Business analytics is a practitioner movement uniting several disciplines to drive value-creating decisions from data. Central disciplines include IT / computer science, statistics, data management, decision science, and scientific research methods. Descriptive, predictive, and prescriptive approaches are often used to categorize particular methodological approaches, themselves derived from the fields of business intelligence, financial forecasting and econometrics, and operations management, respectively. Diagnostics, dynamic visualization, and semantic analytics are particular supporting techniques.
Some claim that analytics and ‘big data’ have reached a hype inflection and that a denouement awaits. This notion is reflected in the Gartner ‘technology hype cycle’, a reoccurring phenomenon associated with new innovations whereby overinflated expectations crash interest, followed by a pragmatic retrenchment.
The hype cycle observes that general and marketing-driven over-exuberance inflates expectations during the introduction of a new technology. The subsequent denouement, in the form of unmet expectations leading to disappointment, over-adjusts. However, in due course, provided the disappointment does not cause complete abandonment, there is a retrenchment in which the true value of the new technology or innovation is established and operationalized. Often the retrenchment leads to subsequent waves of innovation, which duly are over-promoted in yet another cycle.
The most recent dramatic macro-example of this phenomenon was the Dot-Com boom followed by the Dot-Bomb bust. The web did not disappear, as many doomsayers predicted during the 2001 market adjustment. Rather, a retrenchment has occurred in which a refocusing on the practical value of web-based technologies has occurred. The repurposing of web technologies to serve practical, in particular, commercial, goals is now a mainstay of the developed world, so much so that the rapidity and reach of the web is largely taken for granted as general infrastructure. This has also led to secondary innovation waves: social media, mobile, and the emerging internet of things (machine-to-machine internet communication), all of which will also likely disappoint, readjust, retrench, and re-emerge as per the hype cycle pattern.
One key aspect to second-wave web innovations is that they are generating increasing amounts of data which require analytics. This has created an intense interest in data analytics, itself subject to the ‘hype cycle’ – an initial over-enthusiasm, followed by a denouement, and then a pragmatic retrenchment. The subject of this post does not dwell on the reasons for the analytics hype, nor the valid critiques seeking to dampen expectations. Rather, the intention is to raise several core emerging trends which underlie the analytics movement and, it is asserted, will be the foundation for the inevitable retrenchment. This is a complicated proposition as the ‘analytics movement’, as it has been called, is not a single innovation, but a splintering of many innovative applications and methods for deriving value from data analysis.
While speculative in nature, the intention is to raise consciousness concerning long-term trends for the sake of practitioners, particularly for planners concerned with long-term strategy. As always, feedback is welcomed, and insightful critique will lead to revisions to or additions to the proposed trends, proper credit being applied:
The following twelve trends are asserted as the basis for the evolving data analytics ‘plateau of productivity’:
While we will see the advent of increasingly powerful tools to both manage and analyze large sets of data, the ever increasing volume of data besieging organizations will create increasing demands for specialized ‘data plumbers’.