Data Cleanup and Analytics

Having data is not the same as being able to use it.

Most businesses have plenty of data. It is sitting in a Customer Relationship Management (CRM) system, a few spreadsheets, an accounting package, and a vendor portal. The problem is that the same customer appears four different ways, half the required fields are blank, and nobody trusts the report enough to act on it.

Clean the data and the rest gets easier.

What I build

Ingestion

Getting data out of wherever it lives and into one place. Files, databases, feeds, Application Programming Interface (API) connections. This part is rarely one and done, so we set a schedule for how often new data comes in.

Cleaning and validation

The real work. Finding duplicates, checking required fields, matching values to expected formats and ranges, standardizing text so the same thing is not stored five different ways.

The closer this happens to the point of ingest, the better. If bad data gets into the database and each person writes their own rules to filter it, everyone ends up working from a different version of the truth.

Reporting

Once the data is trustworthy, reports can run on their own. Dashboards that update from the source instead of from someone’s Monday morning export.

Models

For businesses with enough history, forecasting and prediction. Nothing exotic. Models you can explain to your team.

Why bother

Every hour spent cleaning data pays back twice. Once because the reports become reliable, and once because anything you build later, including AI, only works as well as the data underneath it.

Good models are built on volume and quality. You cannot skip the second one.