Ram Kumar on Data Strategy as the Cornerstone of AI Success

In today’s data-driven world, organizations are in a race to harness the power of artificial intelligence (AI) and advanced analytics. However, beneath the surface of this AI revolution lies a critical foundation that often goes unnoticed — a robust data strategy. This was the central theme of a recent conversation between Ram Kumar, Chief Data Officer (CDO) at Cigna, and Vidhi Chugh, Chief AI Officer at All About Scale, during an insightful video podcast. Their discussion illuminated the fundamental role of data strategy in driving AI success and addressed several key challenges and opportunities faced by organizations in their journey to become truly data-centric.

Understanding the Data-AI Relationship

At the outset of the interview, Vidhi set the stage by emphasizing the pervasive tendency to jump straight into AI projects without first establishing a comprehensive data strategy. Ram Kumar agreed and passionately explained why this is a misguided approach. “Data is the lifeblood of any business,” he said. "It's a strategic and competitive asset if used wisely; otherwise, it becomes a liability."

Ram likened data to the blood in our bodies — it is essential for all functions, but we don’t think about it until something goes wrong. Just as a blood test can reveal underlying health issues, poor data quality can undermine even the most advanced AI initiatives. Therefore, a solid data strategy must precede any AI strategy, ensuring that the data is accurate, well-governed, and readily available to fuel AI models.

Customer Centricity and Data Strategy: Aligning Business Goals

A significant portion of the conversation revolved around the importance of customer centricity in data strategy. Ram highlighted a common misconception — that being "centric" towards multiple things, such as customers, data, or processes, means being truly focused. "In reality, when we claim to be centric towards many things, we are often centric towards nothing at all," he noted.

Ram argued that organizations must first prioritize customer centricity and then align their data strategy to track and support customer journeys effectively. "Data may feel intangible because it’s not something you can touch or see like technology," he said. "But it is the foundation for understanding and enhancing the customer experience." The challenge, however, lies in changing the mindset of both leadership and employees to recognize data as a strategic asset that drives customer value.

Data Culture: The Key to Unlocking True Value

One of the recurring themes in the discussion was the significance of building a strong data culture. According to Ram, cultivating a data-driven culture requires a top-down and bottom-up approach. While leadership must set the tone and provide direction, every person in the organization should take responsibility for data quality and management.

To illustrate this point, Ram shared a powerful example from his career. At one of his previous organizations, he tied the CEO's bonus to data quality. This move positioned the CEO as a data champion and cascaded the focus on data quality throughout the organization. Employees were incentivized with rewards for good data practices, and data quality became a key performance indicator (KPI) across all levels. As a result, the organization transformed its culture, aligning everyone around the shared goal of maintaining high data quality.

Despite these efforts, Ram acknowledged that changing people's mindsets around data remains one of the biggest challenges. He cited studies from MIT and Harvard Business Review, which indicate that while advancements in data science are progressing, the cultural alignment around data is declining. "We must recognize that data management is not just about technology; it’s a cultural shift," Ram stated.

The Role of AI in the Data Function: An Integrated Approach

Ram also touched on the critical need to integrate AI within the data and analytics function rather than treating it as a separate entity. “Data fuels AI,” he explained. “Without quality data, AI models will not deliver the expected results.” Ram argued that separating AI from the data function risks creating silos and misalignment between the two. An integrated approach ensures that data scientists and AI practitioners are aligned with data governance, data quality, and the overall data strategy.

Vidhi agreed with this perspective, pointing out that aligning AI and data functions is essential to achieve true data-driven outcomes. They both emphasized that AI should not be seen as a magical solution to data problems but rather as a tool that requires high-quality data to be effective.

Balancing Global Consistency with Local Adaptability

Operating across multiple countries presents unique challenges in maintaining a consistent data strategy. Ram emphasized the importance of balancing global consistency with local adaptability, particularly in the context of developing global data products. His principle of “design for global, implement for local” addresses this challenge by standardizing core components of data products while allowing for local customization based on regional regulations and market needs.

He stressed that while core components such as claims analytics might remain consistent, layers need to be customized to respect local requirements like data residency laws. By aiming for 70% standardization and 30% customization, organizations can achieve scalability without compromising on local adaptability.

Prioritizing Data Initiatives: A Structured Framework

To manage the complexity of data initiatives, Ram shared insights into his comprehensive prioritization process, which involves applying 21 different filters to evaluate potential projects. These filters include alignment with enterprise strategy, resource availability, risk, compliance, and potential for value creation.

The key is to start by gathering use cases without constraints, allowing each function to define its priorities. Once these use cases are identified, the organization applies its prioritization framework, which includes input from all stakeholders, to ensure alignment and buy-in across the board. This approach prevents the isolation of projects and ensures that the most valuable initiatives are prioritized.

Embedding Data Value into the P&L: A Bold Step Towards Accountability

A particularly innovative approach discussed during the interview was Ram’s decision to embed the expected value of data initiatives directly into the Profit and Loss (P&L) statement. This ensures that business leaders are accountable for delivering data-driven outcomes and makes the commitment to data quality and governance tangible.

Ram explained that this step helps drive accountability and ensures that data is not just seen as an IT function but as a strategic driver of business value. “The day when an organization recognizes data as an entry on the balance sheet, more valuable than the software or hardware that processes it, is when they truly become data-driven,” he declared.

Shifting Focus from ROI to Economic Benefits

Another critical insight from the conversation was the need to rethink how organizations evaluate the success of data initiatives. Ram argued that Return on Investment (ROI) is not the right metric for assessing data projects. “You can’t measure the value of data with a simple ROI calculation,” he said. "Data is like a gold mine — the more you dig, the more value you extract."

Instead of focusing solely on ROI, organizations should look at the economic benefits created by data, whether through cost savings, operational efficiencies, or new revenue streams. Ram emphasized that investments in data should be viewed as long-term asset-building exercises rather than short-term costs.

Becoming Truly Data-Driven

The conversation between Ram Kumar and Vidhi Chugh provided a deep dive into the complexities and challenges of building a data-driven organization. Their insights underscored the critical role of a robust data strategy as a foundation for AI success, the importance of fostering a data-centric culture, and the need for integrated approaches that align AI and data functions with overall business goals.

To become truly data-driven, organizations must treat data as a strategic asset, invest in building a strong data culture, and integrate data governance and quality into every aspect of their operations. By embedding data value into the P&L, shifting the focus from ROI to economic benefits, and balancing global consistency with local adaptability, organizations can unlock the full potential of their data and achieve sustained competitive advantage in the digital age.