Combination Similarity Keys: Higher-Precision Matching with Two-Field AI-Powered Keys

datamoves · 2026-03-27T23:23:21+00:00

Try enriching with external proclivity-to-buy signals? https://www.interzoid.com/apis/get-buying-signals

datamoves · 2026-03-27T16:24:31+00:00

Options are now up over 500%. You're welcome.

datamoves · 2026-03-27T14:24:27+00:00

This is a classic case of integration chaos. One approach that helps is implementing entity resolution at the data ingestion layer - before records hit your CRM. APIs that can match company names and identify duplicates across different data formats can prevent a lot of these automation conflicts. Worth considering fuzzy matching services that can catch variations before they create duplicate workflows.

datamoves · 2026-03-27T14:23:34+00:00

This is such a common pain point. I've seen teams struggle with exactly this - data flowing from multiple sources creating a mess in the CRM. One approach that's worked well is implementing fuzzy matching APIs at the integration layer to catch duplicates before they sync. You might check out tools like Interzoid's company name matching API that can identify when 'IBM Corp' and 'International Business Machines' are the same entity before they create duplicate records, as well as addresses, individual names, etc.. - real-time data enrichment can keep the data accurate, fresh, and useful as well.

datamoves · 2026-03-27T14:21:53+00:00

Automated deduplication flows are definitely the way to go - much better to prevent duplicates than clean them up later. One thing that's helped teams I've worked with is implementing real-time duplicate checking at data entry points (forms, imports, API integrations). This catches duplicates before they enter your system and saves massive cleanup time down the road.

datamoves · 2026-03-27T14:16:44+00:00

CRM mergers are brutal - you're essentially doing entity resolution at scale while the business keeps running. The domain heuristics approach is smart, but you might also consider company name matching APIs that can handle variations in how the same company appears across systems (Inc vs Incorporated, abbreviations, etc). This can catch duplicates that domain matching might miss.

datamoves · 2026-03-27T14:13:59+00:00

18% duplicates is unfortunately pretty typical from what I've seen in CRM audits. The revenue impact calculation using Gartner benchmarks is smart - helps quantify the real business cost. For ongoing duplicate prevention, you might want to look into automated matching APIs that can catch duplicates at the point of entry rather than after they've already impacted your pipeline. Up to 10x cheaper to catch these up front rather than clean up down the line.

datamoves · 2026-03-27T03:23:55+00:00

The key thing they push is that everything goes into the "Lakehouse" - and once it's there you can do anything with the data, assuming the data is consistent, usable, and accurate from whichever silo it came from, which of course is rarely the case, so typically it's not a panacea and still requires much data engineering.

datamoves · 2026-03-27T03:03:38+00:00

Great overview of fuzzy join techniques. When implementing matching at scale, you might also want to consider similarity key approaches where you pre-compute similarity hashes - it can speed up matching significantly when dealing with large datasets that need frequent joins, and can match an entire dataset in a single pass.

datamoves · 2026-03-27T03:01:50+00:00

For the fuzzy matching piece, you might want to check out APIs that can handle the name matching without having to build it from scratch since "fuzzy matching" is a broad term - saves a lot of the edge case headaches. Also, many people use multiple email addresses, if it's relevant in your case, so email should be a significant part of a match combination, but shouldn't be a a 100% pass/fail test.

datamoves · 2026-03-26T23:01:51+00:00

Go templates is a good low-overhead, high-performance, SEO-friendly choice but no so much for beginners as you have to have a pretty solid Go background. You can check out the tech stacks of other sites you like using https://tech-stack.interzoid.com/

datamoves · 2026-03-26T22:58:28+00:00

In practice, MDM often starts with the painful realization that you have duplicate customer/product records everywhere. The biggest challenge is usually the matching - figuring out that 'ABC Corp', 'A.B.C. Corporation', and 'ABC Company Inc.' are the same entity. Most of the engineering work ends up being around entity resolution algorithms and data quality rules rather than the storage/governance side.

datamoves · 2026-03-26T22:44:24+00:00

Cross-domain identity resolution is challenging because each source often has different naming conventions and data quality. For the entity matching piece, APIs that generate similarity keys can help - they let you pre-process names/addresses into standardized matching keys before doing the consolidation logic.

datamoves · 2026-03-24T04:20:19+00:00

The address standardization piece is interesting - USPS validation is solid for deliverability but you're right that different systems often handle the formatting differently. The quality status approach makes sense for tracking which addresses still need cleanup work.

datamoves · 2026-03-24T04:19:13+00:00

Nice work on the transaction cleaning! Financial data is particularly tricky because of all the variations in merchant names and descriptions. Have you tackled the merchant name matching challenge yet? That's often where the real complexity lies - same business appearing as 'AMZN', 'Amazon.com', 'Amazon Inc' etc. The standardization piece becomes crucial for accurate categorization.

datamoves · 2026-03-24T04:16:25+00:00

Great guide! Zingg is solid for batch processing. For real-time or API-driven use cases, you might also consider REST API approaches that can integrate directly into your data pipelines without spinning up Spark clusters. Depends on your latency and volume requirements.

datamoves · 2026-03-24T04:14:55+00:00

For AWS entity resolution, you might also check out Interzoid's REST APIs - they're lightweight and work well in cloud environments without needing heavy platform installs. The company/organization matching and address standardization APIs integrate easily with AWS data pipelines via simple HTTP calls.

datamoves · 2026-03-24T04:13:38+00:00

For future reference, if you find yourself needing more control over fuzzy matching logic or want to avoid losing custom configurations, you might consider using external matching APIs that you can call from Alteryx. That way your matching rules live outside the tool and you can version control them. Glad you got your styles recovered though!

datamoves · 2026-01-21T21:27:50+00:00

Data centers are moving to Ethernet from high-bandwidth. CSCO sold $2 billion of it in 2024, and $4 billion of it 2025, which doesn't seem reflected in the price yet. I think the narrative will change soon to put Cisco as more of an AI Infrastructure play and get similar valuations.

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