Greg Hood has spent over 20 years as a finance executive inside Canadian and US fintechs and financial services firms. He built Sky Site Analytics to help PE firms, sell-side M&A advisors, and high-growth companies fix the data layer before buyers find it first. We cover the dirty data discount, what actually breaks during exit, and why the back office is always the last to get the budget it needs.

If you have ever sat across from a buy-side team watching trust drain out of a room because three reports show three different revenue numbers, this conversation is for you. Greg and I have worked in the same trenches long enough to know that the data problem is almost never a technology problem. It is a process problem wearing a technology disguise.

Timestamps

Chapters:

1. 00:04 - Challenges in Product Management
2. 03:37 - The Evolution of Data in Finance
3. 09:12 - The Importance of Data Quality in Finance
4. 23:16 - The Importance of Accurate Financial Data Reporting
5. 29:23 - The Impact of AI on Business Practices
6. 36:33 - The Value of Data in Business
7. 43:49 - The Valuation of Data as an Intangible Asset

Guest

Greg Hood is a CPA and CMA who held one of the first Chief Data Officer roles earned by a CPA. He founded Sky Site Analytics, a Toronto-based consultancy that works with PE firms and high-growth companies on finance data infrastructure and exit readiness. His team won Most Innovative Finance Department while at Q Trade, an online brokerage, where they cut a two-and-a-half-day close process down to under two hours.

Companies Mentioned

1. Sky Site Analytics
2. Q Trade
3. Kunai
4. Paramount Commerce
5. Campfire (AI-first ERP)
6. QuickBooks
7. NetSuite
8. Snowflake
9. Databricks
10. Anthropic (Claude)

Websites Mentioned

1. Sky Site Analytics
2. Greg Hood on LinkedIn

Key Takeaways

1. Inconsistent revenue numbers across reports can cost millions in exit valuation, not because the business is bad but because trust is gone.
2. The data layer is routinely skipped during due diligence, and that gap is getting more expensive as data rooms grow from 30 documents to over 300.
3. First-party data can be a monetizable asset, but quality and uniqueness determine value. If your data looks like everyone else's, it is not worth what you think.