Modernizing Data Strategies for an AI-Driven Future: Insights from Industry Leaders

In today’s rapidly evolving business landscape, leveraging data and artificial intelligence (AI) has become crucial for organizations looking to enhance customer experiences and drive innovation. At the recent ETCIO Data Strategy Summit 2024 in Bangalore, industry leaders from various sectors shared their insights on modernizing data strategies to stay ahead in the AI-driven era.

Balancing innovation with customer experience is key, as highlighted by Syed Atif Umar, Chief Data & Analytics Officer at Nykaa. By incorporating data, cloud, and AI into their strategy, Nykaa aims to drive innovation without compromising customer experience. Understanding the customer through data is essential for enhancing their experience and addressing their needs effectively.

Dr. Soumen Ray, Head of Data Science at Hindustan Coca-Cola Beverages, emphasized the importance of a culture of data literacy within organizations. Data completeness and a data-driven decision-making process are crucial for organizational success, especially in legacy systems.

Narendra Saini, Chief Data & Digital Officer at Lupin Pharmaceuticals, addressed the challenge of data quality and governance. Enriching data with historical context and patterns can provide valuable insights for business stakeholders, emphasizing the importance of accurate data sets for developing meaningful use cases.

In the banking sector, Vinod G, Head of Data Science & Analytics at South Indian Bank, highlighted the impact of data mismanagement on project success. Effective data management, focusing on core use cases and integrating relevant data points, is essential for deriving actionable solutions.

Chandan Vijay, Global CDO at ABB Energy Industries, emphasized the importance of embracing open architecture and selecting cloud providers based on scalability and cost-effectiveness. Building a data-driven team at a startup, as shared by Sandeep Varma, Head of AI at PhysicsWallah, requires a robust data policy and a result-oriented approach to research and development.

Shalvi Chitkara, COO of Data and AI at Genpact, identified key barriers to AI adoption, including the lack of data governance policies and integrated data systems. Overcoming these barriers is essential for organizations to achieve a return on investment and avoid biases in data sourcing.

Bhanu Jamwal, Head of Pre-Sales and Solutions Engineering-APAC at PingCap, emphasized the importance of modernizing databases for scalability, especially for modern AI applications. Using vector databases can support advanced AI technologies and enable organizations to analyze and store data effectively.

In conclusion, modernizing data strategies for an AI-ready future requires organizations to address challenges related to data quality, management, and technology adoption. By fostering a culture of data literacy, embracing new technologies, and prioritizing data governance, organizations can effectively leverage data and AI to drive innovation and maintain a competitive edge in the market.