This year is a time of transition for many data leaders—at the intersection of Generative AI, economic shifts, and political change. Data Leader Divya Bokaria and Elizabeth Press (D3M Labs) explore how the world is evolving, while emphasizing that data leaders remain stewards of business value.🧩
Data leaders navigate complex, evolving value chains and translate insights into strategies, objectives, and processes. Technologies and workflows may change, but the core mission remains: turning data and complex technologies into measurable business outcomes, whether in formal leadership roles or beyond.
Data Leadership in Transition
đź•’ 0:44 | What makes a good data leader?
Strong data leaders are defined by the ability translate complexity into business outcomes. We are stewards of value across the value chain, not technologists.
đź•’ 2:30 | How does GenAI change the paradigm?
GenAI accelerates access to data and insight, but it doesn’t replace strategy. The What hasn’t changed. We still need intentional leadership and clear ownership of value. Who can work with data and how value is created has changed.
🕒 7:15 | How has GenAI changed how we create business value – or not?
Agents are evolving how value is extracted from data—but AI is not a shortcut. Business value still comes from understanding the problem first, then deciding whether the solution is better process, better technology, or both.
🕒 10:21 | Data has a superpower—if governed well
Data leaders are the stewards of business value. The value chain is changing: tooling, workflows, and workforce models are shifting, but foundational elements—data quality, governance, and model understanding—are not going out of fashion.
đź•’ 10:50 | Learning from hype cycles
Every major technology wave brings big wins and visible failures. The opportunity now is to learn faster—knowing when to build, buy, or outsource, and when to let others take the first risks.
đź•’ 12:26 | Advice for juniors entering the field
Data is moving into the functions. Anyone can work with data—but not everyone can create value with it. Juniors should focus on:
- Understanding real business problems
- Learning data engineering and data strategy
- Building fluency in AI tools without losing critical thinking
Key takeaway:
CEOs and data leaders must treat technological risk deliberately—FOMO and reactive tactics are not a strategy. Understanding where AI creates value versus risk is now a core leadership skill. Data leadership isn’t about chasing tools; it’s about guiding the organization from data to lasting value.
Veterans of data leadership translate complex technologies and processes into strategies, objectives, actions, and sustainable competitive advantage.
Watch the conversation about navigating data leadership in transition:
