Private Equity Data Guy

I sat down with Cass Ziebel, who works on the operations side at ParkerGale, to talk about what private equity looks like after the deal closes. Cass shared how a Google search on the way to her first internship interview started a career in diligence and portfolio operations. She explained that accounting numbers depend on the rules a team agrees to follow, and that a company looks different once the founder steps away.

Cass described how she handles founders whose price expectations the model cannot support, and why walking away protects investors. We covered the first 100 days at a founder owned software company, where talent, change management, and gaps in finance leadership take priority. We closed with weekly reporting on a short list of metrics and the Parker Gals community that Cass relaunched with Liz Hsu.

Chapters:

• 00:12 - Botched first internship interview story

• 01:15 - Meet Cass Ziebel from ParkerGale

• 12:42 - Quality of earnings and pro forma

• 28:21 - First 100 days after founder exit

• 48:15 - Parker Gals and how to connect

Guest Information

Cass Ziebel started at ParkerGale as the first hire after working in KPMG's deal advisory practice and completing her MBA at Chicago Booth. She has spent ten years across three funds and now works with portfolio companies on financial reporting and visibility. Cass relaunched Parker Gals with Liz Hsu to support career conversations with women in private equity. Listeners can find Cass on LinkedIn at https://www.linkedin.com/in/cass-gunderson/ and learn more about her firm at https://www.parkergale.com/.

Companies Mentioned

ParkerGale

KPMG

Illinois Venture Capital Association

Chicago Booth

Capital One

KKR

Blackstone

LinkedIn

Toys R Us

Websites Mentioned

https://www.linkedin.com/in/cass-gunderson/

https://www.parkergale.com/

Brief Takeaways

Accounting numbers depend on the definitions a team agrees to use, and that agreement comes first.

Talent decisions drive the first 100 days at a founder owned company.

Every metric needs a threshold and an agreed action before the number appears on a dashboard.

Phil Murphy and I cut through the buzz and give you a practical, no-nonsense playbook for actually getting AI working in a PE-backed services business. You won’t get a vendor pitch here — we mandated adoption rather than piloted it, and that forced reality to surface fast. The mandate was simple: every functional leader pick one real, recurring workflow, baseline it, and run it with AI. I let them pick the how; I removed the option not to try. What broke wasn’t the models — it was inconsistent processes, scattered data, and tribal knowledge hiding in inboxes and spreadsheets. So you and I learn a crucial rule: one process, one data structure per function. Don’t start with a data lake; start with one authoritative place for each kind of data, permissions you can enforce, and your top five processes written down. If you’re stepping into a portfolio company next week, my hands-on advice is the same thing we used: pick a high-volume, repeatable, reviewable task (invoicing, AR reconciliation, timesheet/ex travel approvals), run it with AI, and measure before-and-after. Couple adoption with governance from day one: inventory approved tools, draw a clear line for data that never goes into models, and create a sanctioned path that records activity. Remember my quip — you don’t need to add “AI” to your company name to get value — but you do need structure to see it. I promise you, adoption plus visibility is the symbiotic engine that turns table-stakes efficiency into a real, defensible advantage.

Chapters:

• 00:11 - Where the Next Dollar of AI Value Is
• 06:26 - Mandating AI: How Synergy Transformed Back-Office Operations
• 16:53 - Turning Internal Visibility into an Enterprise AI Governance Product
• 21:33 - Defining Full Visibility: The Four Pillars of AI Governance
• 31:40 - Advice for a new PE‑backed CEO: where to start with AI adoption

Companies mentioned in this episode:

• Synergy ECP
• Valfurious Capital Partners
• CACI
• Booz Allen Hamilton
• Strata Solutions
• Anthropic
• OpenAI
• Amazon
• Amazon Web Services (AWS)
• Meta
• Google
• Capital One
• ACG National Capital

I spoke with Sal Tirabassi about the role a fractional CFO can play as a company grows and prepares for a possible sale. He described how his team helped a business consolidate reporting across two related entities, correct system errors, and clarify the performance of its manufacturing and rental operations.

Our conversation covered investor grade financial data, tracing margin changes to specific customers and products, measuring marketing channels through retention, and how AI may affect early career learning. I asked what smaller businesses gain from senior fractional CFO support and how experience across different clients shapes the advice a finance leader can give.

Chapters:

• 00:15 - The value of 1099 work: flexibility and direct access to decision makers
• 01:17 - Guest Intro — Sal Tarabassi: From Growth Investor to Fractional CFO Founder
• 12:26 - Preparing a Business for a $20M Sale
• 18:18 - Tax Savings and R&D Credits for Industrial Equipment Builders
• 24:10 - Investor-Grade Data: What Investors Expect in Diligence
• 31:29 - Inside a NASCAR Racing Team
• 35:44 - Client segments and the value offered to smaller clients
• 44:24 - Closing: Guest Farewell and Contact Information

Companies Mentioned

CFO Pro+Analytics: Sal’s firm provides fractional and interim CFO support.

