The Text Analytics Opportunity

Tom H. C. Anderson
May 13th, 2014

The Text Analytics Opportunity

Text Analytics remains an opportunity for those wishing to gain an information advantage

[Note: This is an ongoing series of interviews on analytics ahead of the Useful Business Analytics Summit. Feel free to check out the rest of the interviews beginning here]


My favorite topic of any analytics conference is of course the mining and analysis of unstructured data. Whether you call it Natural Language Processing (NLP), Text Mining, or the more recently popular Text Analytics chances are you’ve heard of it. Since I started Anderson Analytics ten years ago, as the first consumer insights firm to leverage text analytics, the discipline seems to have gone from an unknown to mainstream ubiquitousness.

That said, because it is such a quickly evolving field many of the main players 10 years ago have faded into relative obscurity. Some have been purchased by other companies, many have simply not been able to keep up with advancements or proven adequate value.

Therefore perhaps I shouldn’t have been surprised that this was the one area where our analytics experts were a bit less sure of themselves, and I received relatively few responses to my questions.

That said, consensus is that there certainly are text analytics software options on the market that do provide strong value. Personally I think the main challenge is that there are still too few analysts with experience in text analytics, and too little time allocated to prove just how amazing unstructured data insights can be!

Q. What is your opinion on the current state of the unstructured/text analytics field?



I am the wrong guy to ask, because I was already blown away six years ago when Attensity was boiling down conversations into subject-verb pairs, and things have only gotten better since then. I think there’s a point in the life of each



At this moment, I believe for the kind of experiments that people have started to leverage it for, it is good enough.



For me personally its more supplemental data. Structured data is easier to utilize, slice and dice.

Unstructured data might be very useful resource of qualitative data and supplemental to quantitative analysis.

Also there are tools that can create structured analytics from unstructured.



A lot of solutions exist in the market place but it is a complex problem and we have a long ways to go.


Q. What if anything in text analytics have you found that really works well? What doesn’t?



I don’t have direct experience with text mining beyond what we’ve done with Attensity.



What works well is the flexibility and ability to change and implement once you have the engine built. What doesn’t work well is the overpriced text analytics tools, which makes many, develop their own and miss the opportunity to focus on analytics instead of transforming the unstructured data.



Works well: Qualitative data, opinion based data.

Doesn’t : Certain KPIs without benchmark



High level sensitivity analysis and high-level signaling works well. But the solutions are not at a place for granular actionable insights. In other words, use them as an indicator and not as an actionable solutions.


Stop by for the next blog post as I ask our experts about tips for selecting a software vendor, how much software should cost. I’ll even be asking how our client side speakers like to be sold to…





[Full Disclosure: Tom H. C. Anderson is Managing Partner of Anderson Analytics, developers of patented Next Generation Text Analytics™ software platform OdinText. For more information and to inquire about software licensing visit ODINTEXT INFO REQUEST]

2 thoughts on “The Text Analytics Opportunity”

  1. We’ve found great value in unstructured data – particularly from social. From channels behaviors to segment behaviors. From Lead gen to churn prevention. Unstructured data offers extremely rich opportunities for customer insight.

  2. Combining text analytics with triplestores in the form of a semantic pipeline has the potential to not only operationalize the power of text but also to discover new relationships you did not know existed. When an inferencing engine is part of the triplestore, additional facts can be created from the original facts pulled from the text. Disambiguating entities that are in essence the same person but expressed slightly differently is very important and the ability to keep all of the unstructured text and the semantic facts in the triplestore synchronized leads to much greater accuracy and less data integration issues. Text mining by itself can be valuable but there’s an entire ecosystem of semantic technology around text mining that should be included in a complete solution. We have not begun to talk about high volume parallel load queries and inferences that need to be done at scale and in real time or Semantic Curation – all of which are very close to the text mining challenge. Today, the technology to do this is real. The tools to wire up a solution exist. Enterprise companies are doing this, in production using enterprise resilient clusters.

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