Module 02: Critical Thinking
Improving Data Governance
Corporations are increasingly moving their data to the cloud. Select an organization, national or international, that has used or should consider using cloud technology. Then, address the following requirements:
1- Explain details (e.g., mission, vision, values, industry) about the organization that you selected.
2- Outline some of the advantages and disadvantages with your selected company using the cloud.
3- Explain industry practices. Do other organizations in your selected organization’s industry use the cloud? Why or why not?
4- What are some of the factors that must be considered before any organization uses the cloud?
Required
· Chapter 2 in Information Technology for Management: On-Demand Strategies for Performance, Growth, and Sustainability
· Al-Ruithe, M., & Benkhelifa, E. (2020).
Determining the enabling factors for implementing cloud data governance in the Saudi public sector by structural equation modelling.
Future Generation Computer Systems, 107, 1061–1076.
https://doi.org/10.1016/j.future.2017.12.057
· Elzein, I. A., & Kurdi, M. (2019).
Analyzing the Challenges of Security Threats and Personal information in Mobile Cloud Computing Infrastructure.
2019 International Conference on Digitization (ICD), Digitization (ICD), 2019 International Conference On, 202–206.
Recommended:
Module 02 PowerPoint slides
Meet the following requirements:
2
5
CHAPTER
2
Information Systems,
IT Architecture, Data Governance,
and Cloud Computing
L E A R N I N G O B J E C T I V E S
2.1 Name the six components of an information system and
match the various types of information systems to the type of
support needed by business operations and decision-makers.
2.2 Describe an IT infrastructure, an IT architecture, and an
enterprisewide architecture (EA) and compare and contrast
their roles in guiding IT growth and sustaining long-term
performance.
2.3 Explain the business benefits of information management and
understand the importance of data governance and master
data management in providing trusted data that is available
when and where needed to support sustainability.
2.4 Understand the concepts of data centers and cloud computing
and understand how they add value in an organization.
2.5 Describe the different types of cloud services and the various
forms of virtualization and understand how they add value in
an organization.
C H A P T E R O U T L I N E
Case 2.1 Opening Case: Detoxing Location-Based
Advertising Data at MEDIATA
2.1 IS Concepts and Classification
s
2.2 IT Infrastructure, IT Architecture, and
Enterprise Architecture
2.3 Information Management and Data
Governance
2.4 Data Centers and Cloud Computing
2.5 Cloud Services and Virtualization
Case 2.2 Business Case: Data Chaos Creates Risk
Case 2.3 Video Case: Cloud Computing at
Coca-Cola Is Changing Everything
26 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
Introduction
One of the most popular business strategies for achieving success is the development of a
competitive advantage. Competitive advantage exists when a company has superior resources
and capabilities than its competitors that allow it to achieve either a lower cost structure
or a differentiated product. For long-term business success, companies strive to develop
sustainable competitive advantages, or competitive advantages that cannot be easily copied
by the competition (Porter, 1998). To stay ahead, corporate leaders must constantly seek new
ways to grow their business in the face of rapid technology changes, increasingly empowered
consumers and employees, and ongoing changes in government regulation. Effective ways
to thrive over the long term are to launch new business models and strategies or devise new
ways to outperform competitors. Because these new business models, strategies, and per-
formance capabilities will frequently be the result of advances in technology, the company’s
ability to leverage technological innovation over time will depend on its approach to enter-
prise IT architecture, information management, and data governance. The enterprisewide IT
architecture, or simply the enterprise architecture (EA), guides the evolution, expansion, and
integration of information systems (ISs), digital technology, and business processes. This guid-
ance enables companies to more effectively leverage their IT capability to achieve maximum
competitive advantage and growth over the long term. Information management guides the
acquisition, custodianship, and distribution of corporate data and involves the management
of data systems, technology, processes, and corporate strategy. Data governance, or informa-
tion governance, controls enterprise data through formal policies and procedures. One goal of
data governance is to provide employees and business partners with high-quality data they
can trust and access on demand.
Bad decisions can result from the analysis of inaccurate data, which is widely referred to
as dirty data, and lead to increased costs, decreased revenue, and legal, reputational, and
performance-related consequences. For example, if data is collected and analyzed based on
inaccurate information because advertising was conducted in the wrong location for the
wrong audience, marketing campaigns can become highly skewed and ineffective. Com-
panies must then begin costly repairs to their datasets to correct the problems caused by
dirty data. This creates a drop in customer satisfaction and a misuse of resources in a firm.
One example of an organization taking strides to clean the dirty data collected through inac-
curate marketing is the data management platform, MEDIATA, which runs bidding systems
and ad location services for firms looking to run ads on websites (see Table 2.1). Let’s see
how they did this.
Dirty data are data of such poor
quality that they cannot be trusted
or relied upon for decisions.
Introduction 27
Case 2.1 Opening Case
DIRTY DATA
AHEAD
C
ou
rt
es
y
of
B
ill
y
R
a
y
Detoxing Location-Based Advertising Data
at MEDIATA
Company Overview
MEDIATA uses its audience and media delivery platform to deliver
thousands of successful online advertising campaigns across Australia,
Hong Kong, and New Zealand. Known as a “programmatic solution
specialist,” the MEDIATA platform is truly cutting-edge. It runs bidding
systems and ad location services for companies that are looking to run
ads on websites and provides its clients with high-impact, fully man-
aged, 100% transparent advertising campaigns that produce results.
MEDIATA is committed to shaking up the online advertising industry
and is evolving into a fast-growing international business. MEDIATA
clients include Qantas, LG, Virgin Money, Konica Minolta, Optus, Carls-
berg, Honda, ACCOR Hotels, Air New Zealand, Heinz, Woolworths, Citi-
bank, and JP Morgan.
The Problem
MEDIATA uses IP address data to locate customers and ad effectiveness.
Unfortunately, as much as 80% of ad inventories come with an incor-
rect location and MEDIATA realized that this “dirty data” was adversely
affecting their business. Location-based advertising provides organi-
zations and companies alike with massive benefits. Target customers
can be reached easily and effectively through marketing campaigns
tailored specifically for them. For example, utility companies and
internet service providers usually have certain areas or regions that
they service. Using location-based targeting (see Figure 2.1), these
companies can target television, newspaper, and online display ads to
attract new customers. Another benefit includes the reduced waste of
running marketing campaigns in unprofitable areas. Firms can choose
precisely where their advertisements are displayed without wasting
resources on customer segments that will not respond because of
location or preference discrepancies.
Advanced data analytics in location-based advertising also allows
companies like MEDIATA to reach customers where and when they are
in decision-making mode using programmatic bidding algorithms and
ad inventories. Browser-based ads use these algorithms to predict
which customer segments will click on certain ads at certain times of
the day. Automated bidding then ensues, with the ad spot on the page
going to the highest bidder (Cailean, 2016). However, the data must
be accurate to be useful and MEDIATA realized that their data could be
much better than it was. Given the importance of this technology to
advertisers and digital advertising agencies, there are overwhelming
issues to overcome.
The issues stem from outdated methods of locating Internet users
through IP addresses. These old systems do not pinpoint where exactly
traffic is coming from, rather they give advertising agencies broad geo-
graphic regions to work with, and the ads go to random coordinates
within the regions. Since the value of these activities comes from having
accurate targeting, the inaccuracies of the antiquated systems severely
impact profitability. As targeting regions shrink, information becomes
more valuable and accurate, but even small inaccuracies dilute the value
of demographic information applied to an audience.
The Solution
In 2016, MEDIATA established a data governance program in which it
partnered with Skyhook, a U.S. global location software company to
TA B L E 2 . 1 Opening Case Overview
Company MEDIATA was launched as Valued Interactive
Media (VIM) in 2009. Rebranded in 2013 as
MEDIATA
Industry Communications; Advertising
Product Lines Wide range of programmatic solutions and
products to provide practical solutions for
digital marketing campaigns to deliver
successful online advertising campaigns to
organizations across Australia, Hong Kong,
and New Zealand
Digital Technology Information management and data
governance to increase trust and
accessibility of data to facilitate a
company’s vision
Business Vision Shake up the online advertising industry.
Improve transparency and foster greater
cooperation between partners
Website www.mediataplatform.com
28 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
FIGURE 2.1 Location-based advertising.
2.1 IS Concepts and Classification
Before we being to explore the value of information systems (ISs) to an organization, it’s use-
ful to understand what an IS is, what it does, and what types of ISs are typically found at differ-
ent levels of an organization.
