mis mod 2

1. Data and Knowledge
Management

3. • The term Big Data to refer to the vast and
constantly increasing amounts of data that
modern organizations need to capture, store,
process, and analyze.
• Managing Big Data represents a very real
problem that every business faces.

4. • The following case provides a variety of
examples of companies that are utilizing Big
Data in creative and profitable ways.
• Big Data will continue to get “bigger,” so
organizations will have to devise ever-more
innovative solutions to manage these data.

5. Human Resources
• Caesars Entertainment (www.caesars.com), for
example, analyzes health insurance claim data
for its 65,000 employees and their covered
family members.
• Managers can track thousands of variables that
indicate how employees use medical services,
such as the number of emergency room visits
and whether employees choose a generic or
brand name drug.

6. • For instance, data revealed that too many employees
with medical emergencies were being treated at
hospital emergency rooms rather than at less-
expensive urgent-care facilities.
• The company launched a campaign to remind
employees of the high cost of emergency room visits
and they provided a list of alternative facilities.
• Subsequently, 10,000 emergencies shifted to less-
expensive alternatives, for a total savings of $4.5
million.

7. hiring.
• Catalyst IT Services (www .catalystitservices.com),
a technology outsourcing company that hires
teams for programming jobs.
• In 2013, the company planned to screen more
than 10,000 candidates.
• Not only is traditional recruiting too slow, but too
often the hiring managers subjectively choose
candidates who are not the best fit for the job.

8. • Catalyst addresses this problem by requiring
candidates fill out an online assessment.
• It then uses the assessment to collect
thousands of data points about each
candidate. In fact, the company collects more
data based on how candidates answer than on
what they answer.

9. Product Development
• Big Data can help capture customer
preferences and put that information to work
in designing new products.
• In this area, both online companies and
traditional companies are using Big Data to
achieve competitive advantage.

10. • Physical manufacturers are using Big Data to
measure customer interest. For example, as
Ford Motor Company (www.ford.com) was
designing the first subcompact model on its
new global platform
• One feature the company considered was a
“three blink” turn indicator that had been
available on its European cars for years.

11. Marketing
• Marketing managers have long used data to
better understand their customers and to
target their marketing efforts more directly.
Today, Big Data enables marketers to craft
much more personalized messages.

12. • Like many hoteliers, United Kingdom’s
InterContinental Hotels Group (IHG; www.ihg .com)
has gathered details about the 71 million members
of its Priority Club rewards program, such as income
levels and whether members prefer family-style or
business-traveler accommodations.
• The company then consolidated all of this
information into a single data warehouse that
extracts information from social media Web sites
and processes queries very quickly.

13. • Using its data warehouse and analytics
software, the hotelier launched a new
marketing campaign in January 2013.
• Where previous marketing campaigns
generated, on average, between 7 and 15
customized marketing messages, the new
campaign

14. • generated more than 1,500. IHG rolled out
these messages in stages to an initial core of
12 customer groups, each of which is defined
by 4,000 attributes.
• One group, for instance, tends to stay on
weekends, redeem reward points for gift
cards, and register through IHG marketing
partners.

15. • Utilizing this information, IHG sent these
customers a marketing message that alerted
them to local weekend events.
• The campaign proved to be highly successful.
It generated a 35 percent higher rate of
customer conversions, or acceptances, than
previous, similar campaigns.

16. What We Learned from This Case
• Information technologies and systems support
organizations in managing—that is, acquiring,
organizing, storing, accessing, analyzing, and
interpreting—data.
• As you noted in Chapter 1, when these data are
managed properly, they become information and
then knowledge.
• Information and knowledge are invaluable
organizational resources that can provide a
competitive advantage.

17. Managing data is critically important in
large organizations. However, it is equally
important to small organizations
• Example Rollins Automotive

18. Managing Data
• All IT applications require data.
• These data should be of
• high quality,
• meaning that they should be accurate,
• complete,
• timely,
• consistent,
• accessible, relevant, and concise.

