Capitalism has always been known for being driven by market mechanism. Conventional wisdom argues that markets are considered to be efficient and the interaction of all players gets reflected in the concept of price. This becomes the leading indicator that drives transactions and makes capitalism an efficient economic system. But this is passé and the rules of the game are being rewritten, where the concept of price being the driver no longer holds. More importantly, the day is not far when it will not matter. This is the crux of Viktor Mayer-Schönberger & Thomas Ramge’s new book, Reinventing Capitalism in the Age of Big Data. The structures are more complex today, with customers being more discerning; and the invisible hand can’t capture all the aspirations adequately.
Big data is in, and all companies rely heavily on such information to draw up their strategies. A simple retail shop that tracks what you do in terms of looking at products and choosing those that you buy sends out signals to the outlet and, hence, also the producer as to what your likes and dislikes are. Companies producing consumer products can analyse the purchases taking place in different locations and draw up plans on new products. Also, such data helps managements to decide which areas require a marketing push for future sales.
In fact, there was a time when price became the clinching factor. But in this age of e-commerce, there are other factors that go into a purchase, like colour, size and texture, which make it easier for a customer to choose with a good deal of accuracy. This is also time-saving, as an online portal covers all brands and options. The authors, thus, argue that in a data-rich market, it is no longer possible to condense a multitude of preferences into a single price. In fact, price becomes an oversimplification when it comes to representing any market.
The authors feel that markets will be driven by the pooling of such data. The decisions made on what to produce and the mechanism to deliver it has undergone significant changes with the help of big data, as it gets used along the value chain. Even at the P2P level, conventional relations may break down, as options unfold and all players are able to interact with a wider section of customers or sellers. This is a paradigm shift that has already started.
Mayer-Schönberger and Ramge also spend a few pages on the peer-to-peer lending concept that had caught on, especially at the smaller level of loans, where people lend and borrow based on information available on the counter-parties. We can actually think of this mode of borrowing, based on big-data analytics, gradually getting into the traditional banking space, where both sides are better off. Big data would give potential lenders all the information of the past behaviour of a potential borrower and this would enable the lender to decide whether or not to lend.
This is an important message for regulators across countries who have to build systems to ensure order in the market. This will be a new world for them, especially when regulation is strong when it comes to depositing money; it is a different ballgame when people choose to lend to others on their own volition.
Several companies are also fully into using big data to formulate future strategies. They have started reshaping markets, from energy to transportation and logistics, and could go even into labour and healthcare. Efficient operations of truckers in the US with the use of data is one example. Similarly, we have seen how taxi services like Uber and Ola are more efficient with the use of data, ensuring maximum use of the vehicle and minimum waiting time for customers. Even in education, big data is being used progressively in the West to map teachers and schools with students. The idea is to go beyond ‘good enough’.
The greater use of technology, where the use of artificial intelligence will be the next step can be scary when it comes to jobs. Here, the authors still seem positive and do not see it as a limiting factor, but look at it more from the point of view of enhancing efficiency.
Another issue that is pertinent is the availability of data to everyone. In a traditional market mechanism, it is assumed that all people have access to the same information, which is not true, as there is information asymmetry in all markets that enables sellers to reap higher profits. But in a data-rich economy, the same might happen where some companies or players have access to more data than others. This is a concern and the final answer will only be known with time.
But the authors are convinced that this is the way forward, which will be closer to optimal than the existing market system. Data-rich markets will help people make better decisions and enhance the overall volume of transactions, leading to higher growth. Those who are not used to technology, or the classic Luddites, will have to change, or face being sidelined from the system.
For companies, there will progressively be a choice to automate decision-making, which sounds odd today, but is being pursued by some firms. This is where AI is coming into play, and there could be a future where companies do business with algorithms in place. Such tools are available for trading on stock markets or commodity exchanges where there is no manual intervention. This might sound scary for those whose jobs are at stake, but can’t be ruled out. Also, the absence of human intervention can make the systems too rigid when run on algorithms. A balance surely would be the way out.
Author is chief economist, CARE Ratings