The analytics boom is just now beginning, and it will be more than a trend, but it will be a new way of life. For this reason, the last decade has seen exponential rise in applications for analytic positions in business, healthcare, and other industries (Davenport 2013; McNeill, 2013). At the same time, a new generation of analysts, Millennials, are entering the workforce as the most technologically advanced generation ever. This could give them an overly developed sense of confidence in analytics, but there is much more they will need to understand to be viable analysts of the future.
Future analysts will need analytics skills for several reasons: first, they are becoming the standard of business acumen; second, they add value and powerful tools that complement and transcend quality management; third, data visualization facilitates communication (Evans, 2015). Future analysts will need to have a high-level understanding of how analytics supports competitive strategy and effective execution across disciplines and levels (Evans, 2015). Future analysts will need to look beyond their data, intuition will still be a vital part of the decision-making process and analysts who understand how to use both will be highly valuable (Bloomberg Businessweek Research Services and SAS 2011).
The future of analytics will involve the use of automated decision-making and the challenges this presents. Virtually all strands of industry and government are already using automated decision-making extensively (Diakopoulos, 2016), and there are ethical challenges that come with the use of these automations. Despite the usefulness of automated decision-making programs and algorithms, there are significant mistakes that can lead to high expenses, discrimination, unfair denials of public services, and even outright censorship (Diakopoulos, 2016). This is a challenge future analysts will need to understand and overcome.
References
Davenport, T. H. (Ed.). (2013). Enterprise analytics: optimize performance, process, and decisions through big data. Upper Saddle River, N.J: FT Press.
Diakopoulos, N. (2016). Accountability in Algorithmic Decision Making. Communications of the ACM, 59(2), 56–62. http://doi.org/10.1145/2844110
Evans, J. R. (2015). Modern Analytics and the Future of Quality and Performance Excellence. Quality Management Journal, 22(4), 6–17.
McNeill, D. (2013). A framework for applying analytics in healthcare: what can be learned from the best practices in retail, banking, politics, and sports. Upper Saddle River, New Jersey: Pearson Education, Inc./FT Press.
The current state of business analytics: Where do we go from here? (2011). Bloomberg Businessweek Research Services and SAS.
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