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A special sector of the digital economy is data science, data analytics and Big Data. In a new book by Viktor Mayer-Schonberger and Thomas Range entitled Reinventing Capitalism in the Age of Big Data, two provocative, interrelated arguments are made as examined by John Thornhill in his column in the Financial Times (April 3, 2018) entitled “Digital superstars threaten traditional companies.” There are two trends described in the book. First, data have largely superseded price as the most effective signaling mechanism in the economy. Second, data-rich markets will increasingly render the traditional company obsolete, with massive consequences for our economies and workforces.
All of us who have taken an elementary course in economics learned that the law of supply and demand is the very foundation of microeconomics. Price has been the central variable in this model of supply and demand, working as a miraculous market mechanism, putting buyers and sellers together, consumers and producers. It is estimated that globally some $100 trillion of transactions take place every year guided by the so-called “invisible hand” of the market. As one of the founders of liberal economics, Austrian Friedrich von Hayek put it: “The market is essentially an ordering mechanism growing up without anybody wholly understanding it, that enables us to utilize widely dispersed information about the significance of circumstances of which we are mostly ignorant.”
Enter data analytics and Big Data. Data-rich platforms have, in some areas, invented a better ordering mechanism that can structure information and reduce ignorance. They can now match buyers and sellers taking into account multiple preferences, such as personal taste, timing and convenience, rather than just price. If and when data supersede price as more efficient economic information capsules, there is the danger that many traditional companies’ existence will be threatened. Companies generally exist because they can co-ordinate some human action more efficiently than decentralized markets. They act as legal entities, raise capital, bundle risks, and separate management of assets from ownership. The authors of Reinventing Capitalism in the Age of Big Data argue that data-rich superstars like Google, Apple, Alibaba and Samsung will suck the life out of many traditional companies. They allege that those who know how to exploit the informational advantages of data will flourish while the rest will die, with very serious implications for the employees of the companies that will not survive. They estimate that two-thirds of the workforce in most countries are employed by the 100 million to 200 million companies that operate today.
Again, these prophecies of doom do not take into account that “soft skills matter in data science,” as John W. Foreman, chief data scientist for MailChimp.com, wrote in an article for DataInformed. Data science cannot be fully automated. You always need the worker who has the soft skills of critical analysis, effective communication, and the ability to work in a team. According to him, the fundamental challenge of analytics is understanding what problem actually must be solved. You must learn the situation, the processes, the data, and the circumstances. You need to characterize everything around the problem as best as you can in order to understand exactly what an ideal solution is. He puts special emphasis on the ability to communicate, which definitely is not developed by learning more math and statistics but by exposure to the humanities, i.e. literature, philosophy, history, etc. In his work as a data scientist, he has arrived at the following conclusion: “In today’s business environment, it is often unacceptable to be skilled at only one thing. Data scientists are expected to be polyglots who understand math, code, and the plain-speak of business. And the only way to get good at speaking to other folks, just like the only way to get good at math, is through practice…Take any opportunity you can to speak with others about analytics formally and informally. Find ways to discuss with others in your workplace what they do, what you do, and ways you might collaborate. Speak with others at local meet-ups about what you do. Find ways to articulate analytics concepts within your particular business context.”
Even in a data-intensive economy, there will be room for professionals and other workers who have developed the following skills in a variety of educational programs, both formal and informal:
--Problem solving skills: ability to frame the right problem
--Communication skills: articulate what is possible and explain the work to be done and obtain buy-ins.
--People skills and team work; ability to collaborate with people of various types, locations and cultures.
--Ability to strike the balance between complexity and usability.
--Humility: ability to see that analytics is at the service of the business or the common good.
--Realism: ability to see the limitations and capabilities of data. The future cannot always be predicted from the past.
--Ethical: data cannot be used to violate some fundamental human rights (e.g. consider the controversy over Facebook using illegitimately private information of customers.)
--Human: data analytics should be at the service of a just and humane society.
It is clear then that even in the specialized sector of data analytics and Big Data, many of the skills required in the workplace can be developed only through an optimum combination of the hard sciences (the so-called STEM) and the humanities or the liberal arts. Our education officials, both in the public and private sectors, must constantly search for a variety of programs that can produce graduates from both the universities and the technical or vocational schools who are able to combine these hard and soft skills. That is the only way that we can guarantee that our young and growing population will be able to find remunerative work, either here or abroad, in an economy that is increasingly being “globalized, digitized and roboticized at a speed, scope and scale we have never seen before.” For comments, my email address is email@example.com.