Slideshow Expert Insight on Unstructured Content

Published
  • June 13 2012, 10:41pm EDT

Consider the Cost

“One of the things we have to do is to analyze and use [unstructured data] effectively. If we store it and don’t use it, it’s a capital waste, a tremendous amount of effort just to capture that information. It’s hard, and there are so many sources, both structured and unstructured. But I think this focus on big data is an absolute competitive differentiator. In my view, it’s nowhere near overhyped. It’s underappreciated in terms of how significant the impact it’s going to have across all businesses, certainly in the capital markets.” – William Adiletta, Capco consultant

Text Ain’t Just for Cell Phones

“Text analytics provide some of the core processing capabilities that are needed to evaluate and govern unstructured data. Natural language processing , linguistic rules and statistical models can be used to decipher the meaning of words and phrases contained in electronic text – the subjects of the text, the topical areas covered in the materials, the concepts, entities and their relationships. Once defined, the resultant models can then be applied to content that has not yet been examined, thus automatically generating the metadata from the content itself.” Fiona McNeill and Mark Troester, “Managing the Unmanageable: Text Analytics for Unstructured Information Management”

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Use More than the Bare Bones BI

“Companies that failed to achieve high levels of ROI [with unstructured and big data] typically had not taken advantage of the collaborative, contextual and predictive benefits of big data. Instead, they were simply automating big data processing for standard reports and workflows. None of these companies [that failed to see ROI] had spread the use of big data to an enterprise-wide level or done any significant work to align the collection of big data with specific revenue-producing activities.” Hyoun Park, analyst Nucleus Research, “Where Big Data Shows Huge Returns”

Don’t Fear Cutting-Edge Solutions

A recent report from non-profit industry group AIIM indicated slim enterprise interest in emerging data parsing tools like Hadoop, NoSQL and MapReduce. “For these extensions of conventional reporting, complexity and scope play a part, as well as the size of the data to be analyzed. The ability to scale the data yet produce query results within reasonable times may lead on from technical differences between conventional data warehouses, and some of the more recent database technologies.” Doug Miles, AIIM researcher, “Big Data Reality Check”

Add Some Science to Your Business Data

The digging capabilities displayed by so-called data scientists could unearth business data gems. Analyst/blogger Steve Miller writes: “I've proposed that both data science and BI share underpinnings of business, technology and statistical science. According to data scientist Drew Conway: ‘First, one must have hacking skills …(which) in this context mean proficiency working with large, unstructured chunks of electronic data … Second, one needs a basic understanding of mathematics and statistics … Finally, and perhaps most importantly, a data scientist must have some substantive expertise in the data being analyzed.’”

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