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Data, Cash and Deal Readiness: Finance Lessons for Scaling Technology Companies
The Technology Spotlight was one of three industry spotlights at The Future Is Fractional 2026, hosted by Scrubbed on September 24, 2026. Anthony John Rogador, Accounting Advisory Services Manager at Scrubbed, spoke with Abdul Wahab Zafar, SVP of Finance at Studycast, who has led M&A integrations and financial systems consolidations at fast-scaling companies. They covered building a single source of truth, rationalizing the tech stack, forecasting usage-based revenue by cohort, managing cash with payment terms, and preparing a back office for M&A. The short version: tie customer, contract and billing data together, hold payment terms firm, and operate as if you might sell tomorrow. Fast-growing technology companies tend to add tools faster than they connect them. Finance ends up consolidating data from systems that don’t agree, forecasting revenue that won’t sit still, and hoping the back office holds up if an acquirer calls. At The Future Is Fractional 2026, a virtual conference hosted by finance and accounting advisory firm Scrubbed on September 24, 2026, Session 4 split into three industry breakouts running at the same time: real estate, nonprofit and technology. In the technology track, Scrubbed Accounting Advisory Services Manager Anthony John Rogador spoke with Abdul Wahab Zafar, SVP of Finance at Studycast, who has run M&A integrations and systems consolidations at fast-scaling companies. Key Takeaways: Accurate financials start upstream. Customer, contract and billing data need a common key tying them together. 90–95% data alignment is often enough. The last last few percent can cost more than it’s worth. Forecast usage-based revenue by cohort . In Zafar's experience, a cohort’s second year grows fastest, then growth settles to low single digits. Protect cash with payment terms. Hold customer terms firm and negotiate longer vendor terms to create a cushion. Operate as if you’ll sell tomorrow. Buyers judge revenue by how well customer, contract and billing data tie out. Don't rush to merge systems after a deal. Move acquired customers into the process that already works. Meet the Speakers Anthony John Rogador (moderator): Accounting Advisory Services Manager, Scrubbed. [LINK: LinkedIn] Abdul Wahab Zafar, MBA, CMA : Senior Vice President of Finance, Studycast, with experience leading M&A integration, financial systems consolidation and scaling finance operations at fast-growing companies . Building a Single Source of Truth upstream Finance, Zafar said, takes data from everywhere else and consolidates it. So accurate financials start 'on the head of the pipe,' with customer data. He thinks of each data set as a 'cube,' and the order matters: customer first, then contract, then billing, then vendors. Data Set The Question To Answer Customer Data Who is buying from you, and is that record correct? Contract Data What exactly did they buy? Billing Does billing reflect the same customer name and contract terms? Vendor Data Who do you pay, under which contracts, against which invoices? When those line up, he said, the financial data naturally becomes accurate and trustworthy. Standardizing data across disconnected systems Growing businesses add the tool they need and forget to connect it. Zafar’s approach: Define the North Star for your data, then break it into the steps needed to get there. Get leadership aligned. If data alignment isn’t a priority for sales or customer teams, finance can only do so much. Create a common hook. Every record in the customer, contract and billing “cubes” should share a key so anyone can trace one customer across all three. Decide whether the last 5% is worth it. Reaching 90–95% alignment is often achievable. The final 5% may require retooling working processes or systems that already work, and in his experience is often less cost-effective than working around it. Rationalizing the tech stack before adding tools New finance tools launch constantly. Zafar’s discipline before buying one Identify the actual problem you’re solving, in depth. Check whether your current systems can solve it . Many companies use only part of what a platform like NetSuite can do. Only then evaluate third-party options, with a clear picture of what you need. Keep it simple. The fix is sometimes the smallest tool on the market and don't create processes 'just to make it look like you're doing a good job.' Complexity has a staffing cost. “If it’s more complicated processes, then a lot of times you need a bigger team to manage those processes,” and most small and mid-sized finance teams run lean. Forecasting Usage-based Revenue by cohort Usage-based revenue billed on how much customers use the product, not a flat fee) shifts with business days, holidays and seasonality. As Zafar put it, 'It's not as simple as, the customer signed a $12,000 contract, my revenue is $1,000 a month.' Studycast groups customers into cohorts by the year they went live, because cohorts tend to behave