In the coming years, increasingly sophisticated big data analytics techniques will enable advancements in artificial intelligence (AI) and automation technologies. However, at least in the short term, AI and big data analytics (BDA) will not completely replace the need for humans in business or finance. At Diplo’s Data Diplomacy Roundtable in April this year, Dr Kars Aznavour argued that big data analytics still requires a human element in two distinct areas before and after analysis: coming up with the question and interpreting the results. This requires smart decision-making and creativity from the researcher, and for now computers cannot match the creative capability of humans. Interpreting the question also requires a context derived from on-the-ground observations and qualitative awareness, both of which require humans.
This perspective was echoed in an interview I conducted with Mr Karl Wellner, CEO of the Papamarkou Wellner Asset Management, Inc. From his experience, algorithms and computer programs have changed the financial industry, but there will always be a need for humans to ‘pull the trigger’. For now, only humans can ask the right questions and examine the right information to make qualified decisions. Likewise, asset-management is often a very client-focused business, and human connection remains extremely relevant. According to Wellner, trust is needed to recommend certain investment solutions, and people are becoming frustrated with having to interact with machines all the time. His clients are increasingly looking for boutique investment firms that can give a more personalised, human approach. This backlash against impersonality may limit the potential for a fully-automated financial industry.
However, BDA and AI technology will continue to rapidly develop in the coming years, and the effects will be monumental. The emergence of the Internet of Things (IoT), in which billions of physical devices such as houses or cars will become connected to the Internet, provides huge potential for BDA. Likewise, as Mr Tony Baer, big data researcher at Ovum, points out, data will increasingly be processed in the Cloud: in 2017, 35-40% of new big data workloads will be stored in the Cloud, and this will pass 50% by 2019. These developments will exponentially increase the amount of data produced, as well as democratise who has access to this information.
Machine learning and automation will create innovative alternatives to countless human-based tasks, and the labour market will experience enormous upheaval in the coming years as the so-called ‘Digital Revolution’ picks up speed. There is huge debate about whether robotics and AI technology will create more job creation or destruction. The pessimistic side states that not only will the amount of available jobs decrease, but the quality of these jobs will as well. Blue-collar jobs are not the only jobs at risk: for perhaps the first time in human history, white collar jobs such as those in finance are also threatened, as pointed out by Prof. Richard Freeman, Harvard University, at the Global Dialogue on Decent jobs for all. On the other side, optimists often state that although the net impact on the amount of jobs is unknown, the quality of jobs left will increase. Speaking at the same panel Prof. Fu Xiaolan, Oxford University, argued that there will be greater demand for high-skilled jobs using IT knowledge, critical thinking, and creativity, and new service sectors will be created to manage and develop the AI industry.
Even more groundbreaking, however, is the paradigm shift in how humanity understands economics. Already, conceptions of the traditional producer-consumer model are being supplemented or even replaced by new business models such as those of the largest Internet companies, Google and Facebook. Instead of exchanging money for Google’s search engine service, users exchange their personal data, which Google then sells to advertisers. In An Introduction to Internet Governance, Dr Jovan Kurbalija describes user data as ‘the core economic resource’ of Internet companies. Similarly, Mr Steve Lohr, journalist, argues that this represents a fundamental shift in the way civilization conducts commerce. The guiding metric of business management of the past century, finance, is being replaced by data: In his book Data-ism: Inside the Big Data Revolution, Lohr argues that “‘financial capitalism’ could be replaced by ‘data capitalism’ in which “the center of gravity in business decision-making will swing toward data”’.
A fully automated, computer-driven world will transform traditional ideas about how to store and exchange value, and understanding big data will be essential. The future remains ambiguous, but by appreciating the changes on the horizon, we can hope to stay ahead of the curve.