Data engineering · Charlotte, NC
Charlotte data engineering, from raw records to a chart
Charlotte is a big place with a lot of neighborhoods, and a business here often sells in several of them. Our pipeline gathers the records from each channel and location, cleans them to match and charts the comparison. Four steps, one result.
One table for a business spread across Charlotte
Wikipedia calls Charlotte the second-largest banking center in the country, and the city has districts that each behave like their own market: Uptown, NoDa, Plaza Midwood, SouthPark, Ballantyne and more. A restaurant group, a gym or a dental practice with two sites sees two sets of numbers.
We start by mining each site's records into the same table with a column for the place. That one change lets you ask questions across neighborhoods instead of squinting at separate exports.
Charlotte data engineering and fair comparisons
Two locations rarely record things the same way. One front desk types the service names in full, another uses shorthand, and a third keeps prices with dollar signs. Compare those raw and the results mislead.
Cleaning gives every location the same labels, merges customers counted twice and stores prices as numbers. Only then does analysis mean something, and the charts at the end compare like with like.
Public numbers beside your own
Scraping collects public details, such as listed prices and review counts, from pages that allow it, so your own table can sit next to your competitors' figures.
Our rule is public pages only, within each site's terms. The result is a chart that shows where you lead and where you trail. Each public figure carries the date we collected it, so an old price never passes as a current one.
What a Charlotte report looks like
The report opens with the question, then the finding, then the chart. It says what changed, what we could not tell from your rows and what to try next.
With several sites, the last page usually ranks them on one measure, so you see the strongest and the weakest at a glance and can ask why. You can then pick one site to study first, and we dig into its rows together.
Questions from Charlotte owners
I run two locations with different systems. Can you join them?
Yes. We pull each system's export, match the fields and stack them into one table. If a field exists in one system only, we say so and show what that limits.
Which Charlotte neighborhood should I read first?
We can't know that before we see your rows. Once the table exists, one chart of sales by neighborhood usually shows the answer, and we talk through it on a call.
Is this only for companies with a data team?
No. Most small businesses have none. You bring the business question, and we handle the rest and explain it in plain words.
Sources for the facts on this page: Charlotte, North Carolina, Wikipedia
Tell us how your Charlotte data is split
Send a few sentences through the contact page, and we will book a free call to sketch the first table.