Managing and Using Information Systems Discussion Please find the instructions below and follow them to complete this discussion. Please find the chapter 1

Managing and Using Information Systems Discussion Please find the instructions below and follow them to complete this discussion. Please find the chapter 12 attached to complete this discussion.In Chapter 12 of the text – Managing & Using Information Systems: A Strategic Approach, we discuss the use of knowledge management in IT in business.Using the lessons learned in Chapter 12 of the text and research about knowledge management, devise a process to retain the knowledge of your organizations’ IT department. How would you retain the documentation and the IT personnel’s knowledge about the different IT products and services so they can be kept, organized, and easily searched in the future? You must have 1 scholarly journal articles along with your text to support your analysis. Try to find articles that are less than 5 years old. Initial posts must be 300 words but no more than 500 words. Responses to classmates 200 words each (2 required).Please make sure you cite and support your posts. Please include a reference for each citation. Your post should use APA formatting. chapter
12
Business Intelligence,
Knowledge Management
and Analytics
Business intelligence and analytics have become a source of strategic advantage for those
firms who understand and develop skills to manage big data. This chapter provides an
overview of the ways businesses make decisions. Making better decisions begins by
understanding how to build capabilities in knowledge management, business intelligence,
and analytics and how to protect an organization’s intellectual property. Data, information,
and knowledge (both tacit and explicit) are then defined and discussed because they compose the foundation of making better decisions. Knowledge is managed through four main
processes, which are outlined next. A discussion of competing with analytics, and the capabilities that enable it, follows. The chapter then takes a more technical turn, addressing the
components of business analytics and big data amassed in data warehouses. The chapter
concludes with a discussion of the Internet of Things, social media analytics, and caveats
that managers must anticipate.
Netflix knew House of Cards would be a blockbuster before it aired the first episode.’ Using data
from its 33 million customers worldwide, Netflix data scientists had their own internal data source
of viewing customer preferences, and analysis indicated that using director David Fincher, starring
Kevin Spacey, and basing the show on the British series House of Cards would be a success. The
scientists identified patterns in the data that gave them support for a decision to create this new
series. For example, they found that Netflix had a very large audience who watched the British version of House of Cards and watched films starring Kevin Spacey and directed by David Fincher. By
“running the numbers,” execs knew this new show would appeal to a very large group of people and
that it would be a hit before the filming even started.
Netflix has a competitive advantage because of its big data and analytics investment-the
company knows not only what is watched on its site by all of its customers but also much more
information. For example, the company knows when someone pauses, rewinds, or fast forwards;
what is being searched for and what is chosen from the search results; what device is used to watch
the program; and when the viewer leaves the content and whether he or she ever comes back. Analytics data can be valuable from these data. Analysis shows that the analytics results differ significantly from the results obtained by convening focus groups, and it turns out the analytics algorithms
give better direction for a more successful outcome. Netflix’s data-driven culture extends not only to
decisions about original content but many other major decisions such as what films to license, what
shows to recommend to customers, and what colors and images to use on their site.
‘ Adapted from “Giving Viewers What They Want,” The New York Times (February 24, 2013), http://www.nytimes.com/2013/02/25/
business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html (accessed September 5, 2015); “Big Data Lessons
from Netflix” (March 11, 2014), http://www.wired.com/2014/03/big-data-lessons-netflix/ (accessed September 5, 2015); “What
Netflix’s ‘House of Cards’ Means for the Future of TV” (March 4, 2013), http://www.forbes.com/sites/gregsatell/2013/03/04/whatnetflixs-house-of-cards-means-for-the-future-of-tv/ (accessed September 5, 2015).
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~
Competing with Business Analytics
mJI
Enterprises have long sought a way to harness the value locked inside the extensive data they collect and store
about customers, markets, competitors, products, people, and processes. In today’s business environment, external
data sources and real-time data flows add opportunities for insight that might otherwise be missed. Algorithms and
analytics programs are the way this value is unlocked and used to describe, predict, and prescribe future activity.
