Unstructured data holds essential business insights. How can you get to that insight?
By Eric Rogge, Sr. Director of Marketing, ExaleadOrganizations are increasingly storing vast amounts of unstructured data in new Hadoop, NoSQL, and MPP analytic databases, and business intelligence tools are getting better about connecting with them.
Still, even with improving connections between BI and unstructured data stores, the challenge with today's business intelligence deployments is that they only enable quantitative analysis of a fraction of an enterprises' information assets. That's because the majority of information available to an enterprise is unstructured content held in documents, e-mail messages, collaboration forums, and on the Web.
Enterprises now realize that to have a complete, 360-degree view of their operations, they need to analyze that unstructured data. That analysis involves both qualitative assessments as well as quantitative analytics. The challenge of BI isn't storing the unstructured data; it is the significant back-end development work needed to gather and quantify unstructured information sources.
Missing from an enterprise's portfolio of BI tools are search and semantic processing technology, which can efficiently process unstructured data into gists and metrics, plus handle large volumes of data from widely dispersed sources.
The effectiveness of today's BI solutions can be improved by working in conjunction with search-based applications (SBAs). SBAs are a new, emerging category of search and semantic technology that aim to improve operational productivity through processing, analysis, and delivery of key information drawn from internal and Web unstructured data. SBAs are a form of business intelligence and complement the highly quantitative analytics delivered by traditional BI products.
Search-based applications complement the ad hoc analysis and quantitative reporting typical of BI implementations. Where BI addresses the what questions, SBAs address the who, how, and why questions to give qualitative cause-and-effect explanations. They do this by collecting and co-displaying quantitative metrics and explanatory text in the same view. SBAs are also useful for extracting customer sentiment and other informational trends from the Internet -- a complex task beyond the capabilities of traditional BI.
By integrating semantic search-based applications with BI information sources (sometimes called the "Googlization of BI"), companies gain a broader understanding of their business activity that enables better business decisions to be made faster. Instead of using a single source of data as with traditional BI, SBAs can simultaneously access a wide variety of information sources while combining structured and unstructured data to provide a holistic, 360-degree view of the enterprise.
SBAs handle staggering amounts of data -- petabytes in some use cases -- while simultaneously providing Web-search-style, natural-language query interfaces that appeal to ordinary users. Today's workers, accustomed to fast and easy Google searches on the Web, can now gain the same easy-to-use tools to help them unlock information in the enterprise and gain insights for better decision making.
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