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Visible and invisible tech stacks, and the upsides and downsides of “shadow IT” in martech and beyond

chiefmartech

I think we can safely say her predictive insight has been thoroughly validated. The three main reasons: It may be wasted spend, duplicative of existing IT-approved licenses. But it does present a non-trivial trade-off. One step is to de-couple technical approval and financial approval for apps used by individuals and teams.

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Machine Learning Problems: The Easy Parts

Contify

So, we implemented de-duplication algorithms to significantly reduce the resources required to process the information in these documents. We therefore had to write our own version of hierarchical clustering that returns a large number of tight clusters or duplicates. just rephrased. Entity Identification. Named Entities.

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Machine Learning Problems: The Easy Parts

Contify

So, we implemented de-duplication algorithms to significantly reduce the resources required to process the information in these documents. We therefore had to write our own version of hierarchical clustering that returns a large number of tight clusters or duplicates. just rephrased. Entity Identification. Named Entities.

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Sales Intelligence: What to Expect When You’re Prospecting

Zoominfo

Uniqueness : Are there duplicate records? Validity : Does the data conform to a common syntax? Look for tools that automatically sync, append, enrich, and de-dupe your data as new information becomes available and your database grows. How to Prospect at a Trade Show with Sales Intelligence. Can records be combined?

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Sales Intelligence: What to Expect When You’re Prospecting

DiscoverOrg

Uniqueness : Are there duplicate records? Validity : Does the data conform to a common syntax? Look for tools that automatically sync, append, enrich, and de-dupe your data as new information becomes available and your database grows. How to prospect at a trade show with sales intelligence. Can records be combined?