Make Trust in Your Data Measurable
Data quality engineering — profiling, rule libraries, remediation pipelines, and scorecards on Informatica CDQ and modern DQ frameworks — so decisions, migrations, and AI models run on data you can defend.
Why Data Quality Programs Stall
Every enterprise claims a data quality problem; almost none can quantify it. That gap is the tell: without profiling, rules, and scorecards, quality is anecdote — and remediation is whoever shouted loudest. The bill lands during migrations (garbage moves faster in the cloud), regulatory reporting, and every AI initiative that quietly underperforms because the training data was never trustworthy.
The Apptad Approach to Data Quality
We industrialize quality: automated profiling to locate the actual damage, rule libraries tied to business impact, remediation pipelines that fix at the source, and scorecards that make quality a number leadership tracks. AI-augmented anomaly detection extends coverage past what hand-written rules catch.
Profile first, argue later
Automated profiling across critical domains replaces opinion with evidence — you fund fixes by measured business impact.
Rules with owners
Rule libraries mapped to domains and stewards, enforced in-pipeline (not in quarterly cleanup projects).
Fix at source
Remediation workflows that route defects to the system and team that created them — downstream patching is a treadmill.
Scorecards leadership reads
Domain-level quality trends over time. Our insurance client’s board sees the same number the stewards do.
Data Quality in Production
Global Manufacturer Replatforms 1,800 PowerCenter Mappings to IDMC in 12 Weeks
Automated conversion with parallel-run validation moved two decades of PowerCenter integration to Informatica IDMC — zero data-quality regression, on-prem estate retired.
Read the case study
P&C Insurer Auto-Adjudicates 60% of Claims with AI on Snowflake + Azure
Document AI + ML triage automate routine claims and reduce average cycle time from 14 days to 2.
Read the case studyData Quality, Answered Straight
How do you measure data quality?
Six standard dimensions — completeness, accuracy, consistency, timeliness, uniqueness, validity — applied as profiled rules per domain and rolled up into scorecards. The measurement framework matters less than the discipline: rules with owners, trended over time, tied to business impact.
Can data quality work run inside a migration program?
It should. Migration is the cheapest moment to fix quality — the data is already being touched, profiled, and validated. Our PowerCenter-to-IDMC migration ran parallel-run validation with row-level comparison and delivered zero quality regressions across 1,800 mappings.
Do AI-based approaches replace quality rules?
They extend them. Anomaly detection catches drift and unknown-unknowns that rule libraries miss; rules encode the constraints the business already knows. We deploy both — rules for the known, ML for the unexpected — feeding one scorecard.
Part of our Data Management practice.
Have a Data Quality Question? Ask the People Who Ship It
Our Data Management practice is led hands-on. Connect on LinkedIn or send a note through the form — a practitioner (not a sales rep) replies within one business day.
