
Challenges with Defining and Aligning (Meta)data & AI strategies
This post explores the challenges organizations face in designing data, metadata, and AI strategies based on my experiences delivering workshops at various international conferences last year. If you’re interested in addressing these challenges, I invite you to join a 🔎free Masterclass, “Aligning Data and AI Strategies,” on January 23rd at 4:00 PM CET.
For more details, visit here. Register here.
💥Challenge 1: Lack of Strategies
A significant challenge is that many organizations lack data-related strategies aligned with their business objectives. Some are driven by legislative pressures to create these strategies but often focus solely on compliance. Others heavily invest in IT tools without recognizing that IT requirements are data-driven. Additionally, some organizations believe they lack the maturity to develop robust data-related strategies.
💥Challenge 2: Diverse Strategy Types
Even when organizations develop data-related strategies, these are often too abstract to implement effectively. A frequent topic of discussion in data management circles is the distinction between data and data management strategies. While data strategies highlight the business value of data, data management strategies focus on how to manage data to achieve that value.
However, other critical aspects are often overlooked. For instance, strategies that fail to account for different data types are impractical. Metadata, a unique data type, may require its own strategic approach. Metadata management enables data lifecycle management and establishes the metadata cycle, which interacts with but often requires distinct capabilities from broader data management. AI strategies, too, are frequently treated as separate entities, though they need integration with data strategies. The rationale is straightforward: metadata management enables data management, and together, they provide the foundation for effective AI management.
💥Challenge 3: Varying Models and Frameworks
Industry authorities such as DAMA-DMBOK and Gartner offer differing frameworks for strategy development. Organizations must navigate these differences to create actionable strategies. A strategy should not only present a high-level vision but also function as a long-term, actionable plan. AI strategies face even greater complexity, with some frameworks focusing on structure and others on development steps.
💥Challenge 4: Misaligned or Incomplete Strategies
Many organizations fail to integrate data and AI strategies. However, data and metadata management are foundational for AI management, as data assets form the core components of AI systems. The degree of integration often varies across industries and regions.
💥Challenge 5: No Universal Approach
The integration of strategies depends on factors like regional regulations, organizational structure, cultural readiness, and resource availability. A one-size-fits-all solution simply does not exist.