Introduction

Advanced Analytics, which includes Predictive Techniques, Machine Learning, and Artificial Intelligence, is leading the next wave of disruption. Using Analytics has become a strategic imperative, leading to a fundamental change in how a business is run. Businesses are capturing an increasing amount of data that can be analyzed using the massive computational power available today. Effective use of this data and analytics can lead to profitability enhancement, revenue growth, and in some cases, the creation of new business models altogether.

In this post-COVID economy, organizations are aware of the impact data and analytics can make in all facets of business. With the increasing pace of digitization, ever-changing demands of customers, and the rapid pace of market disruptions and opportunities, analytics has become a cornerstone of sustainable competitive advantage.

We understand that data and data analytics in any sector can be compounded, thereby, creating enormous knowledge that provides valuable insights into a specific field. However, there is still a significant gap between mature analytics practitioners and late entrants who are trying to catch up.

In this whitepaper, we provide insights to enable business leaders to discern how much time is required to set up and leverage analytics, to derive practical value out of data, as well as the key factors involved in scaling up in the long-term.

6 Issues Organizations Face in Data Analytics

Popular streaming services use their recommendation systems to keep you hooked. Cab hailing companies use real-time analytics to match you with suitable co-riders. While digital businesses and startups have been early adopters (and hence 'leaders') of this data revolution, it is the very large and very small organizations that are usually the ones lagging.

1 Sponsorship: No Thorough Commitment to Analytics

Clearly, it cannot be a question of technical know-how. Even the companies that have started their analytics journey long ago often have sub-par results in terms of Return On Investments (ROI). This raises the critical question — what lies at the root of this issue?

We explored the key reasons behind the low adoption of data and analytics by 'biggies' in conventional businesses like banking, manufacturing, pharma, and healthcare.

Having the right commitment and sponsorship from the top management is crucial to bring about an enterpriselevel transformation. To ensure enthusiasm for analytics across the organization, sustained investment in technology, resources, and training is needed.

A long-term data-driven strategy needs to be chalked out keeping in mind the evolving business environment and organizational goals.

2 Prioritization: Techno-Maximalist Overshadows Value-Driven Minimalism

The decision to develop analytics capabilities should not be for the sake of being technology-driven, but for becoming a value-driven organization. Stakeholders need to choose from a wide range of conflicting and intertwined business problems (for e.g. process quality vs. cost) and devise customized solutions keeping in mind the potential impact on business.

The initial success depends on one's ability to focus on mature value-delivering solutions.

3 Governance Model: Who's in Charge and Why?

Organizations need to decide on where an analytics team fits into the organization's 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 well 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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