Private Equity Data Guy

The episode delivers substantive insights on data infrastructure problems in mid-market companies, particularly the $2.8M valuation loss example and dirty data discount findings. However, significant portions consist of career backstory, AI tangents, and conversational padding that dilute density. Core insights about multi-database reconciliation failures, process-before-technology approach, and data monetization limitations are valuable but interrupted by meandering discussions.

“Great success story, going from three and a half to seven to 14 to 21.6. But what's happening is during the exit process, they needed a product profitability report. Well, the problem is like most firms, they hadn't actually grown the back office. Their product set in three databases. When they pulled that together, it's a 21.15 million revenue. When they went into their CRM, they pulled $21.3 million of sales.”

“We have something we call the dirty data discount. It's usually worth about 10% of the valuation. We did a survey about 120, 122 different advisors and kind of basically said, if we gave you this data, we gave you this data, what would your valuation be? And you know, we got anywhere between a 1% discount and a 22% discount.”

The core argument - that data infrastructure is a constraint in PE-backed companies and that process precedes technology - is sound but not novel. The dirty data discount quantification (10%) is useful but derived from a single survey. Most frameworks are standard industry thinking: fix processes before buying tools, data quality matters for exits. The data monetization section and AI integration discussion add some fresh angles, but largely recycle conventional wisdom about data assets and model risk.

“You can drop a netsuite or you can drop Snowflake into a broken process and all you get is more expensive broken process.”

“Behind every value creation plan, there's a data problem nobody wants to talk about. Fragmented systems, metrics nobody trusts. And decisions made on gut feel dropped, dressed up as analysis.”

Greg Hood is highly credible: 20+ years as finance executive in fintech/financial services, CPA, CMA, founded SkySight Analytics, held CDO title. He brings genuine operational depth and has actually built data warehouses at scale (Qtrade, others). His consulting work across 15-20 portfolio companies adds real-world credibility. However, he is now primarily a consultant/vendor rather than an active operator, which slightly limits caliber versus an active CFO or CEO managing live transformations.

“Greg Hood is a CPA and CMA who spent over 20 years as a finance executive across Canadian and US fintechs and financial services, including roles at Kunai, Paramount Commerce and Qtrade, an online brokerage where his team won most innovative finance department.”

“As a company we've been through about 15 to 20 different scaling ups so far. Right. But we bring in the expertise that your head of finance just may not know or your head of data may not know.”

The episode anchors arguments in concrete numbers and named examples: $2.8M valuation loss case (21.15M vs 21.3M vs 21.6M), 10% dirty data discount from 122 advisors, Qtrade's 100M company with 90% month-end close by 9am on day one, $800K pharma dataset example, commission reporting reduced from 2.5 days to under 2 hours. The QuickBooks-to-NetSuite mistaken recommendation (Claude error: $90K spend) provides a specific failure case. Some discussions lack specifics (AI model drift percentages mentioned but not deeply quantified), but overall evidence density is strong.

“There's a half million dollar discount, but it's not really half a million dollars because it's a 5.8x on multiple. So you're talking about $2.8 million of lost revenue of lost valuation because you didn't have the correct reports in place and you couldn't prove your 21.6 million of revenue.”

“We took a two and a half day process and turned it down to under two hours where our competitors were still copying and pasting spreadsheets to do commission reporting. We had a fully vetted, double, double checked two and a half hour process and that in the last hour and a half it was just a manual intervention of just a human actually take a look at everything, make sure that the output was right.”

Host Graham Crawford asks decent setup questions and shows domain familiarity, but rarely pushes back or challenges Greg's claims directly. Follow-ups are mostly confirmatory ('yeah, no, absolutely'). The AI debate section meanders without sharp interrogation of the Claude NetSuite error or its systemic implications. Host pivots to personal anecdotes (Commodore 64, soccer simulations, location data) rather than extracting deeper expertise. No instances of productive disagreement or probing into contradictions (e.g., if data monetization ROI barely breaks even, why pursue it?). Conversational flow is friendly but lacks edge.

“Yeah, I love it. And actually that metric you mentioned, if you're looking for hidden indicators of how good a company's data is, like how long it takes them to close month, close quarter, close year from a financial perspective, is usually a pretty good barometer of what you're going to find in the other areas of the company.”

“So I thought it was a 17 legal entity. So they was recommending spending 80, $90,000 on NetSuite when the problem wasn't QuickBooks. The problem was all the information going into QuickBooks, not QuickBooks itself. The $225 per month subscription to NetSuite, to QuickBooks was all they needed, not a $90,000. But if you listen to Claude, he would have said, hey, you need to spend $90,000 on NetSuite.”