Business Analytics Competency: Need Internal Evangelists or Technology-based Bulwark?

Dr Arindam Banerjee is a Professor of Marketing, IIM Ahmedabad and Prof. Tanushri Banerjee is an  Associate Professor, Information Systems, School of Management, PDPU, Gandhinagar. SAGE is the proud publisher of their book “Business Analytics”. SAGE has a fast-growing list of high-quality textbooks in Business & Management.

No one denies that Analytics has been much talked about in businesses for the past decade and a half. So much so, that some Business Leaders in India have been vocal about its critical role in driving the sustenance of organizations in the future, more significantly than investing in competent human resources. The more recent manifestations of the Analytics stream such as Data Science, Artificial Intelligence, Machine Learning, and IOT are discussed in business symposiums with equal enthusiasm. Just two decades ago such enthusiasm was unimaginable.

Yet, an overwhelmingly large number of Analytic projects are done in India (some sources quote over 85%) happen to support overseas businesses and, are not connected to the development agenda of local companies. What may be the reason for this overdose of enthused discussion but, not supported by many impactful initiatives in critical business decision making processes in the Indian setting? 

It is quite likely that many Indian organizations do not have the “highbrow” capacity as yet, to absorb such technology at a fast pace. It is not so much about their (dis)ability, but more about their place in the evolutionary cycle of development. Most organizations in India, as we see them, are still in a nascent stage of development of data-driven decision-making capability. And it is visible.

Barring some primarily digitally-driven businesses, many Indian organizations own at best, a smattering of business data, largely stored in not so organized manner and therefore do not facilitate easy usage. The concept of IOT can be used to calibrate initiatives to record pertinent data and manage it – for instance, what to capture and where to place sensors to capture it effectively and store it for further use. However, the technical bulwark associated with IOT should ideally wait till organizations have their “data capture” and processing plans in place.

Second, for many business managers, developing analytics capability is more like an imposition from top management rather than a percolated “felt need” among the rank and file of the organization. Hence the typical response from them varies between “resistance” and “confusion”, perhaps due to the lack of directions on “what to do?”. Technological platforms for large scale data processing are perceived as immediate “succour” from their dilemma. But in the medium term, they are more likely to cause alienation between the user business group and the technical specialists who develop these analytics platforms, because of the lack of “common language for communication” between them.

For most critical decision making in organizations, impersonal data Analytics is at best a helpful support. This notion of complementarity that data-based analyses provide, must be impressed on decision-makers in organizations. Most traditional business managers, therefore, need the phase of unlearning and relearning of capabilities to really appreciate the benefit of analytics support to their decision-making process.    

How should then Firms develop a long term strategy for Analytics capability development? Well, firms starting from “ground zero” in building Analytics capabilities and not endowed with tons of streaming data generated as part of their business require a different intervention than technological prowess to support their transitioning onto Analytic capabilities. They may actually require a team of “Evangelists” to:

 a) mentor top management and build an appreciation in organizations for fact-based decision making,

b) build human resource capability in organizations to gather and use appropriate data (with technology as an enabler) for developing relevant business insights and inferences and,

c)  help analysts communicate in a relevant language to management, the implications of such insights developed.

As the appetite for Analytics increases and, with the relatively more mature capability and experience in organizations, Analytics tasks will become repeatable. Firms may then consider a technology-based platform for facilitating an efficient and scalable solution. They may, at that point, also require better trained specialist data scientists to resolve “difficult” data modeling problems.

This approach does not necessarily require at the outset, a specialized technology (science) based function as it is made out to be in many business forums. Instead, businesses need a cadre of “General Managers” who happen to be comfortable with the field of Analytics and Data Science and are willing to lead projects that yield meaningful insights. They would ideally champion the usage of analytics in business groups as “hands-on” initiatives rather than create the dependence on a “black box” that very few people in the organization understand. They also become the translators of technology output into business acumen for the organization.

Building effective Analytics prowess is more about such enhancements in human resource capabilities in organization. Technology-driven products have a role to expand the Analytic infrastructure but only after the organization has gained sufficient maturity to use it intelligently. In reality however, we find that in the Indian environment a surfeit of technology-based product/platform development in the Analytics domain, which is very mystifying. Examples of technically superior analytic products are mainly to support operational processes (such as, Recommender engines and ChatBots, to name a few) rather than critical strategic issues in the organization.  

Perhaps, it has to do with the dilemma of investing in tangible versus intangible investments. Technology platforms are tangible assets of organization, whereas human resource capabilities are largely invisible and their impact is hard to measure in the short run. In our hurry to showcase development let us hope we do not invest unwisely just in the tangible elements, which may not necessarily drive up  productivity of the business in the long run.     



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