Managers use these insights to make better decisions in virtually every corner of their business from marketing and
customer management to supply chains, risk management, hiring practices, and research and development activities. Moving forward, the amount of data available to analyze will continue to explode, especially with the growth
of the Internet of Things, fueled by rapid growth of smart devices connected to the Web. This chapter describes how
organizations compete with analytics, then addresses basic concepts of knowledge management, and reviews the
current thinking about business intelligence, business analytics, big data, and intellectual property.
Competing with Business Analytics
In recent years, many companies have found success competing through better use of analytics. Companies such
as Netflix as described at the beginning of this chapter have used analytics to improve on their otherwise lackluster
business to become industry leaders. Caesars Entertainment, the largest gaming company in the world by some
measures, found a way to more than double revenues by collecting and analyzing customer data. Capital One has
also emerged from a crowded field of financial services firms to become one of the industry’s leaders through the
use of extensive business analytics. Those analytics enable Capital One to continuously create new products and
services that appeal to new customers and to reinvigorate relationships with existing customers. The bank was
founded on the idea that by mining data about individual customers it could create financial products that addressed
what the big players would consider “niche markets.” Although these markets were unattractive to the large players
because of the smaller number of potential customers, the niche markets were profitable. Using the customer
‘database of a small bank and running numerous analytical tests, Capital One identified characteristics that would
– . , create a profitable service. It learned, for example, that the most profitable customers were ones who charged a large
amount but paid their credit cards off slowly. At the time, most credit cards companies did not differentiate between
these and other customers. Capital One’s innovative idea was to create a product that catered to these customers.
Today, Capital One runs hundreds of experiments to identify new products that target individual customers. Using
analytics to simulate and test is a very low-cost way to design and develop these products. 2
Sports teams have propelled themselves to league success through business analytics. The systematic use of
factual data in proprietary models is credited with helping the Oakland As and the Boston Red Sox. As seen in
the movie, Moneyball, Billy Beane was one of the first general managers in Major League Baseball to build his
organization, the Oakland As, around analytics. Although this industry collected data extensively, it was mostly
used to manage the game in process. The Oakland As used data on things that it could measure such as the on-base
percentage (the number of times a player gets on base) instead of softer criteria such as estimating the effort the
player is willing to put in. The Oakland As used analytics in its recruiting efforts to predict which young players
had the best chances of becoming major league players and hired players that other teams overlooked at salaries
that were much more affordable. This strategy paid off, consistently carrying the Oakland As to the playoffs despite
a budget for player’s salaries that was a fraction of what some of its competitors had.
One reason for the rise in companies competing on analytics is that numerous companies in many industries
offer similar products and use comparable technologies. Therefore, business processes are among the last remaining points of differentiation, and analytic competitors are wringing every last drop of value from those processes. 3
Business analytics fuel fact-based decision making. For example, a company may use simple inventory reports
to figure out what products are selling quickly and which are moving slowly, but a company that uses analytics
also knows who buys them, what price each customer pays, how many items the customer will likely purchase in
a lifetime, what motivates each customer to purchase, and which incentives to offer to increase the revenue from
each sale.
j,
..,.
2
3
Thomas Davenport and Jeanne Harris, Competing on Analytics (Boston, MA: Harvard Business School Press, 2007), 41-42 .
Ibid.
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Business Intelligence. Knowledge Management. and Analytics
According to a study by consulting firm McKinsey and Company, there are five ways big data and analytics can
help an organization: 4
1. Making information more transparent and usable at a frequency that outpaces the competition
2. Exposing variability and boosting performance by collecting and analyzing more transactional and
performance data
3. More precisely tailoring products and services using better-designed segmentation and large data samples
4. Improving decision making through experiments, forecasting and feedback, and just-in-time analysis
5. Developing the next generation of products and services more quickly using sensor data to collect aftersales information on product usage, performance, and so on.