In addition to supporting decision-making, coordination, and control in an organization,
ISs also help managers and workers analyze problems, visualize complex sets of data, and cre-
ate new products. ISs collect (input) and manipulate data (process), and generate and dis-
tribute reports (output) and based on the data-specific IT services, such as processing customer
orders and generating payroll, are delivered to the organization. Finally, the ISs save (store) the
data for future use. In addition to the four functions of IPOS, an information needs feedback
from its users and other stakeholders to help improve future systems as demonstrated in
Figure 2.2.
The following example demonstrates how the components of the IPOS work together: To
access a website, Amanda opens an Internet browser using the keyboard and enters a Web
address into the browser (input). The system then uses that information to find the correct web-
site (processing) and the content of the desired site is displayed in the Web browser (output).
Next, Amanda bookmarks the desired website in the Web browser for future use (storage).
The system then records the time that it took to produce the output to compare actual versus
expected performance (feedback).
Information systems (ISs) is
a combination of information
technology and people’s activities
using the technology to support
business processes, operations,
management, and decision-
making at different levels of the
organization.
IPOS is the cycle of inputting,
processing, outputting, and
storing information in an
information system.
improve the effectiveness of MEDIATA’s user profile data by more pre-
cisely locating IP addresses resolving MEDIATA’s challenges related to
dirty data. Skyhook’s Context Accelerator Hyperlocal IP uses big data
analytics to provide over 1 billion IP addresses to advertising platforms
and cleaned MEDIATA’s dirty data to pinpoint customers within 100
meters, thus increasing ad effectiveness for its clients. Hyperlocal IP
achieves this by using big data analytics to provide over 1 billion IP
addresses to advertising platforms.
Now, every time a device like a cell phone or laptop requests a
location, the on-device software scans for Wi-Fi, GPS, or cell tower
data. Combining all of these data points allows Skyhook to provide
extremely accurate coordinates and pass this information along to
MEDIATA to use.
While this approach still is not perfect, it allows MEDIATA’s adver-
tisements to become closer than ever to their target customers. A
nine-month study conducted after implementing Skyhook showed
that MEDIATA saw a 20% increase in marketing campaign effectiveness.
Creating and employing this data governance system allowed MEDIATA
to clean its datasets and create new, effective methods to reach target
audiences.
Questions
1. What business challenges did MEDIATA face because of its
dirty data?
2. What is the function of location-based advertising?
3. Why is it important to maintain accurate location data?
4. How did Skyhook and data governance enable MEDIATA to
achieve its vision?
5. What benefits did MEDIATA achieve as a result of implementing
data governance?
Sources: Compiled from Cailean (2016), Schneider (2014), and Schneider (2015).
IS Concepts and Classification 29
Components of an IS
A computerized IS consists of six interacting components. Regardless of type and where and by
whom they are used within an organization, the components of an IS must be carefully man-
aged to provide maximum benefit to an organization (see Figure 2.3).
PROCESSING
Programs
Equipments
Storage
FEEDBACK
Error Report
Performance Metrics
Hard Drive
Server
USB
INPUT
Data
Information
Knowledge
Instructions
STORAGE
OUTPUT
Reports
Graphics
Calculations
FIGURE 2.2 IPOS cycle.
People
DATAD
Procedures
Network Softwarek So
Hardware
FIGURE 2.3 Components of an IS.
1. Hardware Any physical device used in a computerized IS. Examples include central pro-
cessing unit (CPU), sound card, video card, network card, hard drive, display, keyboard,
motherboard, processor, power supply, modem, mouse, and printer.
2. Software A set of machine-readable instructions (code) that makes up a computer
application that directs a computer’s processor to perform specific operations. Computer
software is nontangible, contrasted with system hardware, which is the physical compo-
nent of an IS. Examples include Internet browser, operating system (OS), Microsoft Office,
Skype, and so on.
3. People Any person involved in using an IS. Examples include programmers, operators
help desk, and end-users.
4. Procedures Documentation containing directions on how to use the other components
of an IS. Examples include operational manual and user manual.
5. Network A combination of lines, wires, and physical devices connected to each other
to create a telecommunications network. In computer networks, networked computing
30 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
devices exchange data with each other using a data link. The connections between nodes
are established using either cable media or wireless media. Networks can be internal
or external. If they are available only internally within an organization, they are called
“intranets.” If they are available externally, they are called “internets.” The best-known
example of a computer network is the World Wide Web.
6. Data Raw or unorganized facts and figures (such as invoices, orders, payments, customer
details, product numbers, product prices) that describe conditions, ideas, or objects.
Data, Information, Knowledge, and
Wisdom
As you can see in Figure 2.3, data is the central component of any information system. Without
data, an IS would have no purpose and companies would unable to conduct business. Gener-
ally speaking, ISs process data into meaningful information that produces corporate knowl-
edge and ultimately creates wisdom that fuels corporate strategy.
Data are the raw material from which information is produced; the quality, reliability, and
integrity of the data must be maintained for the information to be useful. Data are the raw facts
and figures that are not organized in any way. Examples are the number of hours an employee
worked in a certain week or the number of new Ford vehicles sold from the first quarter (Q1) of
2015 through the second quarter (Q2) of 2017 (Figure 2.4).
Information is an organization’s most important asset, second only to people. Information
provides the “who,” “what,” “where,” and “when” of data in a given context. For example,
Data describe products,
customers, events, activities, and
transactions that are recorded,
classified, and stored.
Information is data that have
been processed, organized, or
put into context so that they have
meaning and value to the person
receiving them.
Knowledge adds understanding,
experience, accumulated learning,
and expertise as they apply to a
current problem or activity, to
information.
Creatively assess knowledge to develop
innovative policies and procedures to
reverse downward trend in sales
Use information to determine reasons
for consistent downward trend in sales
from June 2016 to June 2017
17, 25, 54, 12, 68, 19, 39, 42, 72
Number of new vehicles sold
DATA
(Raw figures)
INFORMATION
(who, what, where, when)
KNOWLEDGE
(how)
WISDOM
(why)
Q
1
2
0
15
Q
2
20
15
Q
3
20
15
Q
4
20
15
Q
1
20
1
6
Q
2
20
16
Q
3
20
16
Q
4
20
16
Q
1
20
17
Q
2
20
17
New Vehicle Sales by Quarter
6
5
4
3
2
1
0
FIGURE 2.4 Examples of data, information, knowledge, and wisdom.
IS Concepts and Classification 31
summarizing the quarterly sales of new Ford vehicles from Q1 2015 through Q2 2017 provides
information that shows sales have steadily decreased from Q2 2016.
Knowledge is used to answer the question “how.” In our example, it would involve deter-
mining how the trend can be reversed, for example, customer satisfaction can be improved,
new features can be added, and pricing can be adjusted.
Wisdom is more abstract than data and information (that can be harnessed) and
knowledge (that can be shared). Wisdom adds value and increases effectiveness. It answers the
“why” in a given situation. In the Ford example, wisdom would be corporate strategists evalu-
ating the various reasons for the sales drop, creatively analyzing the situation as a whole, and
developing innovative policies and procedures to reverse the recent downward trend in new
vehicle sales.
ISs collect or input and process data to create and distribute reports or other outputs based
on information gleaned from the raw data to support decision-making and business processes
that, in turn, produce corporate knowledge that can be stored for future use. Figure 2.5 shows
the input-processing-output-storage (IPOS) cycle.
Wisdom is a collection of
values, ethics, moral codes, and
prior experiences that form an
evaluated understanding or
common-sense judgment.
Storage
Temporary memory (RAM), hard disks, flash memory, cloud
People
Users, clients, customers, operators, technicians, governments, companies
Sending
results,
collecting
data,
feedback
Communication
Working with
information,
changing,
calculating,
manipulating
Processing
Data collected,
captured,
scanned,
snapped from
transactions
Input
Showing
results on
screen,
hardcopy, digital
copy, archive
Output
FIGURE 2.5 Input-processing-output-storage model.
Types of ISS
An IS may be as simple as a single computer and a printer used by one person, or as complex
as several thousand computers of various types (tablets, desktops, laptops, mainframes) with
hundreds of printers, scanners, and other devices connected through an elaborate network
used by thousands of geographically dispersed employees. Functional ISs that support busi-
ness analysts and other departmental employees range from simple to complex, depending on
the type of employees supported. The following examples show the support that IT provides to
major functional areas.
