A recent article by NY Times reporter Steve Lohr discussed the rising importance of data as a major competitive differentiator. The article noted that regulators, policy makers and academics in Europe and the US are increasingly concerned that the vast data assets of digital giants could become a competitive barrier to startups and innovation.
Such competitive concerns have long applied to hardware and software platforms in the IT industry. IBM’s System 360 family of mainframes, announced in 1964, became the premier platform for commercial computing over the following 25 years by developing a 3rd party ecosystem of add-on hardware, software and services. In 1969, the US Department of Justice launched an antitrust suit against IBM based on its market dominance, which was eventually dropped in 1982 given the changing environment in the IT industry.
In the 1980s and 1990s, the explosive growth of personal computers was largely driven by the Wintel platform based on Microsoft’s Windows operating systems and Intel’s microprocessors, which attracted an even larger ecosystem of hardware and software developers. It also attracted the attention of the Justice Department and several states, which sued Microsoft in the 1990s, - a lawsuit that was eventually settled. More recently, the European Commission has accused Google of unfair competition based on its Android mobile operating system.
The competitive concerns surrounding these various platforms are closely linked to the concept of network effects. Scale significantly increases a platform’s value. The more 3rd party applications and services a platform offers, the more users it will attract, helping it then attract more offerings, which in turn brings in more users, which then makes the platform even more valuable… and on and on and on.
As a result, data is now raising competitive concerns especially in Europe, given data’s growing role in creating and shaping markets, led by America’s digital platform giants, - e.g., Google, Facebook, Amazon, Apple and Microsoft. In November, 2016, the OECD held a meeting on Big Data: Bringing Competition Policy to the Digital Era, which noted that while the use of big data has the potential to generate substantial efficiency and productivity gains, “acquiring the necessary size to benefit from economies of scale and scope and network effects related to Big Data may potentially lead to monopoly positions, further enhanced through mergers of smaller, new providers of services that do not at first glance appear to be in the same market.”
In his NY Times article, Lohr cautions that standard antitrust arguments may not be so easy to make in this new era of data competition. “Using more and more data to improve a service for users and more accurately target ads for merchants is a clear benefit, for example. And higher prices for consumers are not present with free internet services.”
The use of data for competitive advantage isn’t new. Business analytics predates the big data era. Since the early days of IT, companies have been using their transactional data to improve logistics, inventory management, sales analysis and fraud detection. In addition, they’ve been leveraging their operational data in the management of the overall company as well as for business and financial planning.
The rise of big data in the 2000s enabled companies to discover hidden insights in the much larger amounts of data they now had access to. Beyond business analytics, new data science methods could now be used to extract actionable knowledge from all that data, that is, knowledge to help make better decisions and predictions.
In addition, data science tools and algorithms have made it possible to address complex problems by working with, linking together and analyzing multiple data sets that were previously locked away within disparate organizational silos, - be they different lines of business within a company, different companies in an industry or different institutions across the economy. This can help financial institutions, for example, to better assess their risks and potentially extend loans to individuals and businesses that would not have otherwise qualified. It could enable health care practitioners to better identify individuals who are most at risk to develop diabetes and start taking precautionary actions.
The ability to work across data sets and silos can also help provide early clues to hard-to-predict, high-impact events, so they can be tracked over time to assess their validity. When experts investigate catastrophic events, be they airline crashes, financial crises, or terrorist attacks, they often find that we failed to anticipate them even when the needed information was present because the data was spread across different organizations and was never properly brought together.
Beyond explanations and predictions, data is now being applied to machine learning, giving computers the ability to learn by ingesting and analyzing huge amounts of data instead of being explicitly programmed. This has now brought artificial intelligence to a tipping point of market acceptance.
AI has been around since the early days of IT, - having gone through ups and downs over the past several decades. But the necessary ingredients seem be finally coming together: lots and lots of data, with the volume of data pouring in expected to double every three years or so; advanced algorithms like deep learning that extract insights and learn from all that data; and drastically lowered technology costs for collecting, storing and analyzing these oceans of information.
As author and radio host Kurt Andersen noted in a 2015 Vanity Fair article, “Artificial intelligence is suddenly everywhere.” This is evident in the articles we see daily in the technology and business press, as well as the growing number of AI startups and acquisitions. Data-driven AI is being increasingly applied to activities requiring intelligence and cognitive capabilities that not long ago were viewed as the exclusive domain of humans. AI-based tools are enhancing our own cognitive powers, helping us process vast amounts of information and make ever more complex decisions.
In 2011, the World Economic Forum published a report on the emergence of data as a new asset class touching all aspects of society. The report succinctly concluded that “personal data will be the new oil – a valuable resource of the 21st century. ” A follow-on WEF report published a year later added that “the growing quantity and quality of personal data creates enormous value for the global economy. It can help transform the lives of individuals, fuel innovation and growth, and help solve many of society’s challenges… personal data represents an emerging asset class, potentially every bit as valuable as other assets such as traded goods, gold or oil.” This is now coming to pass.