In 2015, I wrote with my partner in crime Simon Chignard the first book dedicated to analyzing the value of data. The book is called Datanomics. Summer is always a good time to step back, so let’s do so.
Datanomics in 2015
Our objective with that book was to have business leaders and policymakers change their views on big data technologies. At that time the discussion was framed either around technological solutions or around personal data regulation. Our core argument was that the emergence of big data technologies changes the way companies create and capture value, blurs the market boundaries, and challenges the place of the states in society.
To convey our message, we used a lot of examples and one of the contributions of the book is to synthesize this emerging reality and describe three forms of value:
- commodity: when data is bought or sold, either by data brokers or corporations (for example banks or retailers)
- lever: when data is used to improve the performance of an existing business model (reducing costs or increasing revenue)
- asset: when companies use the data they collect as one side of their two-sided business model (like social networks or search engines) or when they use them to increase their bargaining power within an industry or a value chain
We argued that the value as a commodity, though important when you consider the revenues of the data brokers, is much lower compared to the value generated when data is used as a lever or even more as an asset.
Datanomics in 2020
Five years have passed, a lot has been written and done and I used the summer to synthesize what has remained, changed, what we have discovered and what is on the agenda.
- Promises take time to turn into reality. In 2016, McKinsey dedicated a specific study to explain the gap between the promises of value creation stated in their 2011 landmark study and the observations on the field. For example, they measured that only 30% to 40% of the value has been captured in US retail. Their conclusion is that “The biggest barriers companies face in extracting value from data and analytics are organizational; many struggle to incorporate data-driven insights into day-to-day business processes”.
- From promises to responsiveness. In the same idea, the discussion in research moved from promises and descriptions of value creation mechanisms (like we did in our book) to the operational capabilities required to capture the value creation potential. There is no value being good at predicting churn if, in the end, you can’t retain the clients that you predict will go to the competition. In research, the conversation is now organized around the connection between big data resources and capabilities and operational capabilities.
- Commodity: bigger and bigger. More internet users spending more time on internet services grow the stock of data and hence the possibility of some platforms to monetize them. More importantly, the widespread adoption of connected equipment in manufacturing environments triggered a new continent of data generating new opportunities to monetize or aggregate this data. Companies established themselves on this value proposition, data brokers grew bigger and incumbents started monetizing the data of their users and clients. Regulations on data protection weren’t successful in reducing that trend and some analysts question their ability to strike a fair balance between data producers and data users / monetizers.
- Lever: from marketing to operations. Recommendations algorithms, customized marketing campaigns, individual pricing are now common practices. The attention is now on how to use data for improving the efficiency of operations, through automation of tasks, errors and scrap reduction.
- Asset: the biggest value form. These five years have witnessed the dominance of digital platforms when it comes to the valuation on the stock market. Dominance made stronger with the impressive growth of Chinese based platforms. These valuations strongly rely on the data assets of these companies and their ability to use them at a high velocity rate in automated revenue generation.
- The battle is not over between digital and incumbents. One of our main argument was that big data technologies are contributing to redefine market boundaries and establishing a new competition layer. In mobility, car makers strongly compete with each other but new competitors are challenging the value captured by car makers (mobility services, search engines, intermediation platforms, …). Data is the distinctive asset used by them to position in the market. After a first round when incumbents reacted mimicking the digital giants, we see now they are fighting back leveraging their own capabilities and partnering for acquiring digital capabilities.