1. Marketing Utilizing IBM software, Bolsa de Comercio de Santiago, a large stock exchange
in Chile, is able to process its ever-increasing, high-volume trading in microseconds. The
Chilean stock exchange system can do the detective work of analyzing current and past
transactions and market information, learning, and adapting to market trends and con-
necting its traders to business information in real time. Immediate throughput in combina-
tion with analytics allows traders to make more accurate decisions.
2. Sales According to the New England Journal of Medicine, one in five patients suffers
from preventable readmissions, which cost taxpayers over $17 billion a year. In the past,
hospitals have been penalized for high readmission rates with cuts to the payments they
receive from the government (Zuckerman et al., 2016). Using effective management
information systems (MISs), the health-care industry can leverage unstructured informa-
tion in ways not possible before, according to Matt McClelland, manager of information
32 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
governance for Blue Cross Blue Shield of North Carolina. “With proper support, informa-
tion governance can bridge the gaps among the need to address regulation and litiga-
tion risk, the need to generate increased sales and revenue, and the need to cut costs
and become more efficient. When done right, information governance positively impacts
every facet of the business,” McClelland said in the Information Governance Initiative
(Jarousse, 2016).
Figure 2.6 illustrates the classification of the different types of ISs used in organiza-
tions, the typical level of workers who use them and the types of input/output (I/O) pro-
duced by each of the ISs. At the operational level of the organization, line workers use
transaction processing systems (TPSs) to capture raw data and pass it along (output) to
middle managers. The raw data is then input into office automation (OA) and MISs by middle
managers to produce information for use by senior managers. Next, information is input into
decision support systems (DSSs) for processing into explicit knowledge that will be used
by senior managers to direct current corporate strategy. Finally, corporate executives input
the explicit knowledge provided by the DSSs into executive information systems (EISs)
and apply their experience, expertise, and skills to create wisdom that will lead to new cor-
porate strategies.
Executives
Senior Managers
Middle Managers
Line Workers
Executive Information Systems
(EIS)
Decision Support Systems
(DSS)
Management Information Systems
(MIS)
Transaction Processing Systems
(TPS)
Wisdom
Knowledge
Information
Data
FIGURE 2.6 Hierarchy of ISs, input/output, and user levels.
Transaction Processing System (TPS)
A TPS is designed to process specific types of data input from ongoing transactions. TPSs can
be manual, as when data are typed into a form on a screen, or automated by using scanners or
sensors to capture barcodes or other data (Figure 2.7). TPSs are usually operated directly by
frontline workers and provide the key data required to support the management of operations.
Organizational data are processed by a TPS, for example, sales orders, reservations, stock
control, and payments by payroll, accounting, financial, marketing, purchasing, inventory con-
trol, and other functional departments. The data are usually obtained through the automated or
semiautomated tracking of low-level activities and basic transactions. Transactions are either:
• internal transactions that originate within the organization or that occur within the orga-
nization, for example, payroll, purchases, budget transfers, and payments (in accounting
terms, they are referred to as accounts payable); or
• external transactions that originate from outside the organization, for example, from cus-
tomers, suppliers, regulators, distributors, and financing institutions.
TPSs are essential systems. Transactions that are not captured can result in lost sales, dis-
satisfied customers, unrecorded payments, and many other types of data errors with financial
IS Concepts and Classification 33
impacts. For example, if the accounting department issued a check to pay an invoice (bill)
and it was cashed by the recipient, but information about that transaction was not captured,
then two things happen. First, the amount of cash listed on the company’s financial state-
ments is incorrect because no deduction was made for the amount of the check. Second, the
accounts payable (A/P) system will continue to show the invoice as unpaid, so the accounting
department might pay it a second time. Likewise, if services are provided, but the transactions
are not recorded, the company will not bill for them and thus lose service revenue.
Batch versus Online Real-Time Processing Data captured by a TPS are pro-
cessed and stored in a database; they then become available for use by other systems.
Processing of transactions is done in one of two modes:
1. Batch processing A TPS in batch processing mode collects all transaction for a day,
shift, or other time period, and then processes the data and updates the data stores. Pay-
roll processing done weekly or bi-weekly is an example of batch mode.
2. Online transaction processing (OLTP) or real-time processing The TPS processes each
transaction as it occurs, which is what is meant by the term real-time processing. In order
for OLTP to occur, the input device or website must be directly linked via a network to the
TPS. Airlines need to process flight reservations in real time to verify that seats are available.
Batch processing costs less than real-time processing. A disadvantage is that data are inaccu-
rate because they are not updated immediately, in real time.
Processing Impacts Data Quality As data are collected or captured, they are vali-
dated to detect and correct obvious errors and omissions. For example, when a customer sets
up an account with a financial services firm or retailer, the TPS validates that the address, city,
and postal code provided are consistent with one another and also that they match the credit
card holder’s address, city, and postal code. If the form is not complete or errors are detected,
the customer is required to make the corrections before the data are processed any further.
Data errors detected later may be time-consuming to correct or cause other problems. You
can better understand the difficulty of detecting and correcting errors by considering identity
theft. Victims of identity theft face enormous challenges and frustration trying to correct data
about them.
Management Information System (MIS)
An MIS is built on the data provided by TPS. MISs are management-level systems that are used
by middle managers to help ensure the smooth running of an organization in the short to
medium term. The highly structured information provided by these systems allows managers
©
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to
FIGURE 2.7 Scanners automate the input of data into a
transaction processing system (TPS).
34 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
to evaluate an organization’s performance by comparing current with previous outputs. Func-
tional areas or departments―accounting, finance, production/operations, marketing and
sales, human resources, and engineering and design―are supported by ISs designed for their
particular reporting needs. General-purpose reporting systems are referred to as management
information systems (MISs). Their objective is to provide reports to managers for tracking
operations, monitoring, and control.
Typically, a functional system provides reports about such topics as operational efficiency,
effectiveness, and productivity by extracting information from databases and processing it
according to the needs of the user. Types of reports include the following:
• Periodic These reports are created or run according to a pre-set schedule. Examples are
daily, weekly, and quarterly. Reports are easily distributed via e-mail, blogs, internal web-
sites (called intranets), or other electronic media. Periodic reports are also easily ignored if
workers do not find them worth the time to review.
• Exception Exception reports are generated only when something is outside the norm,
either higher or lower than expected. Sales in hardware stores prior to a hurricane may be
much higher than the norm. Or sales of fresh produce may drop during a food contamina-
tion crisis. Exception reports are more likely to be read because workers know that some
unusual event or deviation has occurred.
• Ad hoc, or on demand Ad hoc reports are unplanned reports. They are generated to a
mobile device or computer on demand as needed. They are generated on request to learn
more about a situation, problem, or opportunity.
Reports typically include interactive data visualizations, such as column and pie charts, as
shown in Figure 2.8.
©
D
a
m
ir
K
a
ra
n
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to
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FIGURE 2.8 Sample report produced by an MIS.
Decision Support System (DSS)
A DSS is a knowledge-based system used by senior managers to facilitate the creation of knowl-
edge and allow its integration into the organization. More specifically, a DSS is an interactive
application that supports decision-making by manipulating and building upon the information
from an MIS and/or a TPS to generate insights and new information.
Configurations of a DSS range from relatively simple applications that support a single
user to complex enterprisewide systems. A DSS can support the analysis and solution of a
specific problem, evaluate a strategic opportunity, or support ongoing operations. These sys-
tems support unstructured and semistructured decisions, such as make-or-buy-or-outsource
decisions, or what products to develop and introduce into existing markets.
Degree of Structure of Decisions Decisions range from structured to unstruc-
tured. Structured decisions are those that have a well-defined method for solving and the
IS Concepts and Classification 35
data necessary to reach a sound decision. An example of a structured decision is determining
whether an applicant qualifies for an auto loan, or whether to extend credit to a new customer―
and the terms of those financing options. Structured decisions are relatively straightforward
and made on a regular basis, and an IS can ensure that they are done consistently.
At the other end of the continuum are unstructured decisions that depend on human
intelligence, knowledge, and/or experience―as well as data and models to solve. Examples
include deciding which new products to develop or which new markets to enter. Semistruc-
tured decisions fall in the middle of the continuum. DSSs are best suited to support these types
of decisions, but they are also used to support unstructured ones. To provide such support,
DSSs have certain characteristics to support the decision-maker and the overall decision-
making process.
