Legacy Data Platform End of Life: Your Migration and Data Quality Options
When a legacy ETL or data integration platform reaches end of life, migrate with a data quality pipeline first: profile, cleanse, deduplicate, resolve entities, and verify addresses before loading into your new system.
Entity Matching: How Algorithms Identify the Same Real-World Object Across Datasets
Entity matching links records across datasets that share no common key, using fuzzy scoring algorithms to identify the same real-world object. Here is how the six-stage pipeline works.
Best Data Preparation Tools: A Framework for Evaluating Your Options
The best data preparation tools cover all six stages: profiling, cleansing, deduplication, address verification, entity resolution, and job automation — in one automated pipeline.
The Modern Guide to Data Cleansing: Tools, Techniques and Best Practices
Modern data cleansing runs as a six-stage automated pipeline — profiling, standardisation, deduplication, address verification, entity resolution, and monitoring. Learn the tools and techniques that cut data error rates by 30–60%.
Entity Resolution in Healthcare: Patient Matching, Compliance, and Data Quality
Entity resolution in healthcare links fragmented patient records across EHR, pharmacy, lab, and claims systems into a verified golden record — with a full HIPAA-ready audit trail.
Dedupe Software for Multi-Source Data Integration: How to Handle Overlapping Datasets
Dedupe software for multi-source data integration identifies and collapses duplicate records across CRM, ERP, and third-party sources using fuzzy matching and survivorship rules to produce a clean golden record.
Data Deduplication Software: How It Works and When You Need It
Data deduplication software identifies, scores, and merges duplicate records into a single golden record using a six-stage pipeline of profiling, standardisation, blocking, fuzzy matching, survivorship, and output.
EMPI vs Entity Resolution: What Healthcare IT Teams Need to Know
EMPI is healthcare-specific patient identity matching. Entity resolution is the broader, configurable alternative for any domain. Learn the key differences, where each fits, and when to use both.
Data Audit Trails for Governance: How to Track Every Change to Your Records
A data audit trail records every insert, update, and delete on your records — who, when, and what changed. Learn the five components, six-stage implementation workflow, and how automated data quality pipelines generate governance-grade trails automatically.
Model Context Protocol and Data Quality: What Data Teams Need to Know
The Model Context Protocol lets AI agents call data quality tools — profiling, fuzzy matching, deduplication, entity resolution — as schema-validated operations with no custom integration code.