Page 261 -

260 Part Two Information Technology Infrastructure

of the major sources of big data that firms want to analyze. E-mail, memos,
call center transcripts, survey responses, legal cases, patent descriptions,
and service reports are all valuable for finding patterns and trends that will
help employees make better business decisions. Text mining tools are now
available to help businesses analyze these data. These tools are able to extract
key elements from unstructured big data sets, discover patterns and relation-
ships, and summarize the information.
Businesses might turn to text mining to analyze transcripts of calls to
customer service centers to identify major service and repair issues or to
measure customer sentiment about their company. Sentiment analysis
software is able to mine text comments in an e-mail message, blog, social media
conversation, or survey form to detect favorable and unfavorable opinions
about specific subjects.
For example, the discount broker Charles Schwab uses Attensity Analyze
software to analyze hundreds of thousands of its customer interactions each
month. The software analyzes Schwab’s customer service notes, e-mails,
survey responses, and online discussions to discover signs of dissatisfac-
tion that might cause a customer to stop using the company’s services.
Attensity is able to automatically identify the various “voices” customers
use to express their feedback (such as a positive, negative, or conditional
voice) to pinpoint a person’s intent to buy, intent to leave, or reaction to a
specific product or marketing message. Schwab uses this information to take
corrective actions such as stepping up direct broker communication with
the customer and trying to quickly resolve the problems that are making the
customer unhappy.
The Web is another rich source of unstructured big data for revealing
patterns, trends, and insights into customer behavior. The discovery and
analysis of useful patterns and information from the World Wide Web is
called Web mining. Businesses might turn to Web mining to help them
understand customer behavior, evaluate the effectiveness of a particular
Web site, or quantify the success of a marketing campaign. For instance,
marketers use the Google Trends and Google Insights for Search services,
which track the popularity of various words and phrases used in Google
search queries, to learn what people are interested in and what they are
interested in buying.
Web mining looks for patterns in data through content mining, structure
mining, and usage mining. Web content mining is the process of extracting
knowledge from the content of Web pages, which may include text, image,
audio, and video data. Web structure mining examines data related to the
structure of a particular Web site. For example, links pointing to a document
indicate the popularity of the document, while links coming out of a docu-
ment indicate the richness or perhaps the variety of topics covered in the
document. Web usage mining examines user interaction data recorded by a
Web server whenever requests for a Web site’s resources are received. The
usage data records the user’s behavior when the user browses or makes trans-
actions on the Web site and collects the data in a server log. Analyzing such
data can help companies determine the value of particular customers, cross
marketing strategies across products, and the effectiveness of promotional
campaigns.
The Interactive Session on Technology describes organizations’ experiences
as they use the analytical tools and business intelligence technologies we have
described to grapple with “big data” challenges.

MIS_13_Ch_06 Global.indd 260 1/17/2013 2:27:44 PM