The main characteristic that distinguishes a DSS from an MIS is the inclusion of models.
Decision-makers can manipulate models to conduct experiments and sensitivity analyses, for
example, what-if and goal seeking. What-if analysis refers to changing assumptions or data
in the model to observe the impacts of those changes on the outcome. For example, if sales
forecasts are based on a 5% increase in customer demand, a what-if analysis would replace the
5% with higher and/or lower estimates to determine what would happen to sales if demand
changed. With goal seeking, the decision-maker has a specific outcome in mind and needs
to determine how that outcome could be achieved and whether it is feasible to achieve that
desired outcome. A DSS can also estimate the risk of alternative strategies or actions.
California Pizza Kitchen (CPK) uses a DSS to support inventory decisions. CPK has over 200
locations in 32 U.S. states and 13 other countries, including 17 California Pizza Kitchen non-
traditional, franchise concepts designed for airports, universities, and stadiums. Maintaining
optimal inventory levels at all its restaurants was challenging and time-consuming. The original
MIS was replaced by a DSS to make it easy for the chain’s managers to maintain updated records,
generate reports as and when needed, and make corporate- and restaurant-level decisions.
Many CPK restaurants reported a 5% increase in sales after the DSS was implemented.
Executive Information System (EIS)
EISs are strategic-level information systems that help executives and senior managers analyze
the environment in which the organization exists. They typically are used to identify long-term
trends and to plan appropriate courses of action. The information in such systems is often
weakly structured and comes from both internal and external sources. EISs are designed to be
operated directly by executives without the need for intermediaries and easily tailored to the
preferences of the individual using them. An EIS organizes and presents data and information
from both external data sources and internal MIS or TPS in an easy-to-use dashboard format to
support and extend the inherent capabilities of senior executives.
Initially, EISs were custom-made for an individual executive. However, a number of
off-the-shelf EIS packages now exist and some enterprise-level systems offer a customizable
EIS module.
The ways in which the different characteristics of the various types of ISs are classified is
shown in Table 2.2.
Here’s an example of how these ISs are used together to add value in an organization.
Day-to-day transaction data collected by the TPS are converted into prescheduled summa-
rized reports by middle managers using an MIS. The findings in these reports are then analyzed
by senior managers who use a DSS to support their semistructured or unstructured decision-
making. DSSs contain models that consist of a set of formulas and functions, such as statistical,
financial, optimization, and/or simulation models. Corporations, government agencies, the
military, health care, medical research, major league sports, and nonprofits depend on their
DSSs to answer what-if questions to help reduce waste in production operations, improve
inventory management, support investment decisions, and predict demand and help sustain
a competitive edge.
Customer data, sales, and other critical data produced by the DSS are then selected for
further analysis, such as trend analysis or forecasting demand and are input into an EIS for
36 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
TA B L E 2 . 2 Characteristics of Types of Information Systems
Type Characteristics
TPS Used by operations personnel
Produce information for other ISs
Use internal and external data
Efficiency oriented
MIS Used by lower and middle managers
Based on internal information
Support structured decisions
Inflexible
Lack analytical capabilities
Focus on past and present data
DSS Used by senior managers
Support semistructured or unstructured decisions
Contain models or formulas that enable sensitivity analysis, what-if analysis, goal seeking,
and risk analysis
Use internal and external data plus data added by the decision-maker who may have
insights relevant to the decision situation
Predict the future
EIS Used by C-level managers
Easy-to-use, customizable interface
Support unstructured decisions
Use internal and external data sources
Focus on effectiveness of the organization
Very flexible
Focus on the future
use by top level management, who add their experience and expertise to make unstructured
decisions that will affect the future of the business.
Figure 2.9 shows how the major types of ISs relate to one another and how data flow
among them. In this example,
1. Data from online purchases are captured and processed by the TPS and then stored in the
transactional database.
2. Data needed for reporting purposes are extracted from the database and used by the MIS
to create periodic, ad hoc, or other types of reports.
3. Data are output to a DSS where they are analyzed using formulas, financial ratios,
or models.
ISS Exist within Corporate Culture
It is important to remember that ISs do not exist in isolation. They have a purpose and a social
(organizational) context. A common purpose is to provide a solution to a business problem. The
social context of the system consists of the values and beliefs that determine what is admis-
sible and possible within the culture of the organization and among the people involved. For
example, a company may believe that superb customer service and on-time delivery are critical
success factors. This belief system influences IT investments, among other factors.
The business value of IT is determined by the people who use them, the business processes
they support, and the culture of the organization. That is, IS value is determined by the
IT Infrastructure, IT Architecture, and Enterprise Architecture 37
relationships among ISs, people, and business processes―all of which are influenced strongly
by organizational culture.
In an organization, there may be a culture of distrust between the technology and business
employees. No enterprise IT architecture methodology or data governance can bridge this
divide unless there is a genuine commitment to change. That commitment must come from
the highest level of the organization―senior management. Methodologies cannot solve people
problems; they can only provide a framework in which those problems can be solved.
Questions
1. Name the six components of an IS.
2. Describe the differences between data, information, knowledge, and wisdom.
3. Define TPS and give an example.
4. Explain why TPSs need to process incoming data before they are stored.
5. Define MIS and DSS and give an example of each.
6. What characteristic distinguishes a DSS from an MIS?
7. What level of personnel typically uses an EIS?
8. What factors determine IS value?
2.2 IT Infrastructure, IT Architecture,
and Enterprise Architecture
Every enterprise has a core set of ISs and business processes that execute the transactions that
keep it in business. Transactions include processing orders, order fulfillment and delivery, pur-
chasing inventory and supplies, hiring and paying employees, and paying bills. To most effec-
tively utilize its IT assets, an organization must create an IT infrastructure, IT architecture, and
an enterprise architecture (EA) as shown in Figure 2.10.
Data
Data Data
Data are extracted,
transformed, &
loaded (ETL)
Data from online
purchases
of transactional
data
Database
Reporting
MIS
Models applied to
data for analysis
DSS
Processes raw
data
TPS
Analytical processing
of data to discover
trends and learn
insights
Data Warehouse
FIGURE 2.9 Flow of data from point of sale (POS) through processing, storage, reporting,
decision support, and analysis. Also shows the relationships among different types of ISs.
38 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
IT infrastructure is an inventory of the physical IT devices that an organization owns and
operates. The IT infrastructure describes an organization’s entire collection of hardware, soft-
ware, networks, data centers, facilities, and other related equipment used to develop, test,
operate, manage, and support IT services. It does NOT include the people or process compo-
nents of an information system.
IT architecture guides the process of planning, acquiring, building, modifying, inter-
facing, and deploying IT resources in a single department within an organization. The IT
architecture should offer a way to systematically identify technologies that work together
to satisfy the needs of the departments’ users. The IT architecture is a blueprint for how
future technology acquisitions and deployment will take place. It consists of standards,
investment decisions, and product selections for hardware, software, and communications.
The IT architecture is developed first and foremost based on department direction and
business requirements.
Enterprise architecture (EA) reviews all the information systems across all departments in
an organization to develop a strategy to organize and integrate the organization’s IT infrastruc-
tures to help it meet the current and future goals of the enterprise and maximize the value of
technology to the organization. In this way, EA provides a holistic view of an organization with
graphic and text descriptions of strategies, policies, information, ISs, and business processes
and the relationships between them.
The EA adds value in an organization in that it can provide the basis for organizational
change just as architectural plans guide a construction project. Since a poorly crafted enterprise
architecture (EA) can also hinder day-to-day operations and efforts to execute business strategy,
it is more important than ever before to carefully consider the EA within your organization when
deciding on an approach to business, technology, and corporate strategy. Simply put, EA helps
solve two critical challenges: where an organization is going, and how it will get there.
The success of EA is measured not only in financial terms, such as profitability and return
on investment (ROI), but also in nonfinancial terms, for example, improved customer satisfac-
tion, faster speed to market, and lower employee turnover as diagrammed in Figure 2.11 and
demonstrated in IT at Work 2.1.
EA Helps to Maintain Sustainability
As you read in Chapter 1, the volume, variety, and speed of data being collected or generated
have increased dramatically over the past decade. As enterprise ISs become more complex,
IT
Infrastructure
IT
Architecture
HR ACCTG.
