Analytics as a specialized field has been in the making for some time. Various experts define an Analytics professional in different terms, without a coherent approach towards what it is expected to do. Thus, a nomenclature of a Data Scientist is emerging, with their associated skill-sets being defined. Here, the quality of data science work can be judged only by the credentials of the person executing the task, and not with any objective standards, or dispassionate rigor. The Data Science Professional Profiles document seeks to extend ESCO (European Skills/Competences, qualifications and Occupations) framework.
I consider Analytics as Data-Driven Decision (D3) making in the business world, which is the empirical application of Decision Theory. The science in Decision Sciences comes from the “body of knowledge and related analytical techniques of different degrees of formality designed to help a decision-maker choose among a set of alternatives in light of their possible consequences.” In this definition of Decision Theory, we notice that it is designed to help decision-makers choose from a set of alternatives, i.e., to make an informed choice. So, the decision is deferred to the people who are supposed to make that decision.
As per INSEAD, “the area of Decision Sciences includes risk management, decision making under uncertainty, statistics and forecasting, operations research, negotiation, and auction analysis, and behavioral decision theory.” Interestingly, even at my own Alma Mater, Indian Institute of Management Bangalore (IIMB), my area of Quantitative Methods and Information Systems (QMIS) in which I did my Fellowship, has been renamed as Decision Sciences and Information Systems (DSIS)! By the way, my first Analytics job was in the Decision Sciences group in a corporate setting.
The Decision Sciences Journal has been in publication since 1970, well before anyone would have heard or used the term ‘Analytics’. This Journal “seeks research papers which address contemporary business problems primarily focused on operations, supply chain and information systems and simultaneously provide novel managerial and/or theoretical insights.”
In the Analytics domain, we have multiple maturity models measuring the capabilities of organizations across various dimensions. Most of these Analytics Capability Maturity Models (ACMMs) are propagated by the vendors of Analytics products and services. Though there are other Analytics Maturity Models (AMMs), which are offered either by non-profit organizations, like INFORMS or is postulated by researchers and practitioners. Then, there is the International Institute for Analytics, which provides Analytics research and advisory services. They have their own DELTA model for assessing Analytics maturity of organizations, led by Thomas H. Davenport.
In all these AMMs, there is an underlying assumption that an organization’s Analytics model building capabilities are commensurate with its level of maturity as per the assessment. It is implicit in these AMMs that, if we provide for the elements identified by them, we would have good Analytics model building capabilities. This may be true for some of the organizations, but may not hold for others. To elaborate, the most common statistical inference anyone would draw is about the correlation between two sets of data, which could be quite misleading.
-By Dr. Vikas Mehra
VIT-AP School of Business, VIT-AP University