Old data, made usable.
I extract data from legacy systems, databases, and exports, then clean and structure it for the work ahead. You get usable datasets and documented pipelines for analytics, machine learning, and AI.
The data exists. Getting it ready is the work.
Years of records sit across databases, exports, and flat files. Before your team can analyze them or build a model, someone has to reconcile the formats, check the records, and make delivery repeatable.
- Records scattered across formats, systems, and years
- No shared schema, so the same field means three things
- Missing values, duplicate records, and totals that do not reconcile
- Manual exports with no reliable refresh or failure alert
From source system to usable dataset.
Extraction
Historical records pulled out of legacy systems, exports, and paper-era flat files, with the source system left intact.
Cleaning and normalization
Deduplication, schema mapping, and reconciliation of conflicting definitions, so one record means one thing everywhere.
Data quality checks
Checks for missing fields, duplicate records, row counts, and reconciled totals. Invalid records follow an agreed handling policy rather than passing through silently.
Analytics and training datasets
Shared field definitions and a schema built around the intended use. When training labels are needed, the classification rules are part of the handoff.
Scheduled pipelines
Load into your warehouse or project environment, with a refresh schedule, failure alerts, and instructions for safe reruns agreed in the scope.
Lineage and handoff
Code, source-to-field mappings, quality rules, and a runbook. Your team can trace a record and understand how to operate the pipeline.
Production standard, not pilot standard.
For four years I owned the pipeline layer inside a rural Critical Access Hospital in the Midwestern US: 200+ automated reports and regulatory submissions with zero missed deadlines, and BI-side data conversion on a Veradigm to Paragon migration working alongside Altera. That experience shapes how I build: explicit checks, documented lineage, and instructions someone else can follow.
200+
Automated reports and submissions in production
4yr
Owning the pipeline layer, on call
0
Missed regulatory deadlines in four years
Scoping starts with the source.
An engagement begins with an inventory: what data exists, where it lives, and what condition it is in. We agree on the destination, field definitions, quality checks, refresh schedule, and handoff. You get a fixed scope and a written price before the build.
For the first conversation, bring a list of source systems, the output you need, and the manual step or failure slowing your team down. Access requirements are agreed before any data moves.
What should your data make possible?
A report, a migration, a model, or a dependable refresh. Tell me the source and the intended output. We can start the scope on a 20-minute call.
Book a 20-min call →christian.g.callahan@cgclabs.org · Direct reply within one business day.