MDM Vendor Acquisition: How to Evaluate Your Data Quality Options
When your MDM vendor is acquired, your data quality program faces roadmap freeze, pricing pressure, and migration risk. Here is a five-step framework for evaluating your options and transitioning without disruption.
Text Data Cleaning: Techniques for Standardising Free-Text Fields at Scale
Free-text fields are the leading cause of downstream matching failures. Learn the six-stage text data cleaning pipeline that reduces match error rates by 40–70% across CRM, ERP, and operational databases.
What Is Merge Purge? A Practical Guide to Deduplication and Record Consolidation
Merge purge combines overlapping datasets and removes duplicates using fuzzy matching and survivorship rules to produce a clean master file. Here is the complete six-stage pipeline.
Entity Resolution in Financial Services: KYC, AML, and Fraud Detection
Entity resolution in financial services links fragmented customer and counterparty records across systems to power accurate KYC, AML screening, and fraud detection — with a full audit trail.
AI Data Profiling: How Automated Analysis Fixes Data Quality Before It Costs You
AI data profiling automatically scans every dataset field, scores quality, and flags issues before cleansing runs. Free trial, no contract.
ERP Data Quality: Why It Matters, Common Issues, and How to Improve It
Poor ERP data quality corrupts procurement, finance, and operations decisions. Learn the seven most common ERP data issues and the pipeline that fixes them before they cascade.
Data Quality in Healthcare: How Bad Data Impacts Patient Outcomes
Poor data quality in healthcare contributes to adverse events in 1 in 5 hospital admissions. Learn the four-stage pipeline to profile, standardise, deduplicate, and continuously monitor patient records.
The Real Cost of Duplicate Records: What Bad Data Is Costing Your Business
Duplicate records cost organisations an average of $15 million per year in wasted spend, failed analytics, and operational errors. Learn how to measure, eliminate, and prevent them with a seven-stage deduplication pipeline.
Data Scrubbing at Scale: Automated Approaches to Cleaning Dirty Data
Automated data scrubbing removes errors, duplicates, and inconsistencies through a 7-stage pipeline: profiling, standardising, parsing, address verification, deduplication, entity resolution, and monitoring.
SAP Data Migration Best Practices: The Data Quality Steps Most Teams Skip
ERP data migrations fail when dirty data moves with them. Here are the six data quality steps — profiling, cleansing, deduplication, entity resolution, address verification, and validation — that most teams skip before go-live.