Private Equity Data Guy

Holli Moeini spent 35 years as a CFO scaling companies through acquisitions before leaving corporate to help founders protect their value when going up against PE buyers. Her book, Find the Missing Millions in M&A, breaks down the five moments in every deal where money moves toward you or away from you. In this episode, she maps out each one and explains what sellers can do before they ever get to the table.

The conversation gets into the financial story, working capital, diligence, earn outs, and integration. Each one is a place where the unprepared seller loses. The ones who walk away with the most are the ones who treat their data and accounting as front office functions, not back office overhead.

[00:00| You cannot go it alone

[04:55| Five crime scenes in every deal

[07:15| Cleaner data, better multiple

[11:41| Working capital costs millions

[20:57| Earn out clarity control cadence

[25:29| Integration starts before close

[29:02| Data as a front office function

[41:49| Run it like you will sell

Guest Information

Holli Moeini is a CPA and M&A advisor with over 35 years in finance. She spent her career as a CFO scaling companies from $30M to $200M through acquisitions. She now works with founders and sellers preparing for PE transactions.

Companies Mentioned

IBM

Capital One

Barnes and Noble

Amazon

Websites Mentioned

https://hollimoeini.com/

Tim Schulte leads value creation at Council Capital, a healthcare-focused PE firm in Nashville. He spent years at Vista Equity Partners before building something different at Counsel. In this conversation, we get into why the playbook model breaks down across a diverse portfolio, what a toolkit approach actually looks like in practice, and what AI is doing to the work of value creation right now.

We get specific on data infrastructure, change management, and why the people question determines whether any of this works. Tim's view on AI is one I share: the tools are getting better fast, the value still sits in implementation and adoption. We cover the state of PE returns, what the industry has to do to justify its position going forward, and how Council Capital is building real operational capability at the lower middle market level.

[00:01:53] Tim's path into private equity

[00:06:01] What Vista Equity got right

[00:10:08] Toolkits vs playbooks explained

[00:14:06] Data infrastructure across diverse portfolios

[00:19:44] AI and the toolkit build process

[00:26:14] Recommending AI across portfolio companies

[00:32:47] PE history and what comes next

[00:39:37] Where the industry is heading

Key Takeaways

The playbook model breaks down when portfolio companies are diverse. Toolkits that operators choose to use work better than mandated processes.

Data strategy has to serve the operating team first. If the PE firm pulls data that operators don't use, the problem is alignment, not data.

AI saves time on framework creation. The value is still in getting people to change how they work.

Companies Mentioned

• Council Capital
• Vista Equity Partners
• Ramp
• Anthropic
• OpenAI
• Brookdale Senior Living
• Cigna
• Kroger
• University of Chicago

Websites Mentioned

• https://councilcapital.com/

Guest Information

Please provide details about the guest who will be featured in the episode.

Guest's Full Name

Tim Schulte

Company / Organization

Council Capital

Website or Main Page

Empty

Guest Social Media Links

https://councilcapital.com/

Ryan Krook has spent his career at the intersection of data and operations, working inside companies like Uber, Shopify, and McKinsey before founding Pareto Apps. In this episode, we get into what it actually looks like to walk into a mid-market PE portfolio company that has never taken data seriously, and what to do about it. Ryan brings a perspective I don't hear often: he has sat on the LP side, worked as an outside consultant, and now operates hands-on inside the companies fixing the problems that matter most at exit.

What stood out to me most was Ryan's take on starting small. Not small as in unambitious, but small as in disciplined. Get one reliable metric. Build from there. That approach, rooted in the Pareto principle, is how you create real operational leverage without pulling people away from the work that actually drives the business. We also got into the risks that come with vibe-coded apps, AI without data governance, and the version control nightmare that happens when everyone builds their own tool.

