Before you can trust your data, you have to fix it. Most businesses I work with have the same problem: years of records spread across systems that were never designed to work together. I clean it up, connect it, and give you a foundation you can actually build on.
I map what you have, where it lives, and what condition it's in. Duplicate records, missing fields, inconsistent formats — I document every issue before I touch anything.
I use AI-assisted matching to catch fuzzy duplicates and near-misses that rule-based cleanup won't find, then normalize your records so the same customer looks the same everywhere.
Your POS, CRM, accounting software, and marketing tools each hold a piece of the picture. I build automated data pipelines that keep everything synchronized.
Moving to a new platform? Merging data from an acquisition? I handle extraction, transformation, and loading so nothing gets lost or corrupted.
You don't trust your own numbers. Someone pulls a report and the totals don't match what you see in another system. So you second-guess everything, or worse — you stop looking at the data altogether.
You've outgrown your setup. What worked when you had 50 customers doesn't work at 500. Your spreadsheets have tabs referencing other tabs referencing other spreadsheets.
You're about to invest in analytics or dashboards but your data isn't ready. You know you need better reporting, but every tool you try produces garbage because the underlying data is inconsistent.
A typical cleanup engagement starts with a 1–2 week audit. I connect to your systems, profile the data, and deliver a findings report that lays out every issue in plain language — not a spreadsheet of error codes, but a clear picture of what's wrong and what it's costing you.
From there, the cleanup itself usually runs 3–6 weeks depending on volume. I work in stages — standardizing records, deduplicating, filling gaps, and building validation rules that prevent the same problems from creeping back in.