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Text Analytics Drives Content Management Value

This article is part of the Best Practices White Paper Text Analytics & Sentiment Analysis [June 2009]

How many times have you searched the Web and received a laundry list of 34,000 hits? Have you ever looked beyond the first page or two of search results? Probably not. Much like a search on the public Web, sifting through the vast amounts of unstructured data in your enterprise content management (ECM) system can be an overwhelming and fruitless process.

Most company data is unstructured, and much of it often resides in enterprise content management systems. If it is leveraged properly, this valuable information can help your company improve customer satisfaction, gain competitive advantage, make better decisions and improve the overall productivity of knowledge and other workers. But the sheer volume of data housed in these repositories can render the information useless if your company doesn’t have the tools to not only access this information, but also to analyze and extract relevant information.

Knowledge workers cannot effectively manually sift through the overwhelming amount of unstructured data. And it’s not realistic to expect them to recognize all the relationships between all the information they do read and other information they can access. But not fully utilizing this content, and failing to spot important relationships to other information, means its value isn’t fully leveraged. That’s where text analytics technology can help.

Text analytics automates the process of analyzing unstructured data and can help your company gain insight into what’s in your content management system. The advanced search capabilities of comprehensive ECM systems enable knowledge workers to access information residing in other sources, such as corporate file shares, email archives and other enterprise applications such as ERP and CRM systems—making the need for integration of text analytics into ECM platforms even greater.

What is Text Analytics?
Hurwitz & Associates defines text analytics technology as "the automated process of analyzing unstructured text, extracting relevant information, and transforming that information into structured information that can then be leveraged in different ways."1 The process uses "algorithms to analyze the unstructured text, extract this information and utilize this information as part of an index in the content management system."2 Hurwitz also states that text analytics "helps companies effectively analyze and maximize the value of the unstructured information in content management systems."3

Driving value from unstructured content: Companies are implementing text analytics to drive the most value from their content—wherever it resides—and are benefiting in the process.

Assessing customer satisfaction/market opinion: Most of the use cases available on text analytics focus on the issue of customer satisfaction or retention. Indeed, text analytics is ideal for this use. Solutions specifically focused on this use are often called voice of the customer (VoC) solutions. Through internal sources of unstructured data such as email, call center notes, Web forms, surveys and others—as well as external sources such as forums, blogs and public databases—companies can quickly develop a composite understanding of their customers’ thoughts and feelings about products and services by automatically analyzing the wealth of valuable customer-focused information that resides in numerous information sources.

Without text analytics, this data must be manually searched, tagged, analyzed and categorized—an inefficient, costly and time-consuming process with incomplete results. Text analytics technology and VoC applications automate this process, producing more reliable results more quickly, and enabling companies to unlock the full value of the information at their disposal.

For example, a company releasing a new product or feature can monitor external sites as well as customer communications to determine customers’ opinions of the feature or product. A retail company experiencing slow business at a specific location can use text analytics to identify issues at the site and address any problems revealed.

Making better decisions: Text analytics can enable companies to make better decisions across the organization—from product management and marketing to customer service and legal considerations.

For example, in addition to gleaning customer opinion on an existing product, text analytics can help to determine customer wants or needs during the development phase. Both internal data and public sources can inform which features are important to existing and potential customers. A marketing organization can direct a campaign at a specific set of customers who expressed concern about a perceived deficiency in a product. And legal departments can utilize text analytics for both e-discovery and compliance.

Gaining competitive advantage: Text analytics can have a hand in helping companies gain competitive advantage in several ways. For example, companies can monitor their overall reputation through analysis of external sources and internal feedback vehicles such as email, surveys and call center notes. Perceptions of their competitors can also be gleaned by analyzing the same sources.

Beyond public perception, the technology can also be used to monitor competitor activity—both through information gathered from customers and from external sources such as public message boards, blogs and news sources. This information can inform a company’s decisions on market direction, marketing and product development, providing a distinct competitive advantage.

Improving productivity: Without text analytics, your knowledge workers inefficiently spend time trying to locate and extract relevant information from content stored in the repository. Searching is either entirely fruitless or highly inefficient from a cost/benefit perspective. Whether knowledge workers want to proactively spot developing market trends or spot fraudulent activity on the trading floor, text analytics does the analysis for them, providing better results at a fraction of the manpower cost.

Industry-specific uses: In addition to these horizontal uses, companies are also using text analytics for industry-specific needs. For example, "insurance companies are using text analytics to help sift through massive amounts of insurance claim information in order to find patterns in the claims to help reduce fraud. Law enforcement is using text analytics to help detect criminal patterns."4 And life sciences companies are mining databases of articles and papers to identify promising new drugs.5

A leader in the enterprise content management market in this new technology area, EMC is the first to incorporate text analytics into its solution. Text analytics is a key capability of EMC CenterStage, a new client for EMC Documentum.

EMC CenterStage customers are utilizing the product’s text analytics capabilities to glean relevant information not only from the Documentum repository, but also from othercorporate data sources and external sources, which can be accessed by the federated search capabilities integrated into CenterStage Pro. CenterStage Pro gives knowledge workers advanced search and powerful text analytics integrated into their ECM systems, so they can efficiently leverage the full value of their information.

1 "Content Management Meets Text Analytics", Hurwitz & Associates by Dr. Fern Halper.
2 ibid
3 ibid
4 ibid
5 "Text Analytics—improving the use case for unstructured text," KMWorld magazine, John Harney, 2/1/09.

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