Data Quality, Fixed and Automated
A one-time cleanup of the mess in your CRM and ERP, plus the pipeline that stops it coming back.
Duplicate customer records. Four spellings of the same vendor. Dates in three formats because two systems were merged in 2021. Fields that were mandatory for one quarter and empty ever since. Every report you run inherits all of it, which is why nobody in the building fully trusts the numbers. This is two pieces of work sold as one. First we clean what is there. Then we build the validation rules and the automated pipeline that keep it clean once we are gone. The second half is the part that matters — a one-time cleanup on its own decays back to roughly where it started within a year.
What's Included
Deduplication and merging
Finding the records that are the same customer, vendor, or asset wearing different spellings, and merging them under rules you approve first.
Standardization
One format for dates, addresses, phone numbers, part numbers, and anything else currently stored five ways.
Validation at entry
Checks that run when a record is created, so bad data gets caught by the system instead of by you three months later.
The automated pipeline
Scheduled jobs that re-run the cleaning rules on new data, with logging, so quality does not quietly degrade again.
Exception routing
The records that cannot be fixed automatically go to a person, with the reason attached, instead of being silently dropped.
Written rules
Documentation of every rule and why it exists, so whoever inherits this understands what they are looking at.
How It Works
Profiling
We measure the damage before proposing anything: how many duplicates, which fields are broken, and which reports are downstream of the worst of it.
Rules and sign-off
You approve every rule before a single record changes. Nothing gets merged or overwritten on our judgment alone.
Cleanup run
The cleanup runs against a copy first. You see the before and after, and what would have changed, before it touches production.
Pipeline build
The approved rules become scheduled, monitored jobs that run against new data automatically.
Handover
Documentation, the exception process, and a walkthrough with whoever will own it day to day.
Not sure which of these you need?
Describe what you're dealing with in an email. You'll get a straight answer about whether we can help — and if we can't, we'll say so.
info@snowllabs.com