Forrester Says “Text Analytics is Key Trend to Watch”

Analytics, as I understand it, reveals intelligence from information. I use the term information because Gartner educated me that data means it is structured. Having worked with data as long as I have, not only is it not structured, it is a MESS!

The rush over the last few years has been that every BI/reporting tool has claimed to be analytics. The input to these tools is usually numbers. However – with the rise in social media, we are seeing a lots of free form text. The biggest rising pool of data is words, and all of it unstructured. Analytics on this needs to reveal patterns to quickly reveal “what is the gist?”

Forrester principal analyst Gene Leganza, compiled the top 15 technology trends over the next three years. He highlights technologies that are new or changing, have the potential for significant impact and require an IT-led strategy to exploit.

Gene Leganza highlights text analytics technology in the report because understanding unstructured data plays a critical part in daily operations. Enterprises have too much content to review and annotate manually. Text analytics products need to reveal hidden patterns, relationships and intelligence in the text. In the Forrester blog, Leslie Owens says “In my 2009 overview of text analytics, I cite the primary use cases for these tools: voice of the customer, competitive intelligence, operations improvements and compliance and law enforcement.”

The key to text analytics is that companies want to know what customers are talking about. What is the buzz? What do they like? What do they not like? What are they buying?  What is the trend?

Leslie Owens adds – “People want to drill in to high level analysis to find the specific thing customers buzz about. And many searchers don’t know how to articulate their need as a query and are looking for the big picture on a topic or trend.” I cannot agree more since I have long believed (and been told) that users hate having to query for intelligence!

In the chatter information, most of the text is noise. It is critical for the analytics to be able to reveal sense from nonsense.  The signal to noise ratio is low…..

This means the text analytics needs to push meaningful summary of all that chatter!!!

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