consistently over time. Cohort Year Typical growth, in Zafar's experience Why Year 1 Partial year Customers go live partway through the year Year 2 Roughly 70% to 100% The first full year of revenue Years 3-4 onward Around 4–5% a year Growth declines sharply, then settles Forecasting each cohort separately is more reliable than projecting the whole customer base at once. Separating enterprise and small-business customers to handle outliers The outliers at Studycast, single customers growing 100% or more a year, were mostly enterprise customers who went live at one site, then rolled out to others, compounding revenue month after month. The team now forecasts SMB and enterprise customers separately. Finance can’t forecast enterprise accounts alone, he added. Customer success and sales know whether an account is at capacity or has room to expand, which turns the forecast from “just a number on the paper” into a strategic decision. AI in Finance: Faster automation, Higher stakes for Data integrity Zafar sees AI making finance both easier and harder. It already helps automate bank and account reconciliations, reporting and financial decks. But deeper uses, such as combining data points to analyze a customer fully, depend on data that lines up across systems. Without that common hook, “it’s essentially garbage in, garbage out,” and AI may point you the wrong way. His team has also implemented third-party tools that automate accounts receivable (AR) and accounts payable (AP), significantly reducing processing time. They aren't cheap, he said, but they're 'not the Mercedes of the brands.' He spends two to four hours a week learning AI himself and encourages his team to practice and fail with it. “If you don’t do it, you’re gonna get left behind.” Managing Cash Flow with Payment Terms Predictable cash starts with predictable payment terms. “You live and die with your collections.” Net 20 means payment is due 20 days after the invoice date. Customer Terms (receivables) Vendor Terms (payables) Target Net 20 Net 45, sometimes settling at net 30 How it’s held Sales can’t change terms without finance approval; about 95% of customers are on net 20 Negotiate everything with vendors Exceptions Mostly larger customers or trades for something else Varies by vendor Support Dedicated people or systems chase late payers AP automation reduces processing time At those terms, collecting at net 20 and paying at net 30 to 45 works out to a cushion of 10 to 25 days. Not every customer pays on time, he acknowledged. The goal is for most (60–70%) to pay promptly and to have people or systems chasing the rest. M&A Due Diligence: Evaluating a Back office Zafar has been on both sides of deals. As the buyer, he starts with the P&L and balance sheet, but he spends most of his time on customer data. He pulls customer, contract and billing data and tries to merge them. If they tie out easily, revenue evaluation is straightforward. If not, the gaps tell him how much to trust the reported revenue. Most of the time the revenue is real, he said; the hard part is pinpointing the contract and customer behind it. As the seller, he makes sure the customer-to-billing chain is as accurate as possible, even if it lives in Excel, so the company can show the strength of its data. Just as important, finance, sales, customer success and the CRO should all tell the same story. Post-acquisition integration: don’t rush the systems The most common trap, Zafar said, is rushing to merge financial systems. The better move is to understand how data flows from customer to contract to billing, then move the acquired customers into your existing process. “If we are acquiring a business, we’re acquiring revenue.” Don’t break a process that works just to force systems together. Before closing, he said, the one thing finance leaders should understand is the target's billing process, from identifying a customer to signing a contract to sending an invoice. Finance is usually more involved before a deal closes, so it can then guide sales and revenue operations on how to bring that data across. Vendor data, by comparison, is 'a pretty easy lift.' M&A readiness checklist, drawn from the session Can every customer be traced across customer, contract and billing records with one common key? Would your customer-to-billing data tie out if a buyer tried to merge it? Do finance, sales, customer success and revenue leadership tell the same story about customers? Are payment terms consistent, and are exceptions approved and documented? Before closing a deal, do you understand the target’s billing process end to end? Rogador’s closing thought: Getting financial systems and data right doesn’t start when the deal shows up. It’s what makes you ready when it does. This session is part of The Future is Fractional 2026. For how deal readiness connects to the wider shift in finance team design, read Own the Core, Access the Rest: What The Future is Fractional (TFIF) 2026 Revealed About How Finance Teams Are Being Rebuilt what finance leaders are rethinking about talent, AI and team structure.