MC Partners: Sal spent nearly a decade there as a partner in growth and venture investing.

IBM and Accenture: I referenced my early career at both companies and the client work I learned from there.

Capital One: I mentioned leading the company’s marketing technology stack. [1]

Websites Mentioned

CFO Pro+Analytics: https://www.cfoproanalytics.com/

Sal Tirabassi on LinkedIn: https://www.linkedin.com/in/stirabassi/

Guest Information

Guest’s Full Name: Sal Tirabassi

Company / Organization: CFO Pro+Analytics

Salvatore “Sal” Tirabassi founded CFO Pro+Analytics in 2021 after 15 years investing in growth and venture businesses, including nearly a decade as a partner at MC Partners. He leads a team of senior fractional CFOs supporting founder owned and family owned companies.

In this episode, I talk with Carson Marr, founder of Buyable, about where real money hides inside private equity portfolio companies. Carson spent years advising executives at firms like Blackstone, Vista, and Thoma Bravo on buying and building enterprise technology before he built Buyable, a company that reviews contracts and billing records and only gets paid when it finds missed revenue. His team found millions of dollars sitting in the gap between a billion dollar company's contracts and its billing system, work completed in sixty days.

Carson explains the two conditions an outcome based deal needs, a buyer who is hungry for a specific result and a project narrow enough to prove direct cause and effect. We talk through why contract and billing gaps open up during mergers, why escalator clauses get missed for years, and why executives choose Salesforce or IBM because the downside of a safe choice protects their career. Carson also shares a story about turning a research tool into a pipeline engine that created one hundred and four million dollars in new pipeline within sixty days for a large AI company.

Chapters:

• 00:09 - Turning Research into Revenue: $104M Pipeline in 60 Days
• 01:10 - Introducing Carlson Ma — From Deal Advisor to Founder of Buyable
• 09:06 - Proving outcomes before engagement — using niche case studies
• 18:13 - Case Study: Turning Research into $104M Pipeline in 60 Days
• 24:06 - The CRM Dilemma: When Implementation Isn't an Outcome-Based Play
• 31:02 - AI Disruption: Industries at Risk

Companies Mentioned

Buyable, RSM, Blackstone, Vista Equity Partners, Thoma Bravo, Salesforce, Sierra, IBM, ZoomInfo.

Websites Mentioned

Carson's personal site is carsonmarr.com. He also writes on Substack and can be found on LinkedIn. His company site is Buyable.

Guest Information

Carson Marr is the founder of Buyable, a company that reviews contracts and billing systems inside private equity portfolio companies and only gets paid when it finds missed revenue. He spent years in management consulting at RSM, where he worked with firms including Blackstone, Vista, and Thoma Bravo on enterprise technology decisions inside their portfolio companies. He writes about deal making on Substack and can be found on LinkedIn.

Here's the scariest sentence in private equity: every KPI on the value creation plan is green and enterprise value is exactly where it started. I paint that horror show because it's exactly what I've seen: dashboards full of check marks, a room of smart people, and nobody who can explain why the company isn't worth more. You get the quick wins in year one—pricing tweaks, a bit of cost takeout—and everyone celebrates. Then year two arrives, the heavy-lift initiatives—cross-sell, add-ons, new segments—take longer, definitions diverge, and the board deck looks prettier than reality. What breaks the thesis isn't bad strategy; it's broken data. I walk you through how every lever—pricing, procurement, cross-sell, M&A—relies on numbers you can trace. Most portfolio companies run on a patchwork of systems, one heroic finance person, and no agreed definitions. So the dashboard turns green because we're measuring activity, not value. The net result: enterprise value flatlines and buyers discount everything because they can't trust the baseline. If you want to find the gap, try my one-meeting test: pick the single number the plan depends on and ask the room who can trace it to source, define it, and reproduce it. If the room goes quiet, you found the problem. I also flag a free scorecard at the end to see where you stand and offer a chat to show you where the plan and the data disagree while you still have time to fix it.

Takeaways:

• You should stop trusting green KPI dashboards — they often measure activity, not the actual value increments buyers will pay for.
• I explain why value creation is the whole game now: cheap leverage and multiple expansion no longer carry deals.
• If you buy a new BI tool or hire a fancy consultant without fixing definitions, you're just decorating broken data — don't order the pizza yet.
• We learned the hard way: merging companies means merging messy datasets first, otherwise any post-acquisition claims won't survive diligence.
• You can test your portfolio quickly — pick the one number the plan depends on and ask who can trace it back to source.
• Foundations matter: build data definitions and ownership early, because they compound value each quarter and speed up provable initiatives.
• I even put a free scorecard at the bottom — use it now so we can find where your numbers and the plan disagree before it's too late.

