Going Beyond the Numbers: How to Incorporate Textual Data into the Analytics Program by Cindi Thompson.
From the post:
Leveraging the value of text-based data by applying text analytics can help companies gain competitive advantage and an improved bottom line, yet many companies are still letting their document repositories and external sources of unstructured information lie fallow.
That’s no surprise, since the application of analytics techniques to textual data and other unstructured content is challenging and requires a relatively unfamiliar skill set. Yet applying business and industry knowledge and starting small can yield satisfying results.
Capturing More Value from Data with Text Analytics
There’s more to data than the numerical organizational data generated by transactional and business intelligence systems. Although the statistics are difficult to pin down, it’s safe to say that the majority of business information for a typical company is stored in documents and other unstructured data sources, not in structured databases. In addition, there is a huge amount of business-relevant information in documents and text that reside outside the enterprise. To ignore the information hidden in text is to risk missing opportunities, including the chance to:
- Capture early signals of customer discontent.
- Quickly target product deficiencies.
- Detect fraud.
- Route documents to those who can effectively leverage them.
- Comply with regulations such as XBRL coding or redaction of personally identifiable information.
- Better understand the events, people, places and dates associated with a large set of numerical data.
- Track competitive intelligence.
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To be sure, textual data is messy and poses difficulties.
But, as Cindi points out, there are golden benefits in those hills of textual data.