Timestamps

[00:02:09] - Ryan's path into data

[00:07:49] - Working inside PE at McKinsey

[00:15:53] - Mid-market companies and data gaps

[00:21:52] - Data governance and why it matters

[00:25:09] - Where to start with data investment

[00:30:51] - Iterative builds and fast delivery

[00:38:00] - Vibe coding risks in enterprise

[00:42:04] - AI capabilities, a 1 to 10 take

Guest Information

Ryan Krook is the founder of Pareto Apps, a firm that builds bespoke data and AI solutions for PE-backed portfolio companies and early-stage startups. He previously held roles at Ontario Teachers' Pension Plan, McKinsey, Uber, and Shopify. Ryan is based in Canada and focuses on getting companies to a place where their data is reliable enough to actually use.

Companies Mentioned

• Pareto Apps
• Ontario Teachers' Pension Plan
• McKinsey
• Uber
• Shopify
• Bowell
• Snowflake
• Bolt

Websites Mentioned

• paretoaps.co
• LinkedIn

Key Takeaways

• Start with one reliable metric before building anything larger
• Data governance is what makes AI outputs trustworthy
• PE-backed companies move faster because everyone is aligned around the same exit timeline
• Vibe-coded apps carry the same version control risks as unmanaged spreadsheets
• The barrier to standing up solid data infrastructure has dropped significantly in the last few years

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.

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

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.

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)

1. Sky Site Analytics
2. Greg Hood on LinkedIn

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.

Shota Ishii joined me on The PE Data Guy to talk about what happens when PE-backed manufacturers cannot answer a basic question: which products are actually making money? He has spent two decades building systems that give companies a clear picture of where their cash goes, and we got into why that gap exists and what it takes to close it.

We covered working capital, data architecture, and what mid-market companies need to do right now to get their data in order before AI can do anything useful for them. Shota shared an example of a $400 million metal company that found $80 million in working capital improvements once they had the right transaction-level visibility.

Chapters:

1. 00:00 - Understanding Capital Efficiency
2. 06:42 - The Journey to Becoming a Robo CFO
3. 20:55 - Amazonifying Legacy Industries: The Need for Real-Time Data
4. 31:51 - The Importance of Data Strategy in Mid-Market Companies
5. 35:18 - The Future of AI in Mid-Market Companies

Shota Ishii is the founder of Proximo Tech, where he works with PE-backed manufacturers on capital efficiency and working capital. He studied applied physics and AI and has a background in quantitative finance, hedge funds, and corporate innovation. He is based in Japan and can be found on LinkedIn.

1. Proximo Tech
2. Moody's
3. Blackstone Credit
4. Amazon
5. Snowflake
6. Databricks
7. AWS
8. Google Cloud
9. Salesforce
10. OpenAI

1. Proximo Tech
2. LinkedIn
3. Snowflake
4. Databricks
5. AWS
6. Google Cloud

1. Clean, granular data at the transaction level is the foundation for improving working capital.
2. The cost of not building a data platform grows over time as competitors who do build one move faster.
3. Data is a company asset. Without structure and governance around it, AI cannot make use of it.
4. Mid-market companies often underestimate how affordable modern data tools actually are.

Shawn Olds spent two decades building AI companies and advising PE firms on what actually produces returns from the technology. He went from the 82nd Airborne to West Point computer science to co-founding Boodle Box, and along the way he has worked with over 200 companies on AI adoption. The number that should make every operating partner stop: over 80% of AI programs in portfolio companies fail. Not because the technology breaks. Because of decisions made before a single tool is ever deployed.

Shawn and I covered why context engineering has replaced prompt engineering, why CEO backing is the one variable that separates companies getting real ROI from those just gaining productivity, and how PE firms can use data scientists during due diligence to surface hidden EBITDA before they close a deal. He also walked through real examples from the security industry that show how unstructured data already sitting inside a company can be turned into millions in annual savings without purchasing a single new tool.