PRODUCTION
SALES
FINANCE
ORGANIZATIONAL
STRATEGY
TO MAXIMIZE
IT VALUE
Enterprise
Architecture
Policy
FIGURE 2.10 Comparing IT infrastructure, IT architecture, and enterprise architecture.
IT Infrastructure, IT Architecture, and Enterprise Architecture 39
SUCCESS
(PROFITABILITY, ROI,
INCREASED CUSTOMER SATISFACTION,
FASTER SPEED TO MARKET,
LOWER EMPLOYEE TURNOVER)
CREATING
IT
LEVERAGING
IT
MAINTAINING
IT
EA
FIGURE 2.11 Enterprise architecture success.
IT at Work 2.1
A New Enterprise Architecture Improves Data
Quality and EIS Use
Executives at a large chemical corporation were supported by an IS
specifically designed for their needs—called an executive information
system (EIS). The EIS was designed to provide senior managers with
internal and external data and key performance indicators (KPIs) that
were relevant to their specific needs. Tech Note 2.1 describes KPIs.
As with any system, the value of the EIS depends on the data quality.
Too Much Irrelevant Data
Unfortunately, the EIS was a failure. Executives soon discovered that
only half of the data available through the EIS related to their level of
analysis and decision-making at the corporate level. A worse prob-
lem was that the data they needed were not available when and
how they wanted them. For example, executives needed to analyze
current detailed sales revenue and cost data for every strategic busi-
ness unit (SBU), product line, and operating business to compare
performance. But, data were not in standardized format as needed,
making analysis difficult or impossible. A large part of the problem
was that SBUs reported sales revenues in different time frames (e.g.,
daily, weekly, monthly, or quarterly), and many of those reports
were not available when needed. As a result, senior management
could not get a trusted view of the company’s current overall perfor-
mance and did not know which products were profitable.
There were two reasons for the failure of the EIS:
1. IT architecture was not designed for customized reporting
The design of the IT architecture had been based on financial
accounting rules. That is, the data were organized to make it
easy to collect and consolidate the data needed to prepare
financial statements and reports that had to be submitted
to the SEC (Securities and Exchange Commission) and other
regulatory agencies. These statements and reports have
well-defined or standardized formats and only need to be pre-
pared at specific times during the year, typically annually or
quarterly. The organization of the data (for financial reporting)
did not have the flexibility needed for the customized ad hoc
(unplanned) data needs of the executives. For example, it was
nearly impossible to generate customized sales performance
(nonfinancial) reports or do ad hoc analyses, such as com-
paring inventory turnover rates by product for each region
for each sales quarter. Because of lags in reports from various
SBUs, executives could not trust the underlying data.
2. Complicated user interface Executives could not easily review
the KPIs. Instead, they had to sort through screens packed with
too much data—some of interest and some irrelevant. To com-
pensate for poor interface design, several IT analysts themselves
had to do the data and KPI analyses for the executives—delaying
response time and driving up the cost of reporting.
Solution: New Enterprise Architecture with
Standardized Data Formats
The CIO worked with a task force to design and implement an entirely
new EA. Data governance policies and procedures were imple-
mented to standardize data formats companywide. Data govern-
ance eliminated data inconsistencies to provide reliable KPI reports
on inventory turns, cycle times, and profit margins of all SBUs.
The new architecture was business-driven instead of financial
reporting-driven. It was easy to modify reports—eliminating the
costly and time-consuming ad hoc analyses. Fewer IT resources are
needed to maintain the system. Because the underlying data are
now relatively reliable, EIS use by executives increased significantly.
IT at Work Questions
1. Why was an EIS designed and implemented at the large
chemical corporation?
2. What problems did the executives have with the EIS?
3. What were the two reasons for those EIS problems?
4. How did the CIO improve the EIS?
5. What are the benefits of the new IT enterprise architecture?
6. What are the benefits of data governance?
40 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
long-range IT planning is critical. Companies cannot simply add storage, new apps, or data ana-
lytics on an as-needed basis and expect those additional IT assets to work with existing systems.
The relationship between complexity and planning for the future is easier to see in physical
things such as buildings and transportation systems. For example, if you are constructing a
simple holiday cabin in a remote area, there is no need to create a detailed plan for future
expansion. On the other hand, if you are building a large commercial development in a highly
populated area, you’re not likely to succeed without a detailed project plan. Relating this to
the case of enterprise ISs, if you are building a simple, single-user, nondistributed system, you
would not need to develop a well-thought-out growth plan. However, this approach would not
be feasible to enable you to successfully manage big data, copious content from mobiles and
social networks, and data in the cloud. Instead, you would need a well-designed set of plans,
or blueprints, provided by an EA to align IT with business objectives by guiding and controlling
hardware acquisition, software add-ons and upgrades, system changes, network upgrades,
choice of cloud services, and other digital technology investments that you will need to make
your business sustainable.
There are two specific strategic issues that the EA is designed to address:
1. IT systems’ complexity IT systems have become unmanageably complex and expensive
to maintain.
2. Poor business alignment Organizations find it difficult to keep their increasingly expen-
sive IT systems aligned with business needs.
Business and IT Benefits of EA Having the right EA in place is important for the fol-
lowing reasons:
• EA cuts IT costs and increases productivity by giving decision-makers access to information,
insights, and ideas where and when they need them.
• EA determines an organization’s competitiveness, flexibility, and IT economics for the next
decade and beyond. That is, it provides a long-term view of a company’s processes, sys-
tems, and technologies so that IT investments do not simply fulfill immediate needs.
• EA helps align IT capabilities with business strategy―to grow, innovate, and respond
to market demands, supported by an IT practice that is 100% in accord with business
objectives.
• EA can reduce the risk of buying or building systems and enterprise applications that are
incompatible or unnecessarily expensive to maintain and integrate.
Tech Note 2.1
Key Performance Indicators (KPIs)
KPIs are a set of quantifiable measures used to evaluate factors that
are crucial to the success of an organization. KPIs present data in easy-
to-comprehend and comparison-ready formats to gauge or compare
performance in terms of meeting an organization’s operational and
strategic goals. KPIs are used in four main areas: increasing revenue;
reducing costs; improving process cycle-time; and improving cus-
tomer satisfaction. Examples of key comparisons include actual versus
budget, actual versus forecasted, and the ROI for this year versus prior
years. KPIs help reduce the complex nature of organizational perfor-
mance to a small number of understandable measures, including:
• Financial KPIs: accounts payable turnover; inventory turn-
over; net profit margin; sum of difference between planned
and actual project budgets
• Social media KPIs: social traffic and conversions (number of
visitors who are converted to customers); likes; new followers
per week; social visits and leads
• Sales and marketing KPIs: cost per lead; how much revenue
a marketing campaign generates; number of customer
complaints; cycle time from customer request to delivery,
percentage of correspondence replied to on time
• Operational and supply chain KPIs: units per transaction;
carrying cost of inventory; order status; back order rate
• Environmental and carbon-footprint KPIs: energy, water, or
other resource use; spend by utility; weight of landfill waste.
IT Infrastructure, IT Architecture, and Enterprise Architecture 41
TA B L E 2 . 3 Components of an Enterprise Architecture
Business
architecture
How the business works. Includes broad business strategies and plans for
moving the organization from where it is now to where it wants to be. Processes
the business uses to meet its goals.
Application
architecture
Portfolio of organization’s applications. Includes descriptions of automated
services that support business processes; descriptions of interactions and
interdependencies between the organization’s ISs.
Information
architecture
What the organization needs to know to perform its business processes and
operations. Includes standard data models; data management policies and
descriptions of patterns of information production and use in an organization.
Technology
architecture
Hardware and software that supports the organization. Examples include
desktop and server software; OSs; network connectivity components;
printers, modems.
IT at Work 2.2
EA Must Be Dynamic and Evolving
In order to keep IT aligned with the business, the EA must be
a dynamic plan. As shown in the model in Figure 2.12, the EA
evolves toward the target architecture, which represents the
company’s future IT needs. According to this model, EA defines
the following:
1. The organization’s mission, business functions, and future
direction
2. Information and information flows needed to perform the
mission
3. The current baseline architecture
4. The desired target architecture
5. The sequencing plan or strategy to progress from the baseline
to the target architecture.
Baseline Transition Target
Im
p
le
m
en
ta
ti
o
n
S
ta
tu
s
Baseline architecture
Sequencing plan
Target architecture
FIGURE 2.12 The importance of viewing EA as a
dynamic and evolving plan The purpose of the EA is
to maintain IT–business alignment. Changes in priorities
and business are reflected in the target architecture to
help keep IT aligned with them (Bloomberg, 2016).
