By Naureen Aziz, Associate Director, Data & AI, Accenture; IKNS Alum and Course Associate; Ex-World Bank & Kathy Krumpe, Associate Director, Talent & Organization, Accenture
AI is creating exponential opportunities. The term “value gap” signifies the ongoing struggle to fully tap into that potential. This gap occurs because we often fail to recognize the need for a complete reevaluation of our approach to deep collaboration across the organization.
Bridging this value gap requires more than just technical skills. It necessitates a profound shift in our ways of working as roles and responsibilities evolve within the workforce and beyond. Rather than fearing AI, fostering collaboration with AI becomes the key.
Leveraging insights from diverse projects, IKNS alum Naureen Aziz and change transformation expert Kathy Krumpe distill the prevalent obstacles hindering successful data and AI transformations and provide approaches to address them. Here's a sneak peek at their discoveries.
Three, two, one … blast off!
The excitement of a countdown is thrilling, but what is behind those three numbers is even more fascinating. Think about years of engineering, research, innovation, and scientific breakthroughs in space exploration.
Space exploration has had wild successes and catastrophic failures. Those failures have prompted NASA to examine its culture closely and move to a more transparent organization that encourages sharing failure, values diversity of perspective, and embraces continual change and evolution. At the heart of all that change is people and the ways in which people work together to solve problems and create new possibilities.
Navigating technology opportunities with AI and ongoing data transformations can feel as daunting as launching a rocket.
Through our work in launching multiple transformative data initiatives and new AI platforms, we have found three critical success factors to ensure real impact for the business:
- Managing data effectively through a data governance framework.
- Cultural transformation and organizational excellence through cross-functional collaboration and change management.
- Leveraging analytics for insights and performance.
Keep in mind: Organizations that unlock the growth combination of data, technology, and people stand to gain 11% in revenue-per-employee; but this drops to only 4% if they unlock data and tech alone. (The CHRO as a growth executive, 2023)
Let’s start with the importance of establishing a strong governance framework. This is the rock to build upon as the organization moves to data-driven decision-making and digital transformation, including being AI-ready. However, achieving this is not easy. The challenges we see in the governance space are vast:
- Technical delivery teams and business stakeholders can fall out of alignment creating inefficiencies, confusion, and conflict among stakeholders;
- Insufficient communication can create a disconnect for frontline teams in how individual components contribute to an overarching big picture;
- Inconsistent mandates or guidance from leadership can lead to varied interpretations across teams or departments and a lack of understanding and commitment to the shared vision;
- Absence of clearly defined goals and Responsibility Assignment Matrices (RACIs) contributes to uncertainty and struggle. Insufficient resourcing is a common challenge, especially when individuals are juggling dual roles without clear understanding and accountability;
- Additional problems also stem from poor data quality management and fragmented data setup.
Data and AI transformation efforts are not just about launching a single system or platform and are certainly not a one-time initiative. Instead, think of them as a journey that requires continuous and sustained effort to build and enhance an organization's platforms, systems, and processes. It’s about simplifying and automating complex processes, adapting to change, and redefining ways of working to drive business value and create maximum impact.
Using data as a foundational asset starts with establishing clear roles and responsibilities in how data is governed, owned, and managed, understanding the business needs to set up the relevant data domains, and establishing data governance policies that drive accountability in how we manage, secure, use, and share data responsibly. Successful data transformations rely on a comprehensive governance framework that addresses these items and helps drive value for the organization.
Here is a checklist to ensure you have all the core elements of a strong governance structure:
- Compelling Vision: Do you have a vision to inspire and align leadership and teams? Co-creating this powerful North Star/Vision Statement is fundamental to get everyone on board for the same journey at speed.
- Data Governance: Are roles and responsibilities clear? Who are the decision-makers? Do you have the governance policies defined? How is data protected and shared, and how is compliance ensured?
- Data Quality: Is the data accurate and dependable? No bad ingredients! This is the time to reestablish data quality best practices and evaluate methods to make it easier to enter and maintain core data in systems of records. This includes strong clarity and education on the data models to increase trust in users.
- Data Architecture: What technology improvements and enhancements are needed to support efficiency, performance, and adaptability of data pipelines? Plan ahead when designing your conceptual models. Think about where the data will be stored and managed to be able to create and leverage reusable assets.
- Data Integration: Are you exploring diverse approaches to maximize efficiency and adaptability in your data integration efforts? Going beyond centralization, are you incorporating technologies like data fabric and data virtualization and considering strategic methodologies like data mesh to optimize data utilization and mitigate potential drawbacks?
- Data Security: Are your security models updated to support cross-functional data access? This is a moment to evaluate policies and work cross-functionally to find the right balance of security and transparency.
- Platform Scalability: Can your platforms maintain performance as users and data sets grow? Ensure platforms can scale with your business to grow beyond MVP (Minimum Viable Product) and pilot phase.
Assess your organization for gaps in these areas as you embark on a data and AI transformation. As you mature these core areas, this is an opportunity to start your culture transformation to ensure your broader stakeholders are ready for the journey.
Views and opinions expressed here are those of the authors, and do not necessarily reflect the official position of Columbia School of Professional Studies or Columbia University.
About the Program
The Columbia University M.S. in Information & Knowledge Strategy (IKNS) program provides students with foundations in information science, organizational psychology, and change management as well as practical skills in project management and executive leadership.
Students train under world-class faculty, including former and current executives from Google, IBM, NASA, and Oliver Wyman, and join a powerful, global alum network in coveted positions, including at Accenture, Alphabet, Harvard Business Publishing, Nike, Pfizer, and the World Bank.
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