Knowledge Management. Business Intelligence.
and Business Analytics
It’s all about making better decisions. Before the terms “big data” and “analytics” were all the rage, managers
talked about knowledge management. Managing knowledge is not a new concept, 5 but it has been invigorated by
new technologies for collaborative systems, the emergence of the Internet and intranets-which in themselves act
as a large, geographically distributed knowledge repository-and the well-publicized successes of companies like
Netflix that use business analytics. The discipline draws from many established sources, including anthropology,
cognitive psychology, management, sociology, artificial intelligence, information technology (IT), and library science. Knowledge management remains, however, an emerging discipline with few generally accepted standards or
definitions of key concepts.
Knowledge management includes the processes necessary to generate, capture, codify, integrate, and
transfer knowledge across the organization to achieve competitive advantage. Individuals are the ultimate source
of organizational knowledge. The organization gains only limited benefit from knowledge isolated within individuals or among workgroups; to obtain the full value of knowledge, it must be captured and transferred across
the organization.
Business intelligence can be considered a component of knowledge management. Business intelligence (Bl)
is the term used to describe the set of technologies and processes that use data to understand and analyze business
performance. 6 It is the management strategy used to create a more structured approach to decision making based on
facts that are discovered by analyzing information collected in company databases. While knowledge management
includes the processes necessary to capture, codify, integrate, and make sense of all types of knowledge as
described earlier, business intelligence is more specifically about extracting knowledge from data. Davenport and
Harris suggest that business analytics is the term used to refer to the use of quantitative and predictive models,
algorithms, and evidence-based management to drive decisions. 7 By this definition, business analytics is a subset
of BI. Some, however, use the terms BI and analytics interchangeably.
The most profound aspect of knowledge management and business intelligence is that an organization’s sustainable competitive advantage ultimately lies in what its employees know and how they apply that knowledge to
business problems. Exaggerated promises and heightened expectations couched in the hyperbole of technology vendors and consultants may create unrealistic expectations. Knowledge management is not a silver bullet, however,
because it cannot solve all business problems. Knowledge must serve the broader goals of the organization, and
4
James Manyika, Michael Chui, Brad Brown, Jacques Bughin. Richard Dobbs. Charles Roxburgh, and Angela Hung Byers, “Big Data: The Next
Frontier for innovation, competition, and productivity,” May 2011, http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_
for_innovation (accessed September 5, 2015).
‘ The cuneiform texts found at the ancient city Ebia (Tall Mardikh) in Syria are, at more than 4,000 years old, some of the earliest known attempts to
record and organize information.
6
Davenport and Harris, Competing on Analytics, 7.
7
Ibid.
Data. Information. and Knowledge
ml
analytics alone do not create competitive advantage. How the information is used and how the knowledge is linked
back to business processes are important components of knowledge management.
Data. Information. and Knowledge
The terms data, information, and knowledge are often used interchangeably but have significant and discrete meanings within the knowledge management domain. As was first presented in the Introduction of this textbook, there
are differences (see Figure 12.1). Data are specific, objective facts or observations, such as “distributor ABC
bought 600 of our sweaters.” Standing alone, such facts have limited intrinsic meaning. But key features of data are
that it can be easily captured, transmitted, and stored electronically.
Information is defined by Peter Drucker as “data endowed with relevance and purpose.” 8 People tum data into
information in different ways. One way is by organizing them into some unit of analysis (e.g., dollars, dates, or
customers), which helps interpret the data by giving it context. Another way is by combining related data to create
relevance. For example, a customer’s data such as name or address become information when combined with the
average order size as well as orders from that customer over time because at that point, the combined facts give a
different meaning than the individual facts alone. Extending the ABC example, knowing that an average distributor
buys 800 sweaters annually provides more than just the data about ABC’s purchase of 600 this year. Also, knowing
that ABC bought 400 sweaters last year, and 200 sweaters the year before starts to indicate much more than just
the current data alone.
Knowledge is a mix of contextual information, experiences, rules, and values. It is richer and deeper than
information and more valuable because someone has thought deeply about that information and added his or her
own unique experience, judgment, and wisdom. Continuing with the sweater example, the sales manager might
know more about distributor ABC and therefore have some additional information or experiences that add to the
information. The manager knows that this is a new distributor, one with a strategy to add additional retail outlets
each year. Then the information put in a richer context indicates something very different than just the sales numbers alone. The sales manager knows that his or her company has an opportunity to grow as the distributor grows.
