We want every small business to have the data team only billion-dollar companies could afford.

SECTION 01The same questions at every size

Why was last month down? Am I making money on this job? Can I afford to hire somebody? Should I open another location? Where is it all going?

Every owner of a business doing $2 million a year lies awake on those questions, and on every other one they never get around to asking. So does the person running a $2 billion company. The questions are the same. What has never been the same is what happens next.

At the big company, somebody walks down the hall and asks a team whose whole job is to know. At the small one, the owner opens a spreadsheet at eleven at night, or goes with their gut, or does not ask at all.

The difference was never the questions. It was who you could ask.

SECTION 02What a data team is

A data team is a group of people whose job is to know what is happening inside a business, why, and what is worth doing about it. A working one is about seven people: someone who leads it, an engineer who gets the data out of every system and joins it, data scientists, analysts, and someone who keeps the whole thing running.[3]

Built in-house and paid at federal median wages with benefits counted, that team costs about $1,107,000 a year, and up to $1,572,000 in the cities where these people mostly live.[4] We worked that number out seat by seat in what it costs to build a data team in-house.

Large companies have run teams like this for forty years. They are why a national chain knows by Tuesday which of its stores had a bad Monday, which products are losing money once the returns are counted, and what a price change will do before it makes one. None of that is genius. It is a team, paid to look, every day.

SECTION 03The wall, computed

The team costs the same whoever hires it. What changes is how big that cost is next to everything else the business pays for.

The Census Bureau counts every employer firm in the United States, 6,395,635 of them in 2022, with their employees and their total payroll, grouped by revenue.[1] We brought each group's average payroll forward to 2026 wage levels with the Bureau of Labor Statistics' Employment Cost Index, a rise of 15.81 percent,[2] then set the $1,107,000 a year team against it.

The full table. Each row is the average US business in that revenue band: how many people it employs, its total yearly payroll at 2026 wage levels, and what the seven-person team would cost as a share of that payroll.

Read the two ends of that table together.

A company doing $100 million or more has, on average, 2,044 people. Seven more is one in every 292, and 0.59% of payroll. Nobody has to win an argument to hire them.

A business doing $2.5 million to $5 million has, on average, 19.4 people and pays them $1,072,200 a year in total. The same seven people would cost 103.25% of that: more than every other person in the business put together. At $5 million to $7.5 million it is still 61.96%. At $10 million to $15 million, 32.05%.

Percentages hide how physical this is. Here it is in people, one dot each.

That number is the wall. Every company above it has spent forty years knowing. Every company below it has spent forty years guessing, and calling it instinct.

And the wall is not at the edge of the economy. Only 35,420 firms, 0.55% of the total, sit at $100 million or more. The other 6,360,215 employ 63.4 million people, 46.68% of everyone who works for an employer in the country. The 35,420 above the line take 74.83% of all the revenue, and every one of them can afford to know what is happening inside itself.

Across the 872,980 firms doing $2.5 million to $50 million alone, the team would cost 45.23% of the average payroll.

SECTION 04Data rich, intelligence poor

Everyone has heard that data is the new oil. Almost nobody finishes the sentence. Crude oil is worth nothing until somebody refines it.

Small businesses are not short of data. The register has every sale for six years. The books have every bill. The scheduler knows who worked when, the payroll file knows what it cost, and the bank feed knows where every dollar went. What they are short of is the refinery.

They have been sold one thing after another that collects it all and hands it back as a report. Reading that report properly takes a data scientist, and nobody at $10 million a year has one. So they stay data rich and intelligence poor: told what happened, over and over, and never once what to do about it.

The refining is the whole job.

SECTION 05Making the team hireable

There were two obvious ways to bring this down to a small business, and both are wrong. Shrink the team, and you get one analyst doing three jobs, two of which never happen. Hand the owner something to run, and you have given the busiest person in the building another job.

So we did the third thing. We made the team itself hireable: the whole team, working on one business, for less than the cost of one person.

It starts with the slow part. Before we answer a single question, we rebuild the business out of the data it already has. The register, the books, the scheduler, the payroll file, the bank feed: systems that have sat side by side for years without speaking. We join them, reconcile them against the profit and loss statement until every day of the year agrees to the penny, and then write the business its own dictionary, because one company's "sale" is another company's "order placed".

What comes out is something no small business has had: a working copy of itself, correct, in its own words. That part is hard and takes weeks. It is also why everything after it looks easy. Questions that used to be month-long projects become a lookup.

SECTION 06What the team does

Hiring us is hiring the whole team at once, with no ramp and nothing to learn.

• They read the business every morning, before the doors open, and say something only when something needs the owner.
• They answer anything the owner asks, in plain English: pricing, margin, customers, staffing, stock, cash, a job that felt wrong.
• They test a decision before the owner makes it. Forecasts, and what a price change, a new hire or a second location would actually do.
• They do not wait to be asked. Every month they go looking for money nobody knew was there: the price that drifted, the customer who stopped coming, the job that never got invoiced.
• They take the reporting off the owner's desk, so the reconciling and the month-end summaries are simply done.

And every one of those ends the same way. We bring the owner what we found and what it is worth, and the owner decides. They know things about their business no record will ever hold. A team that forgets that is a team that gets ignored.

The kinds of things we look for, and why an owner cannot see them from where they stand, are in the four things you cannot see in your own business.

SECTION 07Where this goes

Understanding a business this well is the start, not the whole of it. Once you have rebuilt how a company actually works, you can move from reporting what happened to helping run what happens next.

What we are building toward is a team that has learned how this owner runs this company, well enough to handle the routine calls the way they would, and bring them the ones that need them. Not a generic answer for an average business. Theirs.

What kept small businesses out was never talent. It was access.

The point of all of it is a small business that can compete with anyone, because it finally knows what the big one down the road has always known.

SECTION 08Who it is for

We work with owner-operated businesses across the United States doing $2 million to $50 million a year, in any industry. The team is built around the business it serves: a four-store retailer and a contractor get different specialists and the same standard.

We hire on like any employee, with one difference. If at any point in the first three months the owner decides it was not a good hire, for any reason, every dollar comes back and they keep everything the team found.