Developing an Enterprise Architecture (EA)
Developing an EA starts with the organization’s goals, for example, where does it want to be in
three years? and identifies the strategic direction in which it is heading and the business driv-
ers to which it is responding. The goal is to make sure that everyone understands and shares
a single vision. As soon as managers have defined this single shared vision of the future, they
then consider the impact this vision will have on the business, technical, information, and solu-
tions architectures of the enterprise. This shared vision of the future will dictate changes in all
these architectures, assign priorities to those changes, and keep those changes grounded in
business value.
According to Microsoft, the EA should include the four different perspectives shown in
Table 2.3.
It is important to recognize that the EA must be dynamic, not static. To sustain its effective-
ness, it should be an ongoing process of aligning the creation, operation, and maintenance of IT
across the organization with the ever-changing business objectives. As business needs change,
so must the EA, as demonstrated in IT at Work 2.2.
42 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
Questions
1. What is the purpose of the IT infrastructure?
2. How is the IT infrastructure different from the IT architecture?
3. What is the purpose of an EA?
4. What are the business benefits of EA?
5. Explain why it is necessary to ensure that an EA maintains alignment between IT and business strategy?
6. Explain KPIs and give an example.
2.3 Information Management and Data
Governance
As shown in Figure 2.3, data is the heart of the business and the central component of an IS.
Most business initiatives succeed or fail based on the quality of their data. Effective planning
and decision-making depend on systems being able to make data available in usable formats
on a timely basis. Almost everyone manages information. You manage your social and cloud
accounts across multiple mobile devices and computers. You update or synchronize (“synch”)
your calendars, appointments, contact lists, media files, documents, and reports. Your pro-
ductivity depends on the compatibility of devices and applications and their ability to share
data. Not being able to transfer and synch whenever you add a device or app is bothersome
and wastes your time. For example, when you switch to the latest mobile device, you might
need to reorganize content to make dealing with data and devices easier. To simplify add-ons,
upgrades, sharing, and access, you might leverage cloud services such as iTunes, Instagram,
Diigo, and Box.
This is just a glimpse at some of the information management situations that organiza-
tions face today and shows why a continuous plan is needed to guide, control, and govern IT
growth. As with building construction (Figure 2.13), blueprints and models help guide and
govern future IT and digital technology investments.
Information management is
the use of IT tools and methods
to collect, process, consolidate,
store, and secure data from
sources that are often fragmented
and inconsistent.
Career Insight 2.1
Essential Skills of an Enterprise Architect (EA)
Enterprise architects need much more than technology skills. On a
daily basis, an enterprise architect’s activities can change quickly
and significantly. Ideally, enterprise architects should come from
a highly technical background. Even though enterprise architects
deal with many other factors besides technology, it is still impor-
tant to keep technical skills current. The job performance and suc-
cess of such an architect―or anyone responsible for large-scale IT
projects―depend on a broad range of skills.
• Interpersonal or people skills The job requires interacting
with people and getting their cooperation.
• Ability to influence and motivate A large part of the job is
motivating users to comply with new processes and practices.
• Negotiating skills The project needs resources―time,
money, and personnel―that must be negotiated to get things
accomplished.
• Critical-thinking and problem-solving skills Architects face
complex and unique problems. Being able to expedite solu-
tions prevents bottlenecks.
• Business and industry expertise Knowing the business
and industry improves the outcomes and the architect’s
credibility.
• Process orientation Thinking in terms of process is essential
for an enterprise architect. Building repeatable and reusable
processes as artifacts from the work they do and how they
work themselves.
The most common function an enterprise architect will perform
is that of overseeing a large-scale program. Programs are a
group of related projects and as such, managing EA implementa-
tions requires someone who is able to handle multiple aspects
of a project at one time. Project management is covered in
Chapter 13.
Information Management and Data Governance 43
Information Management Harnesses Scattered Data
Business information is generally scattered throughout an enterprise, stored in separate sys-
tems dedicated to specific purposes, such as operations, supply chain management, or cus-
tomer relationship management. Major organizations have over 100 data repositories (storage
areas). In many companies, the integration of these disparate systems is limited―as is users’
ability to access all the information they need. As a result, despite all the information flowing
through companies, executives, managers, and workers often struggle to find the information
they need to make sound decisions or do their jobs. The overall goal of information manage-
ment is to eliminate that struggle through the design and implementation of a sound data gov-
ernance program and a well-planned EA.
Providing easy access to large volumes of information is just one of the challenges facing
organizations. The days of simply managing structured data are over. Now, organizations must
manage semistructured and unstructured content from social and mobile sources even though
that data may be of questionable quality.
Information management is critical to data security and compliance with continually
evolving regulatory requirements, such as the Sarbanes-Oxley Act, Basel III, the Computer
Fraud and Abuse Act (CFAA), the USA PATRIOT Act, and the Health Insurance Portability and
Accountability Act (HIPAA).
Issues of information access, management, and security must also deal with information
degradation and disorder―where people do not understand what data mean or how the data
can be useful.
Reasons for Information Deficiencies
Organizational information and decision support technologies have developed over many dec-
ades. During that time management teams’ priorities have changed along with their under-
standing of the role of IT within the organization; technology has advanced in unforeseeable
ways, and IT investments have been increased or decreased based on competing demands on
the budget. Other common reasons why information deficiencies are still a problem include:
1. Data silos Information can be trapped in departmental data silos (also called information
silos), such as marketing or production databases. Data silos are illustrated in Figure 2.14.
Since silos are unable to share or exchange data, they cannot consistently be updated.
When data are inconsistent across multiple enterprise applications, data quality cannot
(and should not) be trusted without extensive verification. Data silos exist when there is no
overall IT architecture to guide IT investments, data coordination, and communication.
Data silos support a single function and, as a result, do not support an organization’s cross-
functional needs.
Data silo are stand-alone
data stores. Their data are not
accessible by other ISs that need it
or outside that department.
©
M
a
rt
in
B
a
rr
a
u
d
/A
la
m
y
FIGURE 2.13 Blueprints and models, like those used for
building construction, are needed to guide and govern an
enterprise’s IT assets.
44 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
For example, most health-care organizations are drowning in data, yet they cannot
get reliable, actionable insights from these data. Physician notes, registration forms, dis-
charge summaries, documents, and more are doubling every five years. Unlike structured
machine-ready data, these are messy data that take too much time and effort for health-
care providers to include in their business analysis. So, valuable messy data are routinely
left out. Millions of insightful patient notes and records sit inaccessible or unavailable in
separate clinical data silos because historically there has been no easy way to analyze the
information they contain.
2. Lost or bypassed data Data can get lost in transit from one system to another. Or, data
might never get captured because of inadequately tuned data collection systems, such
as those that rely on sensors or scanners. Or, the data may not get captured in sufficient
detail, as described in Tech Note 2.2.
3. Poorly designed interfaces Despite all the talk about user-friendly interfaces, some ISs
are horrible to deal with. Poorly designed interfaces or formats that require extra time
and effort to figure out increase the risk of errors from misunderstanding the data or
ignoring them.
4. Nonstandardized data formats When users are presented with data in inconsistent or
nonstandardized formats, errors increase. Attempts to compare or analyze data are more dif-
ficult and take more time. For example, if the Northeast division reports weekly gross sales
revenues per product line and the Southwest division reports monthly net sales per product,
you cannot compare their performance without converting the data to a common format.
Consider the extra effort needed to compare temperature-related sales, such as air condition-
ers, when some temperatures are expressed in degrees Fahrenheit and others in Centigrade.
5. Difficult to hit moving targets The information that decision-makers want keeps
changing―and changes faster than ISs can respond to because of the first four reasons in
this list. Tracking tweets, YouTube hits, and other unstructured content requires expensive
investments―which managers find risky in an economic downturn.
These are the data challenges managers have to face when there is little or no information
management. Companies undergoing fast growth or merger activity or those with decentral-
ized systems (each division or business unit manages its own IT) will end up with a patchwork
of reporting processes. As you would expect, patchwork systems are more complicated to
modify, too rigid to support an agile business, and more expensive to maintain.
