I’ve talked before about the growing science of analytics, where companies are using data to make better decisions faster, throughout their organizations. Analytics is going beyond traditional sources into the realm of text.
Analytics naturally came about as a result of today’s prolific generation of electronic data. The amount of numerical information created every day is staggering.
I have seen statistics that claim all of us are generating more than 2 million emails per second, and a similar quantity of tweets on Twitter. Then there are the electronic documents that are created and published from so many sources. Google processes dozens of petabytes of information per day — that’s millions of gigabytes.
Of course all of the text data that is being generated isn’t publicly available, but where it is, organizations are looking to exploit it to extract the useful business intelligence that may be buried within. The approaches being explored go beyond numerical analytics or information searching and retrieval, to add in academic-sounding techniques like artificial intelligence, natural language processing and semantic analysis. It’s definitely cutting edge, but traditional companies are using it.
For example the refrigeration and cooking appliance company Sub-Zero/Wolf Appliance is mining their massive text databases to cut the time it takes to identify and address product defects. They are sifting through their customer service data, inventory information, and warranty claims to help determine trends in product quality issues. Using these approaches the company has claimed to cut the time required to identify quality problems by more than 50 percent. More importantly product failures have been reduced, and of course this is yielding improved customer satisfaction.
A company called ChinaHR.com is using text analytics to extract data from resumes received online and match it with key data provided by prospective employers.
One global consumer products company is using the approach to look through online responses to new marketing campaigns. Text analytics is being used here to quickly sort through volumes of customer comments and discussions to organize and better understand consumer sentiments, and to provide better direction for future marketing efforts.
These are just a few examples, but applications such as these reduce resource requirements, along with administration costs.
Applications of text analytics to extracting hidden market insights are poised for significant growth. Business insights gained from mining social networks or online media has the potential to yield guidance to market research, provide competitive intelligence, or even illuminate perceived brand reputation or corporate image.
As with numerical analytics, the key to using some of the text data that is being generated is to get guidance for making better decisions, and for making them quicker and more efficiently. It may not be hard to understand the general usefulness of extracting information from text, but imagining where it might be applied specifically in your business is what might make a difference.
It isn’t just about the numbers anymore. There could be hidden treasure in your text.
- Text Analytics v. Semantic Content Enrichment (semanticweb.com)
- Text Analytics Gurus Discuss the State of the Industry (arnoldit.com)
- The Importance of Text Analytics: Yesterday, Today and Tomorrow (java.dzone.com)
- Text Analytics Basics (mediatemetrics.wordpress.com)
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