In this episode I talk with Afrozy Ara, founder and CEO of LuminaData, an AI transformation platform for finance operations backed by Techstars and Berkeley SkyDeck. Afrozy started her career as an electrical engineer, then spent over a decade in enterprise consulting at firms including Mu Sigma, where she worked directly with Fortune 500 finance teams and built out diagnostic practices that benchmark how finance work actually happens inside large organizations. She started LuminaData a year and a half ago, beginning with a single problem inside reconciliations, and her founding thesis is that AI adoption is not the same as AI transformation.

We talk about where the money actually hides inside a finance team, from duplicate invoices and unpaid remittances to the nonstandard contracts that large customers like Amazon force onto their vendors. Afrozy explains why giving a team of ten people the same AI tool can produce ten different answers without a shared process behind them, and why she still keeps a human in the loop for every exception rather than letting an agent make deterministic decisions on its own. She also walks through the three step method her team uses inside a client: look across the whole business, pick one area and go deep, then show a small result fast to build trust before scaling the work across the order to cash process.

Chapters:

• 00:19 - The Agent: Reimagining Your Process
• 04:11 - Thesis: AI adoption is not AI transformation
• 08:15 - AI in the Enterprise — From Copilots to Capturing Value
• 20:56 - Humans in the Loop: Designing AI Agents with Human Oversight
• 25:05 - Connecting platforms to reveal business performance and KPIs
• 33:21 - Personal Writing, Unfinished Books, and AI

Companies Mentioned

LuminaData

Techstars

Berkeley SkyDeck

Mu Sigma

Capital One

Amazon

Ramp

Brex

QuickBooks

Anthropic

Websites Mentioned

linkedin.com/in/afrozy-ara-57527612

luminadata.ai

Guest Information

Afrozy Ara is the founder and CEO of LuminaData, an AI platform for finance operations backed by Techstars and Berkeley SkyDeck. She began her career as an electrical engineer before moving into enterprise consulting, spending over a decade at firms including Mu Sigma, where she partnered with Fortune 500 finance teams on data and process problems. She started LuminaData a year and a half ago, beginning with a single problem inside reconciliations, and her work today centers on her belief that AI adoption is not the same as AI transformation. You can find Afrozy on LinkedIn.

Guest's Full Name: Afrozy Ara

Company / Organization: LuminaData

Website or Main Page: Not provided

Guest Social Media Links: https://www.linkedin.com/in/afrozy-ara-57527612/

In this episode I sit down with Rich Kershaw, a technology due diligence specialist who has looked at over 250 companies for private equity and venture capital investors. We talk about why the technology itself is rarely the real problem inside a portfolio company. Rich explains that the org chart exposes decision making gaps that technical documents alone will not show you.

This conversation gets into where large language models help and where they fall short. Rich explains why he treats AI like a smart but inexperienced intern who still needs clear direction. He shares what he is building with Billy, a platform designed to spot gaps in a company's operations before they turn into bigger problems for investors.

Chapters:

• 00:42 - The Hidden Data Problem Behind Value Creation
• 02:28 - A Circuitous Route into Private Equity
• 11:20 - When Technology Is — or Isn't — the Problem
• 17:21 - Org Charts: What They Reveal About Governance and Decision‑Making
• 27:19 - AI, LLMs and Due Diligence: What They Can — and Can’t — Automate
• 37:24 - Origin of Billy: Scaling the Service with LLMs
• 49:57 - Where to find Billy and Diligency — contact, beta sign-up & next steps

Companies Mentioned

Octopus Investments, Octopus Energy, Lecky Kershaw, Diligency Consortium, Billy, Microsoft, GitHub, Monday.com, Atlassian, Salesforce, Intercom

Websites Mentioned

richkershaw.com, diligency.org, leckykershaw.com, billy.works

Guest Information

Rich Kershaw spent his career on both sides of the technology table, first as a CTO and product leader, then running technology and product due diligence for venture capital and private equity investors through Lecky Kershaw. He co-founded the Diligency Consortium, a group of due diligence specialists working across multiple disciplines, and he is now building Billy, a platform that tracks portfolio companies against established operational standards between funding rounds. Rich can be found at richkershaw.com and Billy at billy.works.

This week I sat down with Vinay Raman, the CEO of CAARMO, to talk about the gap between what leadership teams believe is happening inside their company and what is actually happening on the ground. Vinay has spent thirteen years studying that gap. He walked me through the fifteen questions his firm uses to score where a leadership team agrees and where members quietly disagree, and the map his team builds to show exactly where the disagreement lives.

