How Data Intelligence and ESG are redefining Business Today

Bibek Bhattacharya, Assistant Professor, Indian Institute of Management, Ahmedabad in an interaction with Higher Education Review shared his views on the role that Corporate Legitimacy play in shaping CSR, ESG, and Compliance Strategies today, how data-driven decision-making capabilities are redefining competitive advantage across industries and more.

Prof. Bibek Bhattacharya is a faculty member in the Strategy area at Indian Institute of Management Ahmedabad. He holds a PhD in Strategy from Indian Institute of Management Bangalore and specializes in corporate social responsibility, corporate political activity, and nonmarket strategies. His teaching focuses on strategic management, stakeholder strategy, nonmarket strategy, and data-driven decision-making.

What Role Does Corporate Legitimacy Play in Shaping CSR, ESG, and Compliance Strategies Today?

Corporate legitimacy today plays a much more active and strategic role in shaping how firms approach CSR, ESG, and compliance. It is not just about meeting expectations, but about maintaining credibility, especially in moments of scrutiny.

One interesting pattern we see in India is how firms draw on their history of social engagement when their legitimacy is questioned. When companies face allegations around governance lapses, environmental impact, or labor practices, they often highlight their long-standing CSR initiatives, community investments, or nation-building contributions. This is not accidental. It reflects an understanding that legitimacy is cumulative and can be reinforced by demonstrating a consistent record of socially responsible behavior.

CSR and ESG, in this sense, become part of a broader narrative that firms actively manage. For example, large Indian business groups frequently emphasize their philanthropic legacy, rural development initiatives, or sustainability commitments in public communication during periods of reputational challenge. This helps them signal that any issue is an exception rather than a reflection of their core values.

There is also a growing recognition that stakeholders, including investors, regulators, and the public, are looking for coherence between what firms say and what they do. As a result, companies are investing more in integrated reporting, sustainability disclosures, and visible social initiatives that reinforce their credibility over time.

So legitimacy today is not just about compliance. It shapes how firms build reputational capital and how they respond when that capital is tested.

How Are Data-Driven Decision-Making Capabilities Redefining Competitive Advantage Across Industries?

Data-driven decision-making is fundamentally reshaping how firms build and sustain competitive advantage. What is interesting is that firms are not all using data in the same way. We are seeing at least four distinct but overlapping approaches.

First, many firms are using data to inform strategy formulation, particularly decisions around where to compete and how to compete. For example, in retail and e-commerce, firms analyze customer browsing and purchase data to identify which product categories or geographies to enter. Similarly, in banking and financial services, firms use transaction and behavioral data to identify underserved segments. Data also informs how to compete by shaping differentiators such as personalization, pricing strategies, or service levels.

Second, in some cases, data itself becomes the core strategic resource, which aligns closely with the resource-based view of the firm. Proprietary datasets, algorithms, and analytics capabilities can exhibit VRIN characteristics, meaning they are valuable, rare, difficult to imitate, and not easily substitutable. This is most visible in digital platforms and fintech, where firms build advantage not just by using data, but by owning and continuously enriching unique datasets that competitors struggle to replicate.

Third, firms are increasingly using data to resolve specific strategic issues, including nonmarket challenges. For example, companies in sectors like energy or manufacturing use environmental and operational data to address regulatory compliance and sustainability concerns. A more pointed example is Dream11, which, when facing allegations that its platform resembled gambling, relied on data and statistical analysis to demonstrate that outcomes were predominantly skill-based rather than chance-driven. Such use of data helps firms engage with regulators and shape the narrative around their business models.

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Finally, data plays a critical role in strategy implementation and evaluation. Firms across industries use dashboards, experimentation, and real-time analytics to track performance and refine execution. For instance, consumer goods companies monitor campaign effectiveness, while logistics firms continuously optimize operations.

Overall, data is not just improving decisions at the margin. It is reshaping how strategies are chosen, executed, and adapted, making analytical capabilities a key source of competitive advantage.

Why are businesses moving beyond operational analytics toward strategic data intelligence?

Businesses are moving beyond operational analytics toward strategic data intelligence because the real leverage of data lies not just in improving efficiency, but in shaping the most fundamental choices a firm makes.

Strategy sits at the top of all other functions. It defines the broadest set of choices, including where to compete, how to compete, and what trade-offs to make. At the frontier, firms are not just optimizing processes. They are making big, consequential bets under uncertainty. These involve trade-offs across markets, technologies, business models, and stakeholder expectations. The ultimate test of these choices is whether they lead to sustained competitive advantage or superior performance over time.

This is where data plays a different role. Increasingly, firms are treating strategy as a set of hypotheses. For example, a firm may hypothesize that a particular segment will be more profitable, or that a certain differentiator will drive long-term loyalty. Data then becomes a way to test these hypotheses in a systematic and ongoing manner. It allows firms to move beyond intuition and evaluate whether their strategic choices are actually delivering the intended financial and non-financial outcomes.

Perhaps the biggest shift is in the frequency of this evaluation. With greater digitalization and the availability of real-time data, firms are revisiting their strategies much more often. Instead of periodic reviews, strategy is becoming more dynamic, with continuous feedback loops that signal when a hypothesis needs to be refined or even abandoned.

How Should MBA Programs Evolve to Prepare Future Leaders for Data, Governance, and Nonmarket Challenges?

MBA programs need to evolve quite fundamentally to prepare leaders for a world where data, governance, and nonmarket forces are deeply intertwined with business decisions.

On the data side, the biggest shift required is toward a much more hands-on, decision-oriented approach. It is not enough for students to understand analytics conceptually. They need to be comfortable using data in real decision-making contexts. This also calls for more applied learning through problem-solving, live projects, and independent work, where students engage with messy, unstructured problems rather than clean datasets. In doing so, they develop a more holistic capability set, including the ability to translate business problems into analytical questions, draw on theory or prior knowledge to frame hypotheses, and then collect, analyze, and interpret data meaningfully. Equally important is understanding the limitations of data and the challenges of being overly data-driven.

At the same time, the traditional distinction between “leaders” and “analysts” is blurring. Senior managers are increasingly expected to engage directly with data, tools, and models. This calls for a more techno-functional mindset, where students are not just managing teams but are also capable of getting into the details when needed.

In my own teaching, I try to bring this out through experiential exercises. For instance, I ask students to work with a small MSME that has little or no digital or data infrastructure and develop a roadmap for building data-driven decision-making capabilities. This forces them to think end-to-end, from problem framing to data collection to implementation, while also dealing with real-world constraints.

On the governance and nonmarket side, there is a need to build much greater awareness across both core and elective courses. Students should be encouraged to systematically examine how nonmarket forces and actors can create strengths, weaknesses, opportunities, and threats for firms. Classroom discussions should actively incorporate these dimensions so that students learn to factor them into strategic thinking. Overall, the salience of governance and nonmarket considerations needs to be emphasized much more strongly across the MBA curriculum, rather than being treated as a niche or specialized topic.