We’ve worked on over 500 deals at QOE Prep. The number one difference maker on our end is having good data. I’ve had to untangle webs of different data formats set up by different accountants, all tied to different accounts. The mess alone can be enough to scare away potential buyers. That’s why I was so excited to talk to Alyssa McGinn about what she and her team are building. Alyssa has spent the last few years at infoFluency, a family-owned company that works at the intersection of data and investments. Lately, they’ve been developing Luminous, a tool for pre-LOI diligence and evaluation to help specifically in the M&A world. According to Alyssa, data plays a huge role in every M&A transaction from the moment a buyer looks at a deal all the way through exit. The first warning signs of bad data The earliest warning signs of bad data often show up during the diligence phase in an M&A transaction. Alyssa likes to ask how this process has gone to get a good idea of what a company’s data might look like. If it’s been like pulling teeth to get any kind of metric, it’s a clear indicator that there are some bigger infrastructure issues. Sometimes the seller cannot find the data points at all. She pointed out a few patterns that show up over and over: Information lives in an inbox. When information is in your email, it means it is owned by individuals instead of the company. Besides the inefficiency of tracking down the information, this is a systemic risk if the employee leaves the firm. Parts of the business run on old on-premise tools. These systems are usually isolated. They do not speak to anyone (or anything) else, which makes cross-department work almost impossible. The same metric has different names in different systems. One team calls it revenue. Another calls it bookings. Another calls it orders. When no one uses the same terms, everything downstream becomes harder. Alyssa shared an example of a large property-management company that wanted to grow through acquisition but felt blocked by their own internal systems. Everything was manual and siloed. Her team built an application that unified their accounting, investor reporting, maintenance tracking, and day-to-day operations. For good measure, they even threw some AI into the mix to get an overview of tenant requests. If you’re looking to sell your business, you should check to see if any of these data habits apply. Taking some time on the front-end to clean up your data could pay off in the long-run once potential buyers start taking a closer look. Rinse, lather, repeat, repeat, repeat Once the data is gathered and it’s formatted correctly, the next question is how it moves. In many companies, the answer is by hand. Someone exports a CSV. Someone else copies it into a spreadsheet. Then, three people check the numbers. And finally, a fourth person combines them into a deck for management to flip through. Alyssa shared an example from a client that was doing several hundred million in revenue. The private-equity owner needed quarterly board reporting. To get this done, the client had three people working on it for three weeks every quarter. In other words, 25% of three full-time employees’ workload was being used to produce something that could be automatically populated. Here’s a good rule of thumb: If something is repetitive, it can (and should!) be automated. Unfortunately, old habits die hard, and it can be a while before things get automated. Once your employee has done the same boring task 20 times, you shouldn’t be shocked if they do it 21 times. To break these habits, you’ll need to intentionally audit your systems for manual processes. If you’re looking for help, Alyssa and her team would be a great place to start. The notorious “single source of truth” Stop me when you know what I’m talking about: trolley, buggy, trundler, carriage, shopping cart. Unless you’re part of the 2% of my audience that lives in Australia, you probably were lost until I said “shopping cart.” Contrary to what your English teacher might have said, the English language does not have a single source of truth. Different words can have the same meaning, and one word can have different meetings. In the same way, a company’s data needs to speak the same language. It needs to follow the same rules and use the same format. Having a single source of truth means every system in the company will also feed into one place. Setting this up is harder than most people assume. Alyssa shared with me that these systems often need custom connections. You need to coordinate the right permissions and set good expectations with management so they can make good decisions. Alyssa and her team typically focus more on the output, operating with a “done-for-you” model. Once it’s setup, they’ll typically either teach the company how to use it or stay on as a fractional data team. In practice, infoFluency will partner with a portfolio company or investment firm and follow a six-step data intelligence process. It begins with understanding strategic goals and working through data discovery, architecture design, integration, validation, and finally, delivery of actionable insights. After all this, they’ll build a KPI scorecard that looks something like the one shown below. The scorecard gives leadership a single view of performance across the areas they care about and updates it automatically. This lets the leadership team (or you, the new owner) make decisions based on real data instead of gut feel or outdated reports. Give me your tired, your broken, your messy data While a QoE report can be thought of as an accounting exercise, it is also a systemic overview of the story being told by the data. The work reveals gaps, inconsistencies, and unexplained variances that can be buried under bad accounting data. A deal can only move as fast as the data allows it to. Whether you’re on the buy or sell side of things, getting a QoE can help you see what’s really going on in the business. If you’re interested in learning more about what it can do for you, check out our website or set up a call with me directly.