Simplilearn recently had the opportunity to speak with Sankhdeep Mitra, Director of Enterprise Data Literacy & Culture, Walmart, as part of Simplilearn's Learning Leaders Forum, where enterprise leaders share insights on workforce transformation, skills development, and the future of learning.

In this episode, "Building a Data-Literate Workforce in the AI Era," we explored why data literacy, once considered a specialist skill, has become a foundational capability for every employee in an AI-driven organization.

Speakers

Together, they discussed how organizations can build data-confident workforces, connect data literacy initiatives to measurable business outcomes, and scale learning programs across thousands of employees, from technical teams to frontline associates. The conversation also highlighted the critical role of corporate upskilling, workforce training programs, and enterprise learning solutions in creating AI-ready learning cultures.

Why Data Literacy Has Become a Business-Critical Competency

We are living through the third major wave of the digital revolution. The first brought social, mobile, and cloud. The second unlocked analytics and e-commerce. The third, where we now stand, is defined by artificial intelligence, augmented reality, the Internet of Things, and the emerging promise of quantum computing. Each wave has generated more data than the last. And yet, across industries, a striking paradox persists: organizations have more data than ever before, but fewer employees who know how to use it confidently.

In 2026, 88% of enterprise leaders say basic data literacy is essential for day-to-day work, yet 60% still report a significant data skills gap in their organizations. - DataCamp State of Data & AI Literacy 2026

For Walmart's Sankhdeep Mitra, who oversees enterprise-wide data literacy and culture, this gap is not simply a training problem; it is a structural challenge that demands a systems-level response. Data variety, velocity, and volume have all accelerated simultaneously. And the shift from structured relational data toward unstructured formats, images, audio, and conversational AI outputs has added new layers of complexity to an already demanding landscape.

"The amount of data being generated right now is tremendous. And yet we have people who say they don't always have the right data. We are dealing with this dichotomy every day."

This dichotomy, abundant data, limited confidence, is exactly what drives the business case for enterprise-wide data literacy investment.

From Data to Decisions: A Four-Layer Value Framework

Building organizational buy-in for data literacy requires more than presenting a skills gap. It requires connecting learning investment directly to business outcomes. Sankhdeep uses a four-layer value realization framework to make that case.

The model starts with data as the foundation. Data generates insights. Insights drive actions. Actions produce measurable impact. Within that final layer, Sankhdeep identifies three types of business value that leaders across industries consistently care about: improving the customer and associate experience, transforming business operations to reduce costs and increase productivity, and unlocking new revenue streams through data-enabled innovation.

The practical implication is that data literacy programs must never be justified in isolation. They must be explicitly mapped to the specific business priorities that matter most to the leaders who champion them, and those priorities will differ by function, by market, and by moment.

43% of organizations now offer mature AI upskilling programs, nearly double the 25% reported in 2024 — signaling a rapid shift from awareness to structured investment. - DataCamp State of Data & AI Literacy Report 2025

The lesson for L&D and data leaders alike: start with focused use cases. Demonstrate impact. Then let the compounding effect of proven outcomes open the door to enterprise-scale programs.

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Role-Based Competency Models: Building the Right Skills for the Right People

One of the most common mistakes in enterprise data literacy programs is treating the workforce as a monolith. A frontline retail associate, a finance analyst, a data scientist, and a senior business leader all require different levels of data capability, and conflating those needs wastes resources and creates disengagement.

Mitra advocates a dual-model approach: a competency framework that identifies the specific data skills relevant to different roles (data management, governance, visualization, analytics, and communication, among others), paired with a maturity model that defines the minimum data capability expected at each level.

The foundational definition he returns to is simple but powerful: data literacy is the ability to collect data accurately, analyze it critically, present it comprehensibly, and use it ethically. Every word in that definition carries weight. Critically is the one most often undervalued, and in the era of generative AI, it may be the most important of all.

Despite 88% of leaders agreeing that data literacy is essential, only 42% provide foundational data literacy training at scale across their workforce. — DataCamp Data Literacy Skills Gap 2026

The GenAI Feedback Loop: Data Literacy and AI Literacy Are Inseparable

A common framing in the industry positions data literacy and AI literacy as two sides of the same coin. Sankhdeep offers a more precise metaphor: they are a feedback loop. Good data is the bedrock of reliable AI. And AI, in turn, accelerates the speed at which people can work with data, pulling insights that once required expert analysts within the reach of everyday business users.

The rise of generative AI has created a much larger pool of confident Excel users overnight, not by making people experts, but by lowering the barriers to entry. It has surfaced analytical patterns for people who previously lacked the confidence to engage with them. But this acceleration comes with risk.

"Data literacy is not becoming obsolete as a result of GenAI. If anything, GenAI has brought more attention to the importance of data, because trust in AI ultimately depends on it."

Critical thinking, the ability to question outputs, assess sources, identify hallucinations, and distinguish signal from noise, has become a foundational data skill for every employee, not just analysts. So has ethical awareness. As employees increasingly work within AI-assisted workflows, knowing where organizational boundaries lie regarding data use and IP protection is no longer optional.

Scaling to the Frontline: Making Data Approachable for Every Employee

The hardest mile in enterprise data literacy is often the last one: reaching frontline employees who do not see themselves as data people, whose time is customer-facing and tightly constrained, and for whom abstract concepts like data governance feel irrelevant to their daily work.

Sankhdeep's approach centers on three guiding principles: make data approachable, make it contextually relevant, and build curiosity. The sequencing matters. You cannot build curiosity in employees who feel intimidated by the topic. And contextual relevance, connecting data skills directly to how someone does their actual job, is what converts awareness into adoption.

The platforms that achieve this most effectively are participative. Monthly webinars with data practitioners and business leaders. Events that showcase how teams are using data to solve real problems. Hackathons that are open to everyone, not just coders, and that center on analytical problem-solving over technical output. These are the mechanisms that transform data literacy from a corporate initiative into a living culture.

Critically, Sankhdeep also emphasizes the importance of cross-functional enablement. Empowering a data consumer while leaving the data product teams who build their tools without context creates a broken loop. Literacy initiatives must reach both sides of the data ecosystem to generate a durable impact.

Measuring What Matters: Impact Metrics for Enterprise Data Literacy

The most persistent challenge for L&D and data literacy leaders is demonstrating return on investment in terms that leadership cares about. Sankhdeep frames this through three metric layers: input metrics (what is being delivered, courses developed, programs launched); output metrics (coverage: how many people reached, what percentage of the target audience reached); and outcome metrics (what changed as a result).

Outcome metrics are the hardest and most important. At the qualitative level, NPS surveys and learner feedback reveal whether programs are resonating. At the quantitative level, the work involves instrumenting surveys across data communities to surface the real challenges people face, sizing the productivity and decision-quality implications of those challenges, and then validating those numbers with internal finance partners to lend credibility to the business case.

The takeaway is that the measurement conversation has to start before program design, not after. Organizations that define success metrics at the outset build the foundation for advocacy, scale, and sustained investment.

Build the AI-Ready, Data-Confident Workforce with Simplilearn SkillUp+

The conversation between Simplilearn and Walmart reflects a truth that enterprise leaders across every industry are navigating: in the AI era, data literacy is not a nice-to-have. It is the connective tissue between technology investment and business performance. Organizations that build structured, role-appropriate, outcome-linked data literacy programs today will make better decisions, move faster, and compete more effectively tomorrow.

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