Use cases for unstructured data and content analytics software vary widely, with many only now emerging and additional ones envisioned for the future.
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Marketers are looking to use sentiment analytics to better understand how customers feel about products, for example. Insurers are looking to detect fraud. Lawyers use text analytics for e-discovery purposes, while the medical and life sciences industries for the most part are just starting to experiment with the technology.
In short, "there are lots of ways of analyzing text," said Sue Feldman, an analyst with Framingham, Mass.-based IDC. And that means lots of different text and unstructured data analytics tools.
Just as the use cases vary, content analytics tools vary in how they ingest, process, analyze and present text and other unstructured data, Feldman said. It's important for companies considering investments in content analytics to understand the differences between the available tools and pick the ones best suited to their particular needs.
Unstructured data analytics tools:
specialists vs. generalists
"Like any kind of IT area, there are specialized tools and then there are general-purpose tools," said Seth Grimes, founder of consulting firm Alta Plana Corp. in Takoma Park, Md.
Grimes, who has been following text and other forms of content analytics for more than 10 years, said most of what he calls general-purpose tools are offered by the big IT vendors. Those products give users a lot of flexibility while requiring them to create their own lexicons and language rules, he said.
According to Grimes, one of the benefits of general-purpose unstructured data analytics tools is that they potentially can be applied to new uses, some that may not even be evident yet. But it does require a certain level of sophistication from the user to tune and model the tools, he said. For example, users will often have to write their own language rules and insert their own language libraries.
Other text analytics tools, many from smaller and more specialized vendors, are designed with a narrower focus than the general-purpose tools. "There are any number of routes to market," Grimes said. "And there are solutions designed for particular business problems and solutions that are tailored to specific business domains."
For example, some text analytics tools are better at determining and visualizing customer sentiment than others. Such a tool could be a good fit for a marketer looking to identify trends in how a corporate brand or product is perceived. But the same tool wouldn't do an insurer looking to identify fraudulent accident claims much good.
"Think about it in terms of feature sets," said Curt Monash, founder and lead analyst at Monash Research in Acton, Mass. "There are certain applications where sentiment analysis is important and other occasions where it's less important."
Putting an industry spin on unstructured data analytics
Other content analytics tools are built to appeal to specific industries. In these cases, the tools are often designed to understand an industry's unique language. Financial traders, for example, use different speech patterns and lexicons than pharmaceutical researchers do. As such, trading firms would want to find a tool with a data dictionary designed to understand their terminology.
The various tools also aren't all equal when it comes to the specific type and sources of content they can process, said James Kobielus, an analyst at Cambridge, Mass.-based Forrester Research Inc.
Depending on the vendor, different tools may be incompatible with competing vendors' data sources and file formats, Kobielus said. Some tools are designed specifically to analyze Facebook posts and tweets, while others are less adept at doing social media analytics. In addition, it's important to consider the types of prebuilt data connectors the tools offer and see if they match your needs.
Language support is also an important issue, particularly for international organizations and companies that do significant business overseas, said Gartner Inc. analyst Gareth Herschel. He noted that some tools can only process and analyze written English, while others are able to handle some foreign languages as well.
Customization capabilities also should be taken into account during the software evaluation process, Feldman recommended. "When a company goes shopping for a text analytics tool, it's a good idea to see how easy it is to inject their own terminology into the application," she said.
ABOUT THE AUTHOR
Jeff Kelly is a freelance writer.