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AI Readiness Starts With Trusted Data Ft. Dave Shuman, Chief Data Officer At Precisely
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Overview
Dave brings a non-linear career journey to the conversation. He began in broadcasting, where he learned how to cut through noise and deliver signals that connected with audiences. He later moved into e-commerce during the dot-com era, working with behavioral data before real-time analytics became a formal category. From there, he moved into demand signal analytics, supply chain data, big data, Hadoop, IoT, connected industries, smart cities, and enterprise data leadership.
Across each chapter, one lesson remained consistent: data only matters when it helps people make better decisions faster.
Why AI Readiness Starts With The Boring Stuff
Dave is direct about what organizations must do before AI can deliver value. They need to start with the boring stuff: data catalogs, semantic layers, governance, lineage, and quality. These foundational elements may not be as exciting as AI demos or new platforms, but they determine whether AI can operate responsibly and effectively. Before an AI agent can answer questions, recommend actions, or automate workflows, the organization must know what data exists, where it lives, how it is defined, who owns it, and whether it can be trusted.
The OODA Loop For Enterprise AI
Dave uses the OODA loop, observe, orient, decide, act, as a framework for enterprise data and AI strategy. In the observe stage, organizations need visibility into their data landscape. This includes catalogs, lineage, ownership, and quality. Most organizations believe they have this visibility, but many do not.
The Gap Between AI Ambition And AI Readiness
One of the biggest disconnects Dave sees is confidence in the AI model and blindness to the data feeding it. Leaders may see a generative AI demo that is fluent, fast, and impressive, then assume the hard part is done. It is not. The hard part is making sure the model is working with trusted, consistent, and context-rich data. Traditional software usually fails visibly. It crashes, throws an error, or stops functioning. AI agents fail differently. They can provide polished, confident, and coherent answers that are still wrong.
Why Semantic Layers And Runtime Governance Matter
Dave believes AI readiness should be assessed through questions such as: Is data quality continuous? Do we have a semantic layer? Does the AI understand business context? Do we have runtime governance operating as decisions are being made?
Governance cannot only document what happened after the fact. In AI-enabled environments, governance must operate in real time. This becomes even more important with shadow AI. As non-technical users gain the ability to create AI-enabled applications and experiments, organizations face exposure when governance infrastructure has not yet caught up. Experimentation is not necessarily wrong, but leaders need to be clear-eyed about the risks.
Measuring AI ROI Through Business Outcomes
Dave breaks AI ROI into three practical categories. The first is proactive risk avoidance. If AI can identify compliance gaps, data quality issues, or operational risks before they become incidents, that avoided exposure has real value.
The second is labor-hour savings through automation. This should not be described vaguely as “efficiency.” It should be tied to specific workflows, specific time savings, and specific cost reduction.
Real-Time Data Is Not The Same As Reliable Data
The conversation also explores real-time data and zero ETL. Dave explains that the biggest trap is confusing speed with quality. Zero ETL promises faster access, lower latency, and simplified data movement. That promise is real, but it also shifts responsibility. Traditional ETL made transformation, cleansing, normalization, and enrichment more visible. In zero ETL models, those responsibilities do not disappear. They move elsewhere.
Advice For Future Data Leaders
Dave’s advice for professionals moving into leadership is direct.
First, own the outcome, not the output. Building something that works is not the same as building something that changes how the business operates.
Second, learn to translate, not simplify. Technical leaders should not dumb down concepts. They should translate them into business language that executives understand. Saying “we are building a semantic layer” may not land with a CFO. Saying “we are making sure every AI answer uses the same definition of revenue” does.
Third, stay technically curious, but do not make technical depth your entire identity. Data leaders must speak both technical and business languages fluently.
Key Takeaways
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AI readiness starts with trusted data, not the model.
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Data catalogs and semantic layers are foundational to enterprise AI.
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The OODA loop can help organizations structure AI and data strategy.
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AI agents fail differently from traditional software because they can be confidently wrong.
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Continuous data quality is more important than periodic checks.
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Runtime governance must operate while AI-enabled decisions are being made.
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Shadow AI creates exposure when experimentation moves faster than governance.
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AI ROI should be measured through risk avoidance, labor savings, and revenue impact.
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Boards and CEOs care about business outcomes, not plumbing metrics.
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Real-time data is valuable only when it is fit for purpose.
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Zero ETL shifts data quality responsibility rather than removing it.
About Dave Shuman
Dave Shuman is the Chief Data Officer at Precisely, where he focuses on trusted data, data quality, governance, AI readiness, real-time analytics, and enterprise data strategy. His career spans broadcasting, e-commerce, demand signal analytics, big data, IoT, connected industries, smart cities, and data leadership.
Mon, Jul 20, 2026
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