What you are already sitting on, and what of it is usable

The inventory nobody takes

Before buying anything, list what your business already stores. For most small businesses it comes to more than they expect:

• Point of sale or till: every sale, with items, time, amount, often payment method. Two years of this is typically 10,000 to 60,000 rows.
• Bookings or jobs: who, when, what, cancelled or not, by whom.
• Invoices and accounts: customers, amounts, dates raised and dates paid. The dates-paid column is one of the most informative in any small business and is almost never analysed.
• Website or shop platform: sessions, products viewed, baskets abandoned.
• Email and messages: enquiries, complaints, the words customers actually use.
• Suppliers: what you paid, when, and how that moved.

This is better material than anything you could buy, for one reason: it is about your customers, your prices and your area, and a bought dataset is about somebody else’s.

The question data can and cannot answer

Records can only answer questions about what was recorded, and the gap between those two things is where most small-business analysis goes wrong.

Your till knows what people bought. It does not know what they came in for and could not find. Your booking system knows who booked. It does not know who rang, heard the price, and put the phone down. Your invoices know who paid late. They do not know why.

So a perfectly clean analysis can tell you that Tuesdays are quiet, and cannot tell you whether Tuesdays are quiet because demand is low or because you are closed at lunch. The absent rows are invisible and they never appear in any chart.

The practical discipline: before analysing anything, write down what would have to be true for a row to be missing. It takes two minutes and it prevents most confident nonsense.

The row you analyse is a decision

This is the step people skip, and it changes the answer more than any technique.

A sales export has one row per transaction. Most of the interesting questions are about customers, not transactions: who comes back, who spends more over a year, who stopped. Answering those means building a second table with one row per customer: first purchase, last purchase, number of orders, total spent, average gap between orders.

That transformation is the analysis in most small businesses. Everything after it is decoration. And it is ordinary spreadsheet work: a pivot table, or twelve lines of Python, or a GROUP BY if your platform gives you a query window.

The equivalent for a service business is one row per job; for a clinic, one row per patient episode. Decide what a row means before you touch anything, because two analyses of the same export at different grains will disagree and both will look right.

How much is enough

There is no threshold, but there are honest bands.

Under a few hundred rows, you are in the territory of looking at it and thinking. Charts, sorting, reading. Anything called a model is describing noise, and the last lesson in this block goes into why.

A few thousand rows gets you reliable descriptive answers: what sells, when, to whom, which customers are worth what.

Tens of thousands gets you into genuine prediction, with caveats. Most independent shops reach this over two or three years; most small service businesses never do, and should not feel short-changed by it.

Getting it out

Nearly every platform has an export button producing a CSV, usually buried under reports. Take it monthly and keep the files. Two reasons: platforms change what they expose, and vendors get acquired.

Watch for three things in the export. Dates arriving as text in a regional format, which will silently misread. Currency symbols and thousands separators turning numbers into text. And a “customer” column that is a name rather than an identifier, which means two customers called the same thing are one, and one customer who moved house is two.

Free path for all of it: LibreOffice Calc opens anything a spreadsheet can hold, Python with pandas handles files far too large for a spreadsheet, and SQLite will take a million rows on a laptop without complaint. None of this costs anything.

The first exercise worth doing

Export twelve months of sales. Build the one-row-per-customer table. Sort by total spent, descending.

Most owners find something they did not know within five minutes: usually that a small group accounts for far more than they assumed, or that a category they are proud of is nobody’s second purchase. That is the value of this block, and it arrives before any model does.