David Hill of IT-TNA recently wrote, “The talk these days is all about big data, but extracting insights that lead to value is the role of analytics — not just big data alone. And since a lot of that data is textual in nature, then the responsibility for delivering value falls upon textual analytics. And that is a big deal. Recently, I attended the Text & Social Media Summit (which was co-located and had some joint sessions with the Customer Analytics Summit) in Cambridge, Massachusetts…  Traditional analytics has tended to focus on structured data, i.e., relational databases (such as doing analyses using traditional data warehouses). Much of big data tends to fall into the unstructured data category (I use semicolons to distinguish between semi-structured and unstructured data, but I won‘t push the difference here).”

Hill adds, “That unstructured data tends to respond to analytical techniques such as text analytics, rather than the analytics typically applied to SQL data. That has led to the thesis that the two are separate and distinct (as well as to the thesis that non-SQL techniques will dominate). Ralph Winters of Emblem Health (and other speakers, such as IDC) vigorously disagreed with that point of view.”

Hill continues, “In the 21st century, text analytics is taking advantage of machine learning, semantic analysis, and deep linguistic parsing. All that can lead to useful applications, such as loan default analyses and sentiment analyses. One of the more important areas is medical diagnostics, where early diagnostics can eliminate common source of error. Another is the use of text analytics in eDiscovery, which is the examination of electronic information for evidence in a legal case. These areas are just a few examples, but they illustrate areas to which text analytics can be applied.”

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