AI Productive Use: Tackling Data Integrity Issues By Kumar Gaurav Gupta

During my latest trip to the US, where I met with data custodians and customers, I gained some critical insights into the current state of AI in organizations. The reality is that AI has moved beyond the initial hype and is now an integral part of business strategies. However, Chief Information Officers (CIOs) and Chief Data Officers (CDOs) are grappling with moving beyond initial AI use cases to more productive and impactful applications.

· Data Integrity, The Foundation of AI: A recurring theme in these discussions was the challenge of data integrity. Organizations are increasingly initiating projects aimed at harmonizing data to make it consistent and reliable. Without clean, well-integrated data, even the most sophisticated AI models cannot deliver meaningful insights. Therefore, ensuring data integrity has become a top priority for those looking to leverage AI effectively.

· The Emergence of Governance Teams: As organizations recognize the critical importance of data integrity, the role of governance teams and data directors is becoming more prominent. These teams are evolving, with key performance indicators (KPIs) that now extend beyond inter-departmental communication to include the development and implementation of comprehensive data-driven strategies.

· The Quest for an Ideal Data Strategy: Despite these advancements, there is still significant confusion around what constitutes an ideal data strategy and how to establish effective governance. This uncertainty underscores the need for clear, actionable frameworks that organizations can follow to manage their data assets.

· Unlocking Value from Data: One of the first steps many organizations take is to address data duplicates. It is estimated that resolving issues related to duplicate data can unlock 10-35% of the total value derived from data initiatives. This highlights just how crucial data quality and integrity are to the overall success of AI projects.

· Insights from Industry Trends: From my discussions, I learned several key points:

• The evolution of AI is driving a scramble among CDOs to ensure data integrity and harmonization.
• According to McKinsey, approximately 83% of AI initiatives focus on product and customer data domains, indicating where most actions and initiatives will be concentrated.

· Conclusion:

The road to leveraging AI productively is paved with challenges related to data integrity and governance. As organizations strive to harness the power of AI, the need for robust data strategies and governance frameworks becomes ever more critical. By focusing on data quality, establishing clear governance roles, aligning data strategies with business goals, and staying informed about industry trends, organizations can unlock the full potential of their AI initiatives.

Author: Kumar Gaurav (CEO- Verdantis)

References:

1. McKinsey & Company. "The State of AI in 2022—and a Half Decade in Review." Available at: McKinsey

2. McKinsey & Company. "Why data culture matters." Available at: McKinsey