The Challenge
Olo was on a trajectory toward an IPO, but like many high-growth startups, the data infrastructure had been built for speed, not for the scrutiny that public markets demand. The S-1 filing process would put every data claim under a microscope — and the systems needed to support that level of accountability.
My job was to build the data product strategy and governance framework that could withstand investor due diligence while simultaneously driving new revenue through data products.
The Approach
1. Data Governance That Serves the Business
Most data governance initiatives fail because they feel like compliance exercises. I approached it differently — every governance decision was tied to a business outcome. Data lineage wasn't about checking a box; it was about being able to tell investors exactly where a revenue number came from and defend it under questioning.
2. Data Products as Revenue Drivers
Rather than treating data as a cost center supporting the core product, I built data products that restaurants actually wanted to pay for. Insights on ordering patterns, demand forecasting, and customer behavior became features that differentiated Olo's platform and generated $20M in incremental annual revenue.
3. Architecture That Scales Through an IPO
Public companies face different data requirements than private ones. I built systems that could handle the reporting cadence, the audit requirements, and the real-time metrics that investors and analysts would demand — all before the IPO, not after.
Key Insight: Stop building for your next funding round. Start building for the S-1. The difference between Series D data infrastructure and IPO-ready data infrastructure is massive — and retrofitting it under IPO pressure is the most expensive way to do it.
The Results
Olo went public on the NYSE with a $3.6B valuation. The data infrastructure held up under due diligence. The data products continued generating $20M in annual incremental revenue. And 80,000 restaurant clients had a platform they could trust with their most important decisions.
More importantly, this experience showed me how few AI and data leaders have actually been through the IPO process. The gap between "good enough for a startup" and "ready for public market scrutiny" is where most companies stumble — and it's entirely avoidable if you plan for it early enough.
What This Means for You
If you're Series C or later and an IPO is on the horizon (even a distant one), the time to build IPO-ready data infrastructure is now. Read the IPO-Ready AI Blueprint for the full framework, including the 5 Pillars of IPO-Ready AI and the timeline for when to start.
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