Podcast | Advantage Solutions CDO Jo O'Hazo on Trust, Micro Wins and AI Readiness
Jo O'Hazo, Chief Data Officer at Advantage Solutions, joins Nathan Turajski of Informatica to talk about building enterprise data and AI that actually works. Her stance is clear: start with people, prove value in small steps, and earn trust through outcomes you can repeat. She also flags what AI is exposing across many firms-gaps in governance, accuracy, and architecture that can no longer be ignored.
Start with people, not platforms
Technology doesn't fix misaligned incentives, unclear ownership, or weak communication. O'Hazo centers her programs on teams, roles, and habits before tooling. Define decision rights, clarify who owns data products, and set expectations for how business and data teams work together. The stack follows the structure-not the other way around.
Micro wins compound into transformation
Big plans stall. Micro wins stack. Ship a trusted metric, clean one critical dataset, retire a redundant feed, or automate a painful manual step. Each win reduces friction, builds credibility, and frees capacity for the next. Momentum becomes your operating system.
Trust is earned, not announced
Trust grows when leaders listen, align on real business outcomes, and deliver consistently. O'Hazo emphasizes listening tours, shared KPIs with business partners, and crisp scope that solves one problem end-to-end. Report progress openly, publish definitions, and show before-and-after results. Repeatability is what turns trust from a feeling into a system.
AI is revealing the gaps
AI surfaces what spreadsheets hide: poor lineage, missing controls, and inconsistent reference data. If your inputs are suspect, your answers will be too. This is forcing leaders to confront process debt-unclear ownership, duplicated pipelines, and brittle integrations. Treat these as blockers to growth, not back-office chores.
Governance, accuracy, and architecture matter more than ever
Put guardrails in place: data contracts, stewardship, and quality SLOs for the domains that drive revenue and risk. Standardize definitions, manage master data, and capture lineage so teams can trace change. Align to an opinionated reference architecture with reusable patterns, not a new snowflake for every use case.
If you need a neutral benchmark for decision-making and risk, the NIST AI Risk Management Framework is a helpful anchor point. Read it here: NIST AI RMF. For data fundamentals, many leaders reference the Data Management Body of Knowledge: DAMA-DMBOK.
What leaders can do this quarter
- Pick three micro wins that solve a visible business pain and time-box them to 6-8 weeks.
- Assign data product owners for your top five metrics or datasets and publish their charters.
- Set quality SLOs (completeness, timeliness, accuracy) for critical data domains and report them weekly.
- Agree on one reference architecture pattern for new data and AI use cases; fund reuse over reinvention.
- Stand up an AI use-case intake with risk checks, model documentation, and approval criteria.
- Create a simple decision log: what was decided, why, owner, and date-then share it.
- Run a monthly "show the work" review with business leaders: what shipped, what changed, what's next.
Metrics that keep everyone honest
- Adoption: active users, self-serve queries, and stakeholder NPS.
- Speed: time-to-first-insight and cycle time from request to release.
- Quality: data defect escape rate and mean time to restore when issues hit.
- Model health: drift, bias findings, and business outcome variance vs. plan.
- Cost: unit cost per data product or AI use case and reuse rate of shared components.
Why this matters for management
Data and AI are management systems, not IT projects. Your teams will follow what you measure and fund. Sponsor the smallest possible wins, insist on shared KPIs, and anchor decisions to clear architectural patterns. The result is trust you can scale-and outcomes the business can count on.
Listen now.
If you're building AI readiness across leadership roles, you may find these helpful: AI courses by job and popular AI certifications.
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