Netflix uses its recommendation systems to keep you hooked. Uber uses real-time analytics to match you with fitting co-riders. Apart from these, one can point out many other applications of data analytics. While digital businesses and startups have been early adopters (read, leaders) of the big data revolution, it's the large organizations that are lagging.

Clearly, it can't be a question of technical know-how. Even the companies that has started analytics journey, often have sub-par results in terms of RoI. This raises the critical question - what lies at the root of this issue?

In this series of articles, we explore key reasons behind the low adoption of data and analytics by "Biggies" in conventional businesses like consumer goods, banking, manufacturing, pharma, and healthcare. Listed below are a few of them.

Sponsorship

The Trickle-Down Effect.

Having the right commitment and sponsorship from top management is crucial to bring about an enterprise-level transformation. To ensure enthusiasm and buy-in for analytics across the organization, sustained investment in tech, resources, and training is needed. Long term data-driven strategy needs to be chalked out, keeping in mind the evolving business environment and organizational goals.

Prioritization

First Things First!

The decision to develop analytics capabilities should not be for the sake of being AI-driven, but for becoming a value-driven organization. Stakeholders need to choose from a wide range of conflicting and inter-twined business problems (e.g., process quality vs. cost) and devise customized solutions keeping in mind the potential impact on business. The initial success, in fact, depends on one's ability to punch through the hype and focus on mature value delivering solutions.

Governance Model

The Power-Responsibility Dilemma.

Organizations need to decide upon when an analytics team fits into the organization structure. Will it be led by a business unit, or would an external partner own it? Subsequently, team structure, cost, and resource allocation need to be thought through. Ownership and coordination guidelines for cross-functional initiatives need to be clearly defined for successful ideation, implementation, and adoption of analytics practices. Hiring for leadership roles to oversee analytics (Chief Data/ Information Officer) can help establish analytics as the backbone of organizational think tanks.

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