Alabama
The five data problems I see most in Alabama small businesses
Published
Short answer
Most Alabama small businesses do not have a data shortage, they have a trust problem. The same five issues come up again and again: duplicated customer records, numbers spread across systems that disagree, month-end figures that arrive too late to act on, no agreed definition of a key number, and reports nobody reads because nobody chose them.
1. The same customer, three times over
A customer books once by phone, once through a web form and once through a card terminal, and the system records three people. Repeat-customer rate then looks far worse than reality, and marketing spend gets aimed at winning back people who never left.
The fix is unglamorous: a matching rule on phone number and address, run once over history and then on a schedule so it stays clean.
2. Two systems, two versions of the truth
Revenue in the job system rarely matches revenue in the accounting system, because one counts the invoice date and the other counts payment. Neither is wrong. What causes the argument is nobody writing down which one the business is going to use.
Pick the source for each number, write the definition down in one page, and the monthly meeting stops being about whose spreadsheet is right.
3. Numbers that arrive after the decision
If August's margin lands on 20 September, it is history, not management information. Small Alabama operations run on short cycles — a slow week in a coastal shoulder season matters immediately. Weekly indicators beat perfect monthly ones.
4. Nobody agreed what the number means
Ask three people in a ten-person company what counts as a 'lead' and you often get three answers. Every metric needs a written definition: what is included, what is excluded, and when it is counted. This takes an afternoon and removes most reporting disputes for good.
5. Reports built for nobody
Software ships with dozens of built-in reports, so people run all of them and read none. A useful reporting pack is short: the handful of numbers that change a decision this month, and nothing else.
A local example
A typical pattern locally: a service business with roughly 2,800 customer records finds 300 to 400 of them are duplicates. Cleaning them usually moves the measured repeat-customer rate by several percentage points — the customers were always loyal, the data just could not see it.
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