Business intelligence (BI) is an interdisciplinary domain. It draws heavily on information management, computer science and statistics; it also reaches into tangentially related disciplines such as operations research. As the scope of BI expands to include unstructured data sources, particularly free-form text, the discipline will be required to tap other subject areas to complement its existing set of tools. The social sciences have long dealt with analyzing unstructured data and are obvious candidates for appropriating text analysis methods. Many social science studies are based on field notes, survey questions, transcripts and other unstructured sources. Social scientists have developed a broad set of practices called qualitative data analysis (QDA) for dealing with data that does not lend itself to numeric representations. It's also just what we need as the fields of BI, knowledge management and enterprise content management merge.

Here are the basics. QDA is the process of coding segments of free-form text with predefined categories. The segments can be single words, phrases, sentences or entire paragraphs. Coded segments can overlap as well. For example, consider a customer comment on a digital camera such as, "To avoid red eye, remove the flash - but this may cause your subject's eyes to dilate." The phrase "to avoid red eye, remove the flash" in this sentence may be coded as "flash problem," while the entire sentence may be coded as "red eye."

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