This article is the second in a series of six, where we discuss common obstacles faced by enterprises in analytics adoption and the way forward.
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.
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.
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.
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.
Alignment with Business Goals
The current analytical structure of most organizations is haphazard, where data is studied in small pockets (on regional, business function levels, etc.), without an overall strategy and roadmap. While such an approach was just fine until the last decade due to IT constraints, the advent of cloud solutions has now made analytics scalable and deployable across the organization. Companies now need to align analytics initiatives with their long-term business goals in order to derive value from analytics efficiently, cost-effectively, and continuously, thus providing the benefits of synergy and scale. That being said, analytics solutions need to be customized for every company, for as what has worked in the past for others might not necessarily work for your business in the same shape.
Expectation vs. Reality
While analytics may seem like a technological transformation, it is rather, a tech-enabled transformation of business processes and strategies, where immediate results might not be imminent. Hence, it becomes important to set realistic expectations and timelines with a clear roadmap. A good yardstick could be to compare the current and desired state of capabilities with leading industry practices and benchmarks, hence putting you in a better position to plan investments and expected returns.
To Each, Their Own.
The success of any data-driven plan lies in the organization's ability to identify business problems, formulate those as analytics problems, and choose wisely from available solutions that are spread across digitization, advanced analytics, data science, automation, and reporting. Often, there is a tendency to overlook simple yet effective solutions. Simple solutions are necessary as stepping-stones for developing advanced analytic capabilities. To judiciously manage ROI, short term benefits - of available resources and data should be traded off with the long term advantages of - aligning resources and data with clear goals.
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