If your company faced a serious legal issue, you would not ask your CFO to read some case law over the weekend. You would not build a spreadsheet to track your liability exposure. You would not wait until after your ERP migration to deal with it. You would hire a lawyer.
Data problems are different. They come in a hundred shapes and sizes, and because of that, people solve them in a hundred different ways, most of which feel like progress but very few of which actually are.
I have spent years working with lower middle market manufacturing and industrial businesses, and I have seen the same pattern repeat itself: a business owner or PE-backed operator knows they have a data problem. They know the answer is somewhere in their systems. And they put a band-aid on it, because the band-aid works well enough for right now.
That band-aid is expensive. Most people just never see the bill.
The Problem With Data Problems
When you have a legal problem, the path forward is clear. There is a whole profession built around solving it, a set of credentials that signals expertise, and a fairly predictable process for getting from problem to resolution.
Data problems have none of that clarity. They are shapeless. The symptoms show up in a dozen different ways: the owner who has to be in the building to know what is happening, the PE firm waiting three weeks for a portfolio update, the CFO rebuilding the same spreadsheet every Friday, the sales manager with no idea which rep is actually performing.
Because the problem looks different every time, people reach for different tools. And because most of those tools solve the immediate pain, it is easy to mistake relief for resolution.
The band-aid works. Until it does not. And by the time it stops working, the business has usually grown past the point where the same band-aid applies.
The Most Common Workarounds (And Why They Break)
Let me be direct: none of the following approaches are wrong. Each of them makes sense in context. But each of them has a ceiling, and in most manufacturing and industrial businesses I have seen, that ceiling gets hit faster than people expect.
The Spreadsheet
Fast, flexible, free. A good analyst can build a spreadsheet that answers almost any question. The problem is that the spreadsheet lives on someone's computer, depends on someone's time to maintain it, and is already out of date the moment it is sent. It does not scale, it does not automate, and when the person who built it leaves, it becomes a file no one understands.
The In-House Analyst Hire
This one feels like a real solution because it is a real investment. But a single analyst is still a single point of failure. They can pull reports, but they typically cannot build and maintain the kind of scalable data infrastructure a growing business needs. Recruiting and onboarding takes three to six months. And when that person leaves, you are back to square one.
The ERP Migration
This is the most seductive one because it feels strategic. A new system will fix everything. Cleaner data, better reporting, modern infrastructure. The reality is that ERP migrations move your data; they do not organize it. The reporting problems you have today will follow you into the new system unless someone has done the hard work of defining what you actually need to see and building the layer that surfaces it.
Migrations also take longer than planned, nearly every time. Waiting 12 to 24 months for visibility you could have in 30 days is a real cost.
Doing Nothing
The most honest version of this is: we know we have a problem and we will get to it eventually. Eventually is a moving target. And the business keeps making decisions in the dark while it moves.
Why an Analytics Function Wins
I want to be clear that I am not arguing those other approaches have no value. Spreadsheets are great for one-off analysis. A strong in-house analyst is a real asset. A well-executed ERP migration can transform a business. I am arguing that for most lower middle market manufacturing and industrial companies, none of those things is the right primary strategy for solving a data visibility problem.
Here is what I mean by an analytics function: a documented, automated, connected data layer that sits on top of your existing systems, surfaces the metrics that matter to your business, and updates without anyone having to go get the data. It is not a dashboard. It is infrastructure.
It wins for a few specific reasons:
- It works in the system you already have. You do not need a new ERP to start seeing your business clearly. We connect to virtually any system, from Global Shop and Epicor to Acumatica and NetSuite, and build on top of what is already there.
- It gets better as the business gets bigger. A spreadsheet breaks when the data gets too complex. An analytics function scales. Every new data source, every acquisition, every new product line can be added to the foundation.
- It survives transitions. When your CFO changes, your ERP migrates, or you bring on a new operator, the reporting layer is still there. Institutional knowledge does not walk out the door.
- It creates M&A optionality. Businesses with clean, automated, well-organized data tell a better story to buyers, bankers, and investors. One of our clients sold at a higher-than-expected multiple, in part because the analytics we built made the health of the business impossible to argue with.
- It frees up leadership. Owners and operators who have reliable dashboards stop being the dashboard. They can step away, pursue other ventures, and make decisions from a distance because they trust what they are looking at.
The goal is not to have a dashboard. The goal is to run a better business. The dashboard is just what makes that possible.
Why Now, Not Later
The most common thing I hear is some version of: we will do this when things settle down. After the migration. After we hire a new CFO. After we get through this busy season.
Things do not settle down. Businesses that wait for the right moment to build their data infrastructure are still waiting five years later, with five more years of decisions made on incomplete information.
Every month without visibility is a month of operational data you cannot get back. Every month you delay is a month your PE partners are making portfolio decisions with one hand tied behind their back. Every month is a month the business is running on gut feel when it could be running on facts.
If you had a legal problem, you would not wait. You would call someone who knew how to solve it.
Data problems are not that different. You just have to decide to solve them like they matter.