Chapters:

1. 00:07 - The Role of AI in Prompt Creation
2. 02:04 - The Journey into AI Consulting
3. 11:08 - Empowering Innovation in AI Adoption
4. 24:14 - Harnessing AI in Business: Practical Applications
5. 29:39 - The Role of AI in Enhancing Business Processes
6. 37:41 - Starting with AI: Understanding the Journey

Guest Information

Shawn Olds is an AI strategist and co-founder of Boodle Box, a platform that brings multiple AI models into one collaborative workspace. He studied computer science at West Point and served in the U.S. Army's 82nd Airborne before spending two decades building technology companies across the United States, the Middle East, and Africa. He now advises PE firms and portfolio company CEOs on practical AI adoption that produces measurable business results.

Companies Mentioned

1. Boodle Box
2. McKinsey
3. Bain
4. BCG
5. PwC
6. MIT
7. 7-Eleven
8. Hire Aligned
9. Zero Prostate Cancer Foundation

Websites Mentioned

1. NotebookLM
2. ChatGPT
3. Claude
4. Gemini
5. Perplexity
6. LinkedIn

Lee McCabe joined me to talk about why private equity value creation keeps running into the same problem. The problem is messy systems, unclear metrics, and leaders making decisions without clean reporting. Lee shared how that shows up inside portfolio companies, and why the operating partner model often depends on influence, not authority.

We also got into what it takes to fix the foundation. Lee laid out where digitization pays off fastest across consumer services and B2B, plus what breaks attribution in longer sales cycles. We closed on PE firms building real brands, the role of content, and what “media plus investing” can look like in a space that usually avoids having a point of view.

Chapters:

1. 00:00 - The Challenge of Digitizing Businesses
2. 02:38 - The Opportunity for Digital Transformation in Private Equity
3. 10:04 - The Opportunities in B2B and Consumer Services
4. 19:36 - The Changing Landscape of Private Equity
5. 28:07 - The Changing Landscape of Private Equity and Media
6. 34:29 - The Evolving Landscape of Private Equity Marketing
7. 37:30 - The Need for a New Model in Private Equity Conferences

1. eBay: Example of a highly instrumented digital environment.
2. Expedia: Reference point from Lee’s travel background.
3. Facebook: Example of a scaled platform where measurement is built-in.
4. Alibaba: Example of global expansion and operating at scale.
5. Nike: Used to frame the idea of companies built around media and narrative.
6. Google: Example of a company that could offer real product thinking on stage.
7. KKR: Example of a well-known large PE brand.
8. Carlyle: Example of a well-known large PE brand.
9. Apollo: Example of a well-known large PE brand.
10. Blackstone: Example of a well-known large PE brand.

1. equity.com
2. partners.com

Lee McCabe runs Claymore Partners and advises private equity firms on digital value creation. Lee publishes the newsletter Not Very Private Equity and shares opinions publicly, with a focus on what operating partners and portfolio leaders see day to day.

I sat down with Paul Evangelista, Chief Data Officer at the United States Military Academy at West Point, to talk about what happens when decisions carry real weight. Paul spent 30 years in the Army, from engineer officer to operations research analyst, and now leads data strategy where leadership is forged under pressure. We talked about why decision literacy matters more than data literacy, how mission command principles translate to private equity portfolio companies, and why trust is the foundation of high-performing organizations.

Behind every value creation plan is a data problem. Paul breaks down how PE firms can build decision systems that work under stress, why fuzzy strategy is more dangerous than messy data, and what the private sector can learn from military command structures. If you care about building organizations that make better decisions faster, this conversation is for you.

Chapters:

1. 00:10 - The Importance of Real-Time Decision Making
2. 01:07 - The Role of Data in Military Decision Making
3. 16:25 - Understanding Mission Command in Military and Business
4. 28:21 - The Impact of Strategy on Business Resilience
5. 36:22 - Transitioning from Military to Private Sector

Companies mentioned in this episode:

1. United States Military Academy
2. Microsoft
3. Azure
4. Databricks
5. Capital One

Guest Information

Paul Evangelista is the Chief Data Officer at the United States Military Academy at West Point, where he leads the Office of Data and Analytics. He spent 30 years as an Army officer, including combat deployments to Iraq and Afghanistan, and holds expertise in operations research, systems engineering, and decision science. Paul is transitioning from military service this spring to bring his experience in high-stakes decision-making to private sector leadership.

