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David Seuss

Only recently have enterprises begun applying text analytics to in-depth market research content in order to distill "strategic scenarios", thus truly advancing the competitive intelligence function with automated analysis (what I call "meaning extraction"). In order for such analysis to have maximum value, it must be conducted against *all* of an organization's market research assets -- including primary and licensed secondary content, plus whatever news sources and industry or government databases are relevant to the company and its industry -- and it must be possible to conduct a unified search of that heterogeneous repository so all the relevant documents are part of the analysis. None of this is easy to do for many reasons -- and those companies that are doing it successfully have a significant leg up on their competition. Without "meaning extraction" technology in place, a common-variety search engine applied to a market research database of rich customer information puts an enormous burden on the user to read enough of the reports to gain an overall understanding of a topic like, “What are my customer’s strategic priorities right now?”

Julie Hunt

Hi David,
I appreciate your taking time to read my article and leave your comment.

These are very interesting and challenging times for many kinds of analytics, with a great many tech innovations in play. The participation of humans in analytic initiatives remains equally important.


Phil Simon

Great stuff, Julie.

This struck me:

**Although I’m not addressing data warehousing in this article, I do want to point out the need for basing any data-driven intelligence and analytics processes on reliable data.**

How many times have you seen people spend tons of cash on fancy tools that contain suspect data?

Talk about putting perfume on a pig....


Julie Hunt

Hey Phil! Glad you stopped by and had some time to read this article.
Thanks for adding your excellent point re: ascertaining - and maintaining - data quality before building out solutions.

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