In today’s rapidly evolving business landscape, data is no longer just a tool - it’s the backbone of innovation, especially in using AI for marketing. According to Gartner’s 2024 report, 75% of organizations will shift from piloting to operationalizing AI by the end of the year, but many will struggle with data quality issues as a key obstacle to realizing the full potential of AI. As businesses across sectors race to integrate AI into their operations, the readiness to harness and leverage data becomes critical. While the potential for data to transform decision-making is clear, organizations often find it challenging to cultivate a data-first mindset and infrastructure to catalyze AI innovation.

This was the focus of a recent discussion in MMA Global India’s ‘LinkedIn Café’ initiative, where industry leaders shared their insights on what it takes to become truly data-ready. Leaders contributed to a lively exchange on the cultural and technological shifts required to embed data-first thinking into the DNA of a company. They included -

1. Amit Phutane (Chief Business Officer, Neemans)
2. Vanda Ferrao (CMO, WOW Skin Science)
3. Ankit Prasad (Founder & CEO, Bobble.ai)
4. Amitabh Pande, VP- Consumer Planning & Insights and Digital, Media and Advocacy, Diageo
5. Girish Kalra, CMO, Tata AIA
6. Hitarth Saini, Head - Marketing, Freo
7. Jaydeep Deshpande, Head - Enterprise Marketing, Google Cloud
8. Pranesh Urs, VP & Head Marketing, Ather Energy
9. Sandeep Singh (COO, SingleInterface)

Below, we highlight key takeaways from the session, shedding light on how organizations can unlock the full potential of data in their AI-driven strategies.

1. Indicators of True Data Readiness: Building the Foundation for AI

When asked what defines a "data-ready" organization, Hitarth Saini, Head of Marketing at Freo, emphasized the need for structured, enriched data that evolves with business needs. He highlighted, "Measurement of business impact via data programs - such as the accuracy of predictive algorithms and how well distinctive consumer behaviors are identified - is crucial." This sentiment was echoed by Ankit Prasad, who pointed out that “a data-ready organization breathes data into every function. It’s not just about having sophisticated tech stacks but about a cultural shift where leaders are fluent in data-driven decision-making.” In other words, data readiness isn’t just about tools; it’s about embedding data into the very fabric of how an organization operates.

Meanwhile, Amit Phutane stressed the importance of data lakes and visualization tools like Tableau and PowerBI to understand historical trends and predict future outcomes. He stated, “Data modeling is essential to predict what’s coming in the next quarter or fiscal year,” highlighting the predictive power that a well-organized data infrastructure can offer.

Girish Kalra pointed out that organizations must systematically gather data from different sources and analyze it to extract meaningful insights that can drive decision-making and business growth.

2. Challenges in Adopting a Data-First Mindset

Transitioning to a data-first culture is not without its challenges. Sandeep Singh identified the shift from intuition-based decisions to data-driven strategies as one of the biggest hurdles. “The biggest challenge is getting everyone to trust the data and take that as the single source of truth,” he shared. Many organizations still rely on instinct and experience, making it difficult to transition to data-backed decisions.

Ankit Prasad added that unlearning old ways of working is critical: "Data-first means surrendering to facts, which can shake egos." He also noted that without robust systems, there’s a risk of data loss, complicating the journey further.

For Amitabh Pande, VP of Consumer Planning & Insights at Diageo, the integration of data is often hindered by the silos that exist in large organizations. "Getting the data that exists in silos into a common platform is one of the biggest hurdles," he noted. Without a unified data set, AI initiatives may falter, as the foundation for meaningful analysis is lacking.

Vanda Ferrao highlighted that one of the primary challenges organizations face is the existence of data silos, where information is stored across disparate systems, making access and integration difficult. She also highlighted the importance of data integration from different sources, maintaining quality and consistency of data for actionable insights, and having strong data governance to ensure accuracy, consistency, and compliance.

3. The Role of Leadership and Culture

Leadership and culture play an undeniable role in driving a data-first approach. Pranesh Urs, VP & Head of Marketing at Ather Energy, highlighted that leadership should not only champion the importance of data but also demonstrate its value across functions. “Leadership should demonstrate the significance of data and its sanity across functions and as far down the organization as possible,” he suggested.

Ankit Prasad took this a step further by emphasizing the importance of curiosity. "Leaders should model this by asking tough questions, challenging the status quo, and empowering teams to test, measure, and innovate," he said, highlighting that experimentation and data literacy are at the heart of a data-first culture.

Sandeep Singh agreed, noting that data-first thinking must start from the top. “If leaders consistently refer to the data as the single source of truth during business reviews, it becomes inculcated as a regular practice,” he explained.

4. Using Data to Anticipate Customer Needs and Improve CX

One of the key benefits of a data-first approach is the ability to anticipate customer needs and improve customer experience (CX). Ankit Prasad noted, “Data is the voice of the customer if you’re listening hard enough. Anticipating needs isn’t just about looking at what customers did in the past; it’s about predicting what they might need next.” At Bobble.ai, for instance, data is used to customize user experiences in real time, proving that marrying creativity with data can lead to a richer, more personalized customer experience.

Pranesh Urs further emphasized the importance of connecting the dots between past and present data to predict future needs. He advocated for automating data-driven decisions to ensure timely interventions, adding a human touch where necessary to avoid feeling like a bot-driven experience.

Meanwhile, Vanda Ferrao emphasized that ensuring data quality is crucial, as poor quality data can lead to inaccurate insights and ineffective AI models.

5. Building a Robust Data-First Ecosystem

Finally, organizations need the right capabilities to build a sustainable data-first ecosystem. According to Sandeep Singh, digitizing every data flow and ensuring it is readily accessible for analysis is critical. This requires not only strong technological platforms but also the right talent. Ankit Prasad identified three pillars for success: technology, talent, and trust. “You need scalable data infrastructure, the right people, and trust across the organization. Without trust in the data, there’s no data-first ecosystem,” he said.

Amitabh Pande echoed these sentiments, adding that data audits and a clear vision of why data is needed are essential steps in the journey toward data readiness.

Jaydeep Deshpande shed light on the role of a data-driven culture, robust infrastructure, and continuous improvement as pillars of a data-first ecosystem.

Girish Kalra emphasized the importance of having a structured system for data capture, analysis, and deriving actionable insights.

Conclusion - Data Readiness as the Catalyst for AI Success

As companies look to the future, embracing a data-first mindset will be the key to unlocking the full potential of AI. The experts who contributed to MMA Global India’s LinkedIn Café emphasized that this transformation is both cultural and technical, requiring a shift in mindset as well as robust infrastructure. In a world where AI is fast becoming the competitive differentiator, businesses must evolve their approach to data, ensuring they are ready not just for today, but for the rapidly changing landscape of tomorrow.