We talked about what happens after a private equity firm buys a company and replaces its leadership team, why a system built for twenty three employees can break at one hundred thirty nine, and why placing AI on top of a broken process only makes the process fail faster. Vinay closed with a daily private equity news product he calls the Pulse, built to hand operating partners one sharp question before they walk into a room.

Chapters:

• 00:03 - Common Sense: Why It's So Uncommon
• 01:02 - Meet Vinay Rahman — Measuring the Gap Between Belief and Reality
• 12:29 - From Misalignment to Coherence
• 18:42 - Leadership: The Biggest Challenge in Portfolio Companies
• 25:20 - Private Equity Analogy: Why 'Copilot' Alone Won't Deliver
• 38:42 - The Future of Private Equity: Value Creation, AI, and the Shift to Effectiveness

CAARMO - Vinay's company scores leadership teams on fifteen questions and builds a map of where the team agrees and where it splits.

Bain & Company - named for research on how much of a strategy actually reaches execution.

McKinsey - named alongside other firms publishing reports on private equity value creation.

KKR - named alongside other firms publishing reports on private equity value creation.

Tesla - referenced through a story about a meeting practice from Tesla's early growth years.

caarmo.com

Vinay Raman on LinkedIn

Vinay Raman is the CEO of CAARMO, a firm that studies execution friction inside growing service businesses. He spent more than twenty years working in data analytics and artificial intelligence before building CAARMO's fifteen question survey and cohesion mapping tools. He produces a daily private equity news product called the Pulse.

I sat down with Dan Grainger, who runs the private equity practice at T4, a voice of customer research firm that ended up in private equity almost by accident. He spends his time inside letter of intent windows, calling the customers of companies my listeners are trying to buy or fix. We talked about what those customers say once nobody from the target company is on the line.

Dan walked me through real deals where customer interviews changed the outcome. One packaging company lost twenty five million dollars off its price after customers described new competitors already undercutting them. Another deal died after a flooring company's dealers reported a failed software rollout that nobody in the boardroom had flagged. We also talked about where artificial intelligence helps his research team and where it still falls short.

Timestamps

Chapters:

• 00:14 - AI's struggle to connect unstructured data
• 01:08 - Guest Introduction — Dan Granger & T4's Private Equity Practice
• 10:59 - Case Study — Hidden Competitive Threats Uncovered Through Customer Interviews
• 19:38 - Customer Diligence Red Flags
• 28:24 - AI in Customer Research — Tools, Limits, and Human Judgment
• 34:50 - Closing Remarks and Contact Info

Companies Mentioned

T4, T4 Associates, Medallia, SAP, ChatGPT

Websites Mentioned

https://www.t4associates.com/

https://www.linkedin.com/in/danielgrainger/

Guest Information

Dan Grainger leads the private equity practice at T4 Associates, a voice of customer research firm found at t4associates.com. He previously worked in customer research consulting and spent time at Medallia during its time as a private technology company before moving into private equity diligence work. He interviews target company customers during letter of intent windows to surface risks and growth opportunities before deals close.

In this episode I sat down with Regan Inkster, a strategist who has spent 25 years building strategy for Global 1000 companies and served three times as a chief architect. We talked about a pattern he found in his own research on organizational coherence: the distance between what a company says internally and what employees actually experience appears in performance data before it appears in the financial numbers. He calls this warning window about two quarters, and he built an AI model that reads language across a company to measure it.

We covered real cases, including a merger that destroyed about 75 percent of the deal's value and a widely recognized coffee company where the space between leadership and frontline teams matched swings in market value for years. Regan shared advice for private equity operating partners: close the loop between decisions and the reasons behind them, and check whether the dashboard rewards match what leadership wants the business to improve.

Timestamps

00:02:26 Coherence drifts before financial results

00:04:13 Sensing dysfunction inside an organization

00:08:00 Building the coherence measurement platform

00:16:30 CEO strategy rollout builds agreement

00:18:24 Starbucks coherence linked to market value

00:21:23 Private equity ownership raises mismatch risk

00:32:59 Many AI initiatives fail to deliver

00:41:16 Closing the loop after decisions

Companies Mentioned

IBM

Capital One

Oracle

Starbucks

Fidelity International Systems

World Pay

Netflix

Blockbuster

The New York Times

HBO

Websites Mentioned

Amazon: https://www.amazon.com

Barnes and Noble: https://www.barnesandnoble.com

LinkedIn: https://www.linkedin.com

Glassdoor: https://www.glassdoor.com

Reddit: https://www.reddit.com

Indeed: https://www.indeed.com

Guest Information

Regan Inkster spent 25 years building strategy for Global 1000 companies and served three times as a chief architect. He worked as a global go to market and value creation leader at Oracle and as a country lead for emerging tech and AI at a global systems integrator. He wrote the book Two Quarter Warning, which describes how organizational coherence drops about two quarters before financial results do.