TDWI Checklist Report Helps Enterprises Get Started with Text Analytics
New report helps enterprises understand how text analytics is used, the benefits of the technology, and how to introduce it into their environment
SEATTLE, WA, September 24, 2013—TDWI Research has released its newest Checklist Report: How to Gain Insight from Text. The report examines how text analytics is used in today’s enterprises and offers 10 ways organizations can introduce the technology into their own environment.
From medical research and fraud detection to understanding warranty activity, text analytics is providing organizations valuable insight by helping them answer why and what questions. For example, what is causing a rise in service calls or why are customers churning? Text analytics can also aid in discovery and improving the lift or accuracy of analytical models—which impacts both top- and bottom-line results, according to Fern Halper, author of the report.
Our checklist report focuses on helping organizations understand how to get started with text analytics. We go beyond the basics to look at how text analytics can provide structure to unstructured data, how to think through a text analytics program, and what resources are needed to maximize the value of your text,” Halper says.
The report looks at nine steps to consider when undertaking a text analytics project. Halper explains how to look for top-line and bottom-line improvements, lists five considerations for gaining access to and preparing the data you need to analyze, and points out how enterprises can identify the data they must extract from a variety of data sources. She also looks at the importance of iterating, tuning, and testing your analysis.
In addition, the report:
- Explains the role of visualization in text exploration and how to identify the most important terms and text, draw relationships between terms and entities, and how to interact with text data visually
- Discusses the nuances of sentiment analysis, from understanding opinions to parsing the nuances of language (such as mood and sarcasm)
- Reveals how text data in discovery can be utilized in advanced ways, from marrying structured and unstructured data to predictive analytics using text alone
- Examines the skills needed to perform text analytics and how they are similar to general analytics skills
- What features to look for in text analytics solutions, from taxonomies and lists to speed and scalability
A Webinar will be held October 1 featuring a panel discussion with Halper and representatives from the report sponsors.
This research was sponsored by Angoss, Lexalytics, and SAS.
For the Media:
For a complete copy of the report or to ask questions of the author, members of the press should contact Fern Halper at email@example.com.
The report is freely downloadable by the public; a short registration is required for those downloading a TDWI report for the first time.
About the Author
Fern Halper is director of TDWI Research for advanced analytics, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and other “big data” analytics approaches. She has more than 20 years of experience in data and business analysis, and has published numerous articles on data mining and information technology. Halper is co-author of "Dummies" books on cloud computing, hybrid cloud, service-oriented architecture, and service management, and big data. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Laboratories. Her Ph.D. is from Texas A&M University. You can reach her at firstname.lastname@example.org, or follow her on Twitter: @fhalper.
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Fern Halper, TDWI