Information Requirements:
Understandable
Relevant
Timely
Accurate
Secure
Parts Replenish
Procuring
Design
Build
Ship
Sales
Fulfillment
Billing
Support
Customer data
Product data
Procurement data
Contract data
Data order
Parts inventory data
Engineering data
Logistics data
Data Types
Operations
silos
Sourcing
silos
Customer-facing
silos
FIGURE 2.14 Data (or information) silos are ISs that do not have the
capability to exchange data with other systems, making timely coordination
and communication across functions or departments difficult.
Information Management and Data Governance 45
Factors Driving the Shift from Silos to Sharing
and Collaboration
Senior executives and managers are aware of the problems associated with their data silos
and information management problems, but they also know about the huge cost and
disruption associated with converting to newer IT architectures. The “silo effect” occurs when
different departments of an organization do not share data and/or communicate effectively
enough to maintain productivity. Surprisingly, 75% of employers believe team work and
collaboration are essential, but only 18% of employees receive communication evaluations
during performance critiques (Marchese, 2016). In the new age of efficiency of service, many
companies like Formaspace, an industrial manufacturing and service corporation, must work
toward complete cloud integration of old silos to increase customer service and generate more
revenue. Enabling applications to interact with one another in an automated fashion to gain
better access to data increases meaningful productivity and decreases time and effort spent in
manual collaboration efforts. In an illustration of how silo integration is essential for a modern
corporation, IT technician at Formaspace, Loddie Alspach, claims that in 2015, the company
managed to increase revenues by 20% using Amazon-based cloud technology (Shore, 2015).
However, companies are struggling to integrate thousands of siloed global applications, while
aligning them to business operations. To remain competitive, they must be able to analyze and
adapt their business processes quickly, efficiently, and without disruption.
Greater investments in collaboration technologies have been reported by the research
firm Forrester (Keitt, 2014). A recent study identified four main factors that have influenced the
increased use of cloud technologies, as shown in Table 2.4 (Rai et al., 2015).
Tech Note 2.2
Need to Measure in Order to Manage
A residential home construction company had two divisions: stand-
ard homes and luxury homes. The company was not capturing
material, labor, and other costs associated with each type of con-
struction. Instead, these costs were pooled, making it impossible
to allocate costs to each type of construction and then to calculate
the profit margins of each division. They had no way of calculating
profit margins on each type of home within the divisions. Without
the ability to measure costs, they did not have any cost control.
After upgrading their ISs, they began to capture detailed data
at the division level. They discovered a wide profit margin on stand-
ard homes, which was hiding the losses occurring in the luxury
home division. Without cost control data, the profitable standard
homes division had been subsidizing the luxury home division for
many years. Based on the cost control data, the company decided
to focus more on standard homes and adjust their pricing on luxury
homes. This new cost control strategy increased the company’s
long-term performance.
TA B L E 2 . 4 Key Factors Leading to Increased Migration to the Cloud
Cost Savings
Efficient Use of Resources
Unlimited Scalability of Resources
Lower Maintenance
Business Benefits of Information Management
Based on the examples you have read, the obvious benefits of information management are:
1. Improves decision quality Decision quality depends on accurate and complete data.
2. Improves the accuracy and reliability of management predictions It is essential for
managers to be able to predict sales, product demand, opportunities, and competitive
46 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
threats. Management predictions focus on “what is going to happen” as opposed to finan-
cial reporting on “what has happened.”
3. Reduces the risk of noncompliance Government regulations and compliance require-
ments have increased significantly in the past decade. Companies that fail to comply with
laws on privacy, fraud, anti-money laundering, cybersecurity, occupational safety, and so
on face harsh penalties.
4. Reduces the time and cost of locating and integrating relevant information.
Data Governance: Maintaining Data Quality
and Cost Control
The success of every data-driven strategy or marketing effort depends on data governance.
Data governance policies must address structured, semistructured, and unstructured data
(discussed in Section 2.3) to ensure that insights can be trusted.
Enterprisewide Data Governance With an effective data governance program,
managers can determine where their data are coming from, who owns them, and who is
responsible for what―in order to know they can trust the available data when needed. Data
governance is an enterprisewide project because data cross boundaries and are used by people
throughout the enterprise. New regulations and pressure to reduce costs have increased the
importance of effective data governance. Governance eliminates the cost of maintaining and
archiving bad, unneeded, or inaccurate data. These costs grow as the volume of data grows.
Governance also reduces the legal risks associated with unmanaged or inconsistently managed
information.
Three industries that depend on data governance to comply with regulations or reporting
requirements are the following:
• Food industry In the food industry, data governance is required to comply with food
safety regulations. Food manufacturers and retailers have sophisticated control systems in
place so that if a contaminated food product, such as spinach or peanut butter, is detected,
they are able to trace the problem back to a particular processing plant or even the farm at
the start of the food chain.
• Financial services industry In the financial services sector, strict reporting requirements
of the Dodd−Frank Wall Street Reform and Consumer Protection Act of 2010 are leading
to greater use of data governance. The Dodd−Frank Act regulates Wall Street practices by
enforcing transparency and accountability in an effort to prevent another significant finan-
cial crisis like the one that occurred in 2008.
• Health-care industry Data are health care’s most valuable asset. Hospitals have moun-
tains of electronic patient information. New health-care accountability and reporting obli-
gations require data governance models for transparency to defend against fraud and to
protect patients’ information.
Master Data and Master Data Management (MDM) Master data is the term
used to describe business-critical information on customers, products and services,
vendors, locations, employees, and other things needed for operations and business trans-
actions. Master data are fundamentally different from the high volume, velocity, and vari-
ety of big data and traditional data. For example, when a customer applies for automobile
insurance, data provided on the application become the master data for that customer.
In contrast, if the customer’s vehicle has a device that sends data about his or her driving
Data governance is the control
of enterprise data through formal
policies and procedures to help
ensure data can be trusted and
are accessible.
Information Management and Data Governance 47
behavior to the insurer, those machine-generated data are transactional or operational, but
not master data.
Data are used in two ways―both depend on high-quality trustworthy data:
1. For running the business Transactional or operational use
2. For improving the business Analytic use
Master data are typically quite stable and typically stored in a number of different sys-
tems spread across the enterprise. Master data management (MDM) links and synchronizes
all critical data from those disparate systems into one file called a master file, to provide a
common point of reference. MDM solutions can be complex and expensive. Given their com-
plexity and cost, most MDM solutions are out of reach for small and medium companies. Ven-
dors have addressed this challenge by offering cloud-managed MDM services. For example, in
2013, Dell Software launched its next-generation Dell Boomi MDM. Dell Boomi provides MDM,
data management, and data quality services (DQS)―and they are 100% cloud-based with near
real-time synchronization.
Data governance and MDM manage the availability, usability, integrity, and security of data
used throughout the enterprise. Strong data governance and MDM are needed ensure data are
of sufficient quality to meet business needs. The characteristics and consequences of weak or
nonexistent data governance are listed in Table 2.5.
Data governance and MDM are a powerful combination. As data sources and volumes continue
to increase, so does the need to manage data as a strategic asset in order to extract its full
value. Making business data consistent, trusted, and accessible across the enterprise is a criti-
cal first step in customer-centric business models. With data governance, companies are able
to extract maximum value from their data, specifically by making better use of opportunities
that are buried within behavioral data.
TA B L E 2 . 5 Characteristics and Consequences of Weak or Nonexistent Data Governance
and MDM
• Data duplication causes isolated data silos.
• Inconsistency exists in the meaning and level of detail of data elements.
• Users do not trust the data and waste time verifying the data rather than analyzing them for appro-
priate decision-making.
• Leads to inaccurate data analysis.
• Bad decisions are made on perception rather than reality, which can negatively affect the company
and its customers.
• Results in increased workloads and processing time.
Questions
1. What is information management?
2. What is the “silo effect” and how does it affect business performance?
3. What three factors are driving collaboration and information sharing?
4. What are the business benefits of information management?
5. Explain why it is important to develop an effective data governance program?
6. Explain the purposes of master data management.
7. Why has interest in data governance and MDM increased?
48 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
2.4 Data Centers and Cloud Computing
Data centers and cloud computing are types of IT infrastructures or computing systems. Data
center also refers to the building or facility that houses the servers and equipment. In the past,
there were few IT infrastructure options. Companies owned their servers, storage, and network
components to support their business applications and these computing resources were on
their premises. Now, there are several choices for an IT infrastructure strategy―including cloud
computing. As is common to IT investments, each infrastructure configuration has strengths,
weaknesses, and cost considerations.
