Master Data Cleansing: How to Get Results Quickly After Implementation
Master data cleansing delivers clean, deduplicated, verified records in 30 days using a six-stage pipeline: profile, standardise, deduplicate, resolve entities, verify addresses, and automate.
Data Quality Software Deployment: Evaluating Cloud vs. On-Premise Options
Cloud SaaS deploys in hours with zero infrastructure overhead. On-premise gives security-first teams full data custody. Here is how to choose the right data quality software deployment model.
Fuzzy Matching in SQL: Why Native Functions Break at Scale and What to Use Instead
SQL’s LIKE, SOUNDEX, and pg_trgm break on real-world data at scale. Learn why native SQL fuzzy matching fails and what dedicated platforms do instead — with pipeline stages, scored examples, and thresholds.
Mapping Revenue Bands to Scores: A Complete Implementation Guide
Mapping revenue bands to scores converts raw revenue into a discrete numeric value using a tiered scoring model. Covers band design, threshold logic, and pipeline integration with worked examples. Free trial, no contract.
How to Build a Financial Data Quality Management Program
A financial data quality management program profiles, standardises, deduplicates, and continuously monitors every financial dataset. Here is how to build one in six stages.
When Enterprise MDM Is Overkill: Right-Sizing Your Data Quality Stack
Enterprise MDM costs $200K–$1M+ per year and takes 12–18 months to deploy. Most data teams don’t need it. Learn how to right-size your data quality stack with fuzzy matching, entity resolution, and job automation.
Best Financial Data Quality Software: Features, Use Cases, and How to Evaluate
The best financial data quality software combines AI fuzzy matching, deduplication, entity resolution, and CASS address verification in one pipeline. Here is what to look for and why each capability matters.
How to Match Records Across Multiple Systems Without a Shared Identifier
Match records across multiple systems without a shared identifier using a five-stage pipeline: profile, standardise, block, score, and apply survivorship rules to build a golden record.
Entity Resolution in Automated Pipelines: Connecting Records Without Manual Review
Entity resolution in automated pipelines links records across siloed systems using fuzzy scoring, threshold routing, and survivorship rules — no manual review for every pair. Here is how to build it.
How Fuzzy Matching Algorithms Work and When to Use Each One
Choosing the wrong fuzzy matching algorithm for your field type is the top cause of missed matches. Learn which algorithm fits names, codes, phone numbers, and free text — with threshold tables.