From Data Chaos to Business Clarity
How do we turn data into faster, more confident decisions at scale?
At the Summer 2026 VISIONS Data Strategy & Analytics Forum, data and technology leaders came together to answer this critical question.
In this fireside chat, Jeff Schodowski, VP of Transformation Services at Datavail, was joined by a senior data and AI leader from Hertz to share what data transformation actually looks like inside a complex, global enterprise.
This isn’t theory or vendor messaging. It’s a practical look at how an organization with highly fragmented systems, inconsistent metrics, and real financial pressure is working to build a data foundation that supports AI and business outcomes.
“Most organizations aren’t limited by AI. They’re limited by the state of their data.”
Hertz shared how challenges show up in practice:
- Fragmented systems across finance, operations, and customer data
- Conflicting KPIs across teams
- Manual work required before decisions can be made
And more importantly, they shared what it takes to move forward. “AI doesn’t create clarity. It reveals reality.”
What You’ll Learn
- How organizations like Hertz move from fragmented, low-trust data to actionable insights
- A practical roadmap to align data quality, governance, and business outcomes
- Why data alignment, not tooling, is the key to scaling AI
- The most common failure points in data transformation initiatives
- How to measure success based on business impact, not technical activity
Frequently Asked Questions
What does it mean to move from data chaos to clarity?
Moving from data chaos to clarity means shifting from fragmented, inconsistent data across systems to a unified, trusted view of the business. This includes standardizing definitions, improving data quality, and ensuring teams use the same metrics. When data is aligned, organizations reduce time spent reconciling information and make faster, more confident decisions.
Why do AI initiatives struggle to deliver business value?
AI initiatives often struggle because the underlying data is inconsistent, incomplete, or poorly governed. When data cannot be trusted, models may produce unreliable outputs, and adoption slows. Organizations that succeed with AI focus first on data quality, governance, and alignment, ensuring that AI use cases connect directly to business outcomes.
How can organizations improve data governance without slowing teams down?
Effective data governance focuses on clarity and ownership rather than complexity. Organizations improve governance by defining who owns key data elements, standardizing definitions, and embedding governance into everyday workflows. When implemented correctly, governance reduces friction by eliminating confusion and improving confidence in the data.
What metrics should organizations use to measure data and AI success?
Organizations should focus on business impact metrics rather than technical metrics alone. These include faster decision cycle times, a higher percentage of decisions made using trusted data, reduced manual reconciliation, and measurable revenue or cost improvements. These indicators show whether data is actually improving how the business operates.