I've spent 20 years watching brilliant people build companies on gut instinct and prayer when it comes to hiring. After losing a million dollars on a single bad hire who tanked an entire engineering team for six months, I wanted to understand if there was actually a way to measure the thing everyone claims is unmeasurable: culture fit.

Jacob Crockett built Higher Aligned after nearly getting fired at one company for the exact same behaviors that made him a star at the next. That personal disaster led him to ask a question most people assume is impossible: can you actually quantify whether someone will thrive in your environment before they walk through the door? We talked about what happens when you stop guessing and start measuring the human side of business decisions. Jacob walked me through how his AI platform scores alignment before the hire, the invisible costs that never show up on any P&L, and why the recruitment industry has devolved into an automated mess where both sides are gaming broken systems. If you've ever hired someone who looked perfect and crashed spectacularly, this conversation will explain exactly why that happened and what you can do differently.

Chapters:

1. 00:09 - Navigating Workplace Challenges
2. 01:34 - Understanding Culture in Organizations: A Data Perspective
3. 21:21 - Understanding Workplace Culture and Individual Fit
4. 28:06 - The Impact of Culture on Business Success
5. 45:12 - The Evolution of Work: From Loyalty to Portfolio Careers

Companies Mentioned

Higher Aligned

UnitedHealth Group

LinkedIn

Tesla

Indeed

Websites Mentioned

aligned.com

Guest Information

Jacob Crockett is the founder of Higher Aligned, a company that uses AI to measure cultural alignment and team fit in hiring. He has a background in data science and has worked at UnitedHealth Group and several other organizations before starting Higher Aligned.

I sat down with Scott Golder, Senior Director of Data Science at Home Depot, to talk about what actually works when you're building data teams. Scott has spent 20+ years fixing what others couldn't, from Capital One to running algorithms behind one of the top five e-commerce platforms in the world. He breaks down why deeper academic backgrounds don't always make better data scientists, how to make AI trustworthy at scale, and what happens when you fall in love with your methodology instead of the problem you're supposed to solve.

This conversation gets into the messy reality of deploying machine learning in the real world. Scott shares how Home Depot uses recommendations differently than selling sweatpants, why human accountability can't be replaced by 10,000 AI coworkers, and which AI tools he actually uses when his kids go to bed. If you're building data products or trying to figure out where AI fits in your business, this one's for you.

Chapters:

• 00:00 - The Importance of Seasonality
• 01:43 - Connecting Data Science with Real-World Challenges
• 10:17 - Understanding Customer Empathy in Product Design
• 16:15 - Transitioning to AI and Machine Learning
• 21:41 - Navigating Accountability in AI Decision-Making
• 25:00 - AI in Everyday Life: Personal Experiences and Insights
• 33:10 - The Future of Software Engineering and AI
• 38:14 - The Importance of Data Governance in AI

Companies Mentioned

Home Depot

Capital One

IBM

Google

Duolingo

Guest Information

Scott Golder is Senior Director of Data Science at Home Depot, where his team powers the algorithms behind one of the world's top five e-commerce platforms. He previously helped scale data science at Capital One and has a background in sociology, linguistics, and computer science. Scott specializes in building data teams that blend academic depth with real-world implementation in hostile corporate environments.

Key Takeaways

Academic credentials don't predict data scientist performance. Fall in love with the problem, not your methodology. AI works best for summarization when you fence the data. Human accountability can't be replaced by software. Speed and cost of AI models dictate where they're feasible. Your data foundation must be solid before AI can help.