on other users behaviours whereas others are more manual and...
Proprietary and closed data strategy has clear advantages but in some cases engaging in open data proves to have a bigger impact on competitive advantage.
Recommendation systems are powerful revenue engines. Different types and logics exist but a lot of them rely strongly on personal data. Internet users and regulators exert growing pressures on how personal data is used which curb the ability of platforms to collect them. Recent research investigated how perceived usefulness could play a significant role in the...
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