Data Centers
Traditionally, data and database technologies were kept in data centers that were typically run
by an in-house IT department (Figure 2.15) and consisted of on-premises hardware and equip-
ment that store data within an organization’s local area network.
Today, companies may own and manage their own on-premises data centers or pay for the
use of their vendors’ data centers, such as in cloud computing, virtualization, and software-as-
a-service arrangements (Figure 2.16).
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FIGURE 2.15 A row of network servers in a data center.
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FIGURE 2.16 Data centers are the infrastructure
underlying cloud computing, virtualization, networking,
security, delivery systems, and software-as-a-service.
Data Centers and Cloud Computing 49
In an on-premises data center connected to a local area network, it is easier to restrict
access to applications and information to authorized, company-approved people and equip-
ment. In the cloud, the management of updates, security, and ongoing maintenance are out-
sourced to a third-party cloud provider where data is accessible to anyone with the proper
credentials and Internet connection. This arrangement can make a company more vulnerable
since it increases exposure of company data at many more entry and exit points. Here are some
examples of data centers.
• National Climatic Data Center The National Climatic Data Center is an example of a
public data center that stores and manages the world’s largest archive of weather data.
• U.S. National Security Agency The National Security Agency’s (NSA) data center, shown
in Figure 2.17 is located in Bluffdale, UT. It is the largest spy data center for the NSA. People
who think their correspondence and postings through sites like Google, Facebook, and
Apple are safe from prying eyes should rethink that belief. You will read more about reports
exposing government data collection programs in Chapter 5.
• Apple Apple has a 500,000-square-foot data center in Maiden, NC, that houses servers for
various iCloud and iTunes services. The center plays a vital role in the company’s back-end
IT infrastructure. In 2014 Apple expanded this center with a new, smaller 14,250-square-
foot tactical data center that also includes office space, meeting areas, and breakrooms.
Since only the company owns the infrastructure, a data center is more suitable for organiza-
tions that run many different types of applications and have complex workloads. A data center,
like a factory, has limited capacity. Once it is built, the amount of storage and the workload the
center can handle does not change without purchasing and installing more equipment.
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FIGURE 2.17 The NSA data center in Bluffdale, UT.
When a Data Center Goes Down, so Does Business Data center failures dis-
rupt all operations regardless of who owns the data center. Here are two examples.
• Uber The startup company Uber experienced an hour-long outage in February 2014 that
brought its car-hailing service to a halt across the country. The problem was caused by an
outage at its vendor’s West Coast data center. Uber users flooded social media sites with
complaints about problems kicking off Uber’s app to summon a driver-for-hire.
• WhatsApp WhatsApp also experienced a server outage in early 2014 that took the ser-
vice offline for 2.5 hours. WhatsApp is a smartphone text-messaging service that had been
bought by Facebook for $19 billion. “Sorry we currently experiencing server issues. We
hope to be back up and recovered shortly,” WhatsApp said in a message on Twitter that
was retweeted more than 25,000 times in just a few hours. The company has grown rapidly
to 450 million active users within five years, nearly twice as many as Twitter. More than
two-thirds of these global users use the app daily. WhatsApp’s server failure drove millions
of users to a competitor. Line, a messaging app developed in Japan, added 2 million new
registered users within 24 hours of WhatsApp’s outage―the biggest increase in Line’s user
base within a 24-hour period.
50 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
These outages point to the risks of maintaining the complex and sophisticated technology
needed to power digital services used by millions or hundreds of millions of people.
Integrating Data to Combat Data Chaos
An enterprise’s data are stored in many different or remote locations―creating data chaos
at times. And some data may be duplicated so that they are available in multiple locations
that need a quick response. Therefore, the data needed for planning, decision-making, opera-
tions, queries, and reporting are scattered or duplicated across numerous servers, data cent-
ers, devices, and cloud services. Disparate data must be unified or integrated in order for the
organization to function.
Data Virtualization As organizations have transitioned to a cloud-based infrastruc-
ture, data centers have become virtualized. For example, Cisco offers data virtualization, which
gives greater IT flexibility. The process of data virtualization involves abstracting, transforming,
merging, and delivering data from disparate sources. The main goal of data virtualization is to
provide a single point of access to the data. By aggregating data from a wide range of sources
users can access applications without knowing their exact location. Using data virtualization
methods, enterprises can respond to change more quickly and make better decisions in real
time without physically moving their data, which significantly cuts costs. Cisco Data Virtualiza-
tion makes it possible to:
• Have instant access to data at any time and in any format.
• Respond faster to changing data analytics needs.
• Cut complexity and costs.
Compared to traditional (nonvirtual) data integration and replication methods, data virtu-
alization accelerates time to value with:
• Greater agility Speeds 5–10 times faster than traditional data integration methods
• Streamlined approach 50–75% time savings over data replication and consolida-
tion methods
• Better insight Instant access to data
Software-Defined Data Center Data virtualization has led to the latest development
in data centers—the software-defined data center (SDDC). An SDDC facilitates the integration
of the various infrastructures of the SDDC silos within organizations and optimizes the use
of resources, balances workloads, and maximizes operational efficiency by dynamically dis-
tributing workloads and provisioning networks. The goal of the SDDC is to decrease costs
and increase agility, policy compliance, and security by deploying, operating, managing, and
maintaining applications. In addition, by providing organizations with their own private cloud,
SDDCs provide greater flexibility by allowing organizations to have on-demand access to their
data instead of having to request permission from their cloud provider (see Figure 2.18).
The base resources for the SDDC are computation, storage, networking, and security. Typi-
cally, the SDDC includes limited functionality of service portals, applications, OSs, VM hardware,
hypervisors, physical hardware, software-defined networking, software-defined storage, a
security layer, automation and management layers, catalogs, a gateway interface module, and
third-party plug-ins (Figure 2.19).
It is estimated that the market share for SDDCs will grow from the current level of $22
billion to more than $77 billion in the next five years. As the use of SDDCs grows at this extraor-
dinary rate, data center managers will be called upon to scale their data centers exponentially
at a moment’s notice. Unfortunately, this is impossible to achieve using the traditional data
center infrastructure. In the SDDC, software placement and optimization decisions are based
on business logic, not technical provisioning directives. This requires changes in culture,
Data Centers and Cloud Computing 51
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FIGURE 2.18 Corporate IT infrastructures can consist of an on-premises data center
and off-premises cloud computing.
Catalogs
3rd
Party
Plug-Ins
Gateway
Interface
Module
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Software-defined Networking
Software-defined Storage
Physical & Virtual
Compute
Limited Functionality of Service Portals
FIGURE 2.19 SDDC infrastructure (adapted from Sturm et al., 2017).
processes, structure, and technology. The SDDC isolates the application layer from the physical
infrastructure layer to facilitate faster and more effective deployment, management, and moni-
toring of diverse applications. This is achieved by finding each enterprise application an optimal
home in a public or private cloud environment or draw from a diverse collection of resources.
From a business perspective moving to a SDDC is motivated by the need to improve secu-
rity, increase alignment of the IT infrastructure with business objectives and provision of appli-
cations more quickly.
52 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
Traditional data centers had dedicated, isolated hardware that results in poor utilization
of resources and very limited flexibility. Second-generation virtualization data cases improved
resource use by consolidating virtualized servers. By reducing the steps needed to decrease
the time it takes to deploy workloads, facilitating the definition of applications and resource
needs, the SDDC creates an even more flexible environment in which enterprise applications
can be quickly reconfigured and supported to provide infrastructure-as a service (IaaS). Tran-
sitioning to an SDDC enables an organization to optimize its resource usage, provide capacity
on demand, improve business-IT alignment, improve agility and flexibility of operations, and
save money (Figure 2.20).
TRADITIONAL DATA
CENTER
• Hardware silos
• Limited utilization
• Limited flexibility
VIRTUALIZED DATA CENTER
• Virtualized servers
• Better resource use
• Automate/Balance workload
• Sub-optimal
performance
SOFTWARE-DEFINED
DATA CENTER (SDDC)
• IaaS
• Optimized resource use
• Increased business-
IT alignment
• Improved agility & flexibility
• Capacity on demand
• Cost savings
FIGURE 2.20 Evolution of data centers (adapted from Sturm et al., 2017).
Cloud Computing
In a business world where first movers gain the advantage, IT responsiveness and agility pro-
