Data and AI: How Business Leaders Gain a Competitive Edge

Data and AI Insights from Siddharth Rajagopal, EMBA Alum of Quantic School of Business and Technology and Author of “Data as the Fourth Pillar.”

In a world increasingly shaped by artificial intelligence, data is no longer just a technical asset, it’s a strategic imperative. That’s the central thesis behind Data as the Fourth Pillar: An Executive Guide to Scaling AI, a new book co-authored by Siddharth Rajagopal, Chief Architect at Informatica and Quantic Executive MBA alumnus. During a recent LinkedIn Live edition of Quantic Conversations, Sidd joined us to share how executives can future-proof their organizations by adopting a data-first mindset.

The New Operating Model: People, Process, Technology… and Data

For decades, business leaders have structured operations around three core pillars: people, process, and technology. But Sidd argues it’s time to add a fourth: data. Embracing data and AI as a unified force is critical for modern success. 

“We noticed that although AI is a hot topic in boardrooms, data is still being managed the same way it was 10 or 15 years ago,” he explains.

Treating data as a strategic pillar, rather than a byproduct of tech or operations, enables leaders to unlock exponential value, especially in AI adoption. It ensures data is governed, invested in, and measured with the same rigor as any other business function.

A Framework for Readiness: Quality, Compliance, Speed (QCS)

To help business leaders understand how prepared their data is for AI adoption, Siddharth and his co-author, Sujay Roy propose a simple yet powerful framework: QCS, which stands for Quality, Compliance, and Speed. This approach ensures data and AI integration is strategic and effective.

Each of these pillars represents a critical dimension of data readiness:

• Quality refers to the accuracy, completeness, and consistency of the data being used. AI systems rely heavily on clean, reliable data to produce meaningful results. Poor data quality can lead to biased outputs, failed predictions, or missed opportunities.
  
• Compliance addresses how well data management practices align with internal governance standards and external regulations (such as GDPR or HIPAA). With increasing regulatory scrutiny, this pillar ensures responsible AI deployment and protects organizations from costly risks.
  
• Speed captures the timeliness and accessibility of data. In some cases, AI models need real-time or near-real-time data to function effectively, especially in areas like fraud detection, logistics, or customer support.

What makes the QCS framework especially valuable is its flexibility across different business functions. Not every AI use case requires maximum intensity across all three dimensions. For example:

• A customer service chatbot may need highly accurate and compliant data delivered at lightning speed to respond in real-time with personalized support.
  
• Meanwhile, a supply chain optimization tool may still function effectively with slight delays in data delivery, provided the data is accurate and meets compliance standards.

By evaluating use cases through the QCS lens, organizations can identify where to prioritize investments and avoid the trap of overengineering. Rather than striving for gold-standard data across the board, leaders can intentionally tailor their data efforts to meet the specific needs of each initiative.

Ultimately, the QCS framework enables a smarter approach to data strategy, balancing supply with real business demand and aligning technical capabilities with enterprise goals.

Data and AI: A Flywheel of Value

Sidd uses the analogy of a flywheel to describe the relationship between data and AI. High-quality data fuels better AI performance, and well-trained AI, in turn, helps clean, structure, and improve data. This synergy of data and AI creates a powerful cycle of continuous improvement. 

“In a few years, AI will become a commodity,” he says. “The real differentiator will be how good your data is.”

This mutual reinforcement makes data strategy essential, not just for AI success, but for innovation and operational resilience.

Measuring Impact: Total Addressable Value (TAV)

To shift from vague data initiatives to ROI-driven outcomes, Sidd introduces the concept of TAV—Total Addressable Value. Inspired by the familiar startup metric TAM (Total Addressable Market), TAV encourages leaders to identify all the value-generating opportunities data could unlock in their organization, from operational efficiency to entirely new revenue streams. Harnessing data and AI through TAV redefines strategic potential.

Takeaway: Practical Data and AI Advice for Business Leaders

Whether you’re scaling an enterprise or launching a startup, Sidd offers this advice:

1. Map where data sits today: is it siloed or fully integrated into decision-making?
   
2. Elevate data to the strategic level by assigning leadership, budget, and clear KPIs.
   
3. Start small, but think big: find quick wins that align with long-term transformation goals.
   
4. Align AI with core business value. Don’t innovate for innovation’s sake.
   
5. Treat data like fire. It can spark innovation or burn value, depending on how it’s handled.

Whether you’re an enterprise executive or an aspiring founder, Sidd’s message is clear: embracing data as a core pillar is no longer optional; it’s essential for any organization looking to thrive in the AI era. Mastering data and AI is your key to sustained innovation.