19. The Difficulties of Managing Data
• Managing data in organizations is difficult for
many reasons.
• First, the amount of data increases exponentially
with time.
• Much historical data must be kept for a long
time, and new data are added rapidly.
• For example, to support millions of customers,
large retailers such as Walmart have to manage
petabytes of data.

20. • In addition, data are also scattered throughout
organizations, and they are collected by many
individuals using various methods and devices.
• These data are frequently stored in numerous
servers and locations and in different
computing systems, databases, formats, and
human and computer languages.

21. How New York City is benefiting
from the system it utilizes to manage its data management
problems

22. • Another problem is that data are generated from
multiple sources:
• internal sources (e.g., corporate databases and
company documents),
• personal sources (e.g., personal thoughts, opinions,
and experiences), and
• external sources (e.g., commercial databases,
government reports, and corporate Web sites).
• Data also come from the Web, in the form of
clickstream data.

23. Clickstream data
• Clickstream data are those data that visitors
and customers produce when they visit a Web
site and click on hyperlinks.
• Clickstream data provide a trail of the users’
activities in the Web site, including user
behavior and browsing patterns.

24. • Adding to these problems is the fact that new sources
of data, such as blogs, podcasts, videocasts, and radio
frequency identification (RFID) tags and other wireless
sensors, are constantly being developed.
• In addition, data degrade over time. For example,
customers move to new addresses or change their
names, companies go out of business or are bought,
new products are developed, employees are hired or
fired, and companies expand into new countries.

25. • Data are also subject to data rot. Data rot refers
primarily to problems with the media on which the
data are stored.
• Over time, temperature, humidity, and exposure to
light can cause physical problems with storage
media and thus make it diffi cult to access the data.
• The second aspect of data rot is that fi nding the
machines needed to access the data can be diffi
cult.

26. • Data security, quality, and integrity are critical,
yet they are easily jeopardized.
• In addition, legal requirements relating to data
differ among countries as well as among
industries, and they change frequently.

27. • Another problem arises from the fact that, over
time, organizations have developed information
systems for specific business processes, such as
transaction processing, supply chain management,
and customer relationship management.
• Information systems that specifically support
these processes impose unique requirements on
data, which results in repetition and conflicts
across the organization.

28. • For example, the marketing function might
maintain information on customers, sales
territories, and markets.
• These data might be duplicated within the
billing or customer service functions.
• This situation can produce inconsistent data
within the enterprise.

29. • There are two additional problems with data
management: Big Data and data hoarding.

30. Data Governance
• To address the numerous problems associated
with managing data, organizations are turning to
data governance.
• Data governance is an approach to managing
information across an entire organization.
• It involves a formal set of business processes and
policies that are designed to ensure that data are
handled in a certain, well-defined fashion.
• That is, the organization follows unambiguous
rules for creating, collecting, handling, and
protecting its information.

31. • The objective is to make information available,
transparent, and useful for the people who
are authorized to access it, from the moment
it enters an organization until it is outdated
and deleted.

32. • One strategy for implementing data
governance is master data management.
• Master data management is a process that
spans all organizational business processes
and applications.

33. Master Data
• It provides companies with the ability to store,
maintain, exchange, and synchronize a
consistent, accurate, and timely “single version
of the truth” for the company’s master data.
• Master data are a set of core data, such as
customer, product, employee, vendor,
geographic location, and so on, that span the
enterprise information systems.

34. Master data vs Transaction data
• Transaction data, which are generated and
captured by operational systems, describe the
business’s activities, or transactions.
• In contrast, master data are applied to
multiple transactions and are used to
categorize, aggregate, and evaluate the
transaction data.

35. • Let’s look at an example of a transaction: You
(Mary Jones) purchase one Samsung 42-inch
plasma television, part number 1234, from Bill
Roberts at Best Buy, for $2,000, on April 20, 2013.
In this example, the master data are “product
sold,” “vendor,” “salesperson,” “store,” “part
number,” “purchase price,” and “date.”
• When specific values are applied to the master
data, then a transaction is represented. Therefore,
transaction data would be, respectively, “42-inch
plasma television,” “Samsung,” “Best Buy,” “Bill
Roberts,” “1234,” “$2,000,” and “April 20, 2013.”