Values and beliefs are also a component of knowledge; they determine the interpretation and the organization of
knowledge. Tom Davenport and Larry Prusak, experts who have written about this relationship, say, “The power of
knowledge to organize, select, learn, and judge comes from values and beliefs as much as, and probably more than,
from information and logic.” 9 Knowledge also involves the synthesis of multiple sources of information over time. 10
Data
Definition
Information
! Knowledge
Simple observations of the
; state of the world
Data endowed with
relevance and purpose
: Information from the human mind
: (includes reflection, synthesis, context)
• Easily structured
• Easily captured on
machines
• Often quantified
• Easily transferred
I • Mere facts presented
• Unit of analysis required : • Hard to structure
• Data that have been
: • Difficult to capture on machines
processed
• Often tacit
• Human mediation
• Hard to transfer
necessary
t———t————+———–·–,—-
Characteristics
Example
Daily inventory report of
all inventory items sent
to the CEO of a large
manufacturing company
Daily inventory report
of items that are below
economic order quantity
levels sent to inventory
manager
Inventory manager knowing which
items need to be reordered in light of
daily inventory report, anticipated labor
strikes, and a flood in Brazil that affects
the supply of a major component.
FIGURE 12.1
The relationships between data. information. and knowledge.
Source: Adapted from Thomas Davenport, Information Ecology (New York: Oxford University Press, 1997).
8
9

0
Peter F. Drucker. ‘The Coming of the New Organization” (January-February 1988), 45-53.
Thomas H. Davenport and Laurence Prusak. Working Knowledge (Boston, MA: Harvard Business School Press, 1998), 12.
Thomas H. Davenport, Information Ecology (New York: Oxford University Press, 1997). 9-10.
flfJ
Business Intelligence. Knowledge Management. and Analytics
Value
Sources of Value
Sharing of best practices
• Avoid reinventing the wheel
• Build on valuable work and expertise
Sustainable competitive advantage
•
•
•
•
•
•
•
•
•
•
——-··–Managing overload
Rapid change
Embedded knowledge from products
•
•
Globalization
i

Shorten the life cycle of innovation
Promote view of an “infinite resource” that isn’t used up
Impact bottom-line returns
Filter data to assimilate relevant knowledge into the company
Provide organization and storage for easier data retrieval
Build on previous work to make company more agile
Streamline processes/build dynamic processes
Sense and respond to changes more quickly
Customize preexisting solutions for unique customer needs
·-
Use smart products to gather product information automatically to
refine products, provide maintenance, add upgrades and identify
customer usage.
Blur distinction between manufacturing and service firms when
information systems are embedded in products
Add value through intangibles such as fixing systems before customers
know they’re broken
•
Decrease cycle times for global processes because information moves
faster than physical process components
• Manage global competitive pressures
Provide global access to knowledge
• Adapt to local conditions
•
Insurance for downsizing
•
•
•
Protect against loss of knowledge when workers leave
Provide portability for workers who move between roles
Reduce time for knowledge acquisition
FIGURE 12.2 The value of managing knowledge.
The amount of human contribution increases along the continuum from data to information to knowledge. Computers work well for managing data but are less efficient at managing information. The more complex and ill-defined
elements of knowledge (for example, “tacit” knowledge described in the next section) are difficult if not impossible
to capture electronically.
Although knowledge has always been important to the success of an organization, it was presumed that the
natural, informal flow of knowledge was sufficient to meet organizational needs. But managing knowledge has
become far more complex, the amount of knowledge to manage far greater than ever, and the tools to manage
knowledge far more powerful. Managing knowledge provides value to organizations in several ways as summarized in Figure 12.2.
Tacit versus Explicit Knowledge
Knowledge can be further classified into two types: tacit and explicit. Tacit knowledge was first described by philosopher Michael Polanyi in his book, The Tacit Dimension with the classic assertion that “We can know more than we
can tell.” 11 For example, try writing, or explaining verbally, how to swim or ride a bicycle. Describe the color aqua
to someone who cannot see or the sound made by a piano to someone wh…
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