EP 550: How a Transformative Data Strategy Powers AI Success
🎯 Summary
Podcast Episode Summary: EP 550: How a Transformative Data Strategy Powers AI Success
This episode of the Everyday AI Show, featuring Asheesh Verma, US Chief Data and Analytics Officer at Deloitte, centers on the critical, often overlooked, role of a robust data strategy in achieving successful, scalable AI and agentic transformation. The core argument is that as generative AI lowers the barrier to entry for using models, data quality, diversity, and governance have become the primary differentiators and competitive moats.
1. Focus Area
The discussion focused heavily on Data Strategy for AI/ML, specifically addressing the shift required by the advent of Generative AI and Agentic AI. Key themes included data procurement beyond internal silos, data marketplace implementation, data labeling/annotation for probabilistic models, and the challenges of governing data used by autonomous agents.
2. Key Technical Insights
- Data Diversity is Non-Negotiable: Successful AI at scale requires moving beyond internal data to incorporate third-party, business partner, and synthetic data. The use case dictates the necessary data mix.
- Data Marketplace as a Prerequisite: Enterprises must implement a centralized “data marketplace” (like Deloitte’s internal system with 520+ feeds) to manage consumption criteria, policy engines, and compute environments, eliminating human middleware for data provisioning.
- Annotation for Agent Behavior: For agentic AI, the attribution and labeling of data are paramount. Incorrectly labeled data directly leads to agents exhibiting unexpected or non-deterministic behavior (hallucinations are framed as a feature of probabilistic models requiring precise data hygiene).
3. Business/Investment Angle
- Data as the New Moat: With state-of-the-art LLMs accessible to everyone, proprietary, well-governed, and diverse data assets are now the key competitive advantage, superseding technological infrastructure alone.
- Ambition Must Match Data Strategy: A common failure point is when an organization’s AI ambition is not commensurate with its underlying data strategy and readiness.
- Unstructured Data Monetization: Organizations must treat unstructured data (like resumes or documents) as a primary source for AI training by contextualizing and indexing it, mirroring how search engines index the web.
4. Notable Companies/People
- Asheesh Verma (Deloitte): The featured expert, providing insights from his role as CDAO, focusing on large-scale enterprise data governance and AI enablement.
- Deloitte: Used as a case study for proactively embracing AI transformation to evolve its service portfolio (“menu”) and remain relevant in industries undergoing AI-driven value chain disruption (e.g., pharma, healthcare).
- Google (Sponsor Mention): Briefly mentioned in relation to their Gemini model and video generation capabilities.
5. Future Implications
The industry is moving rapidly toward agentic orchestration, where multiple agents interact and execute complex tasks. This necessitates the development of open, standardized protocols for agent-to-agent communication and robust registration/guardrail systems, as seamless, large-scale multi-agent orchestration remains an unsolved challenge. The focus will shift from simply using data to ensuring the data governs autonomous decision-making.
6. Target Audience
This episode is highly valuable for Chief Data Officers (CDOs), Chief Information Officers (CIOs), AI/ML Strategy Leaders, and senior business leaders involved in digital transformation, particularly those struggling to move AI projects from experimentation to enterprise scale.
🏢 Companies Mentioned
💬 Key Insights
"...not when the use case arrives, not when your business partner asks, but in anticipation of the fact that it is what I call horizon two, not even horizon three."
"For example, if your ambition is to be agentic, or if your ambition is to be agentic plus whatever the permutation or choice of tool that you use or consumption pattern, right, means you want to use the data to consume it in a certain way, whether it's for reasoning, whether it's for LLM, whether it's for conform SQL, whatever it may be, IML, you pretty much have to build your data strategy anticipating that that is the capabilities that you need to have..."
"What I would say is walk with the end in mind, right? If you understand the outcome that you need to intend to do with your data, that is your starting point. Everything else that you do should be in the service of that."
"Anybody that is claiming that they've done it at scale and it works seamlessly [multi-agent orchestration], we don't buy it, because we do all my experimentation and we realize how high we can do it. And we just get it started on multi-agent orchestration, even before multi-agent, we're just getting started on single agents doing the intended outcome before we talk about agent-to-agent and handing off to other agents, right? That is still something that we need to conquer."
"What you have to get right in essence is that the attribution of the data set that feeds that agent needs to be annotated correctly for you to be able to get that agent to behave within the guardrails."
"What really happened is Google parsed the entire worldwide web, parked it in the content store, indexed that data set, and gave you contextuality through query to be able to figure out rank and relevance for you to get to the answer. We did the same thing with the resume database, right? We contextualized it, we indexed it, we gave it a query engine."