by Angela Guess
A recent article takes a look at the rise of text analytics and asks the question, is text data processing part of your enterprise information management framework? The writer states, “The current prominence [of text analytics] is due to the number of different organizations using text analytics to gain insight into how the masses use social media and the Web. Customer service and support, brand-reputation management, competitive intelligence, and market research applications are a few examples that highlight the main-stream adoption of text analytics. These applications and others use text analytics for automated, natural-language processing techniques to identify and extract names, facts, relationships, and sentiment, etc. from a range of data sources, including blogs, forums, news, twitter/social updates, and e-mail (going forward I will refer to these as Web/social data).”
The article continues, “No one questions that significant insight lays hidden within Web/social data. Many organizations use it as described above. Others want to dig deeper, and often that requires a well-thought-out EIM framework to handle text data and help derive insights from across the enterprise, including back-office processes.”
It goes on, “To understand the use of text analytics (depth/breadth) within your organization and the maturity of your EIM framework to process text data, ask the following questions: (1) Can your organization track sentiment around its top-10 customers based on amount of revenue generated over the past four quarters and their top 3 products, including all name variations for their company, products, and subsidiaries? (2) Can your organization then effectively use the information above to develop a revenue risk mitigation strategy? (3) Can your organization track sentiment around its top-10 suppliers and their products – including all the name variations – and then use that information to mitigate any risks associated with product quality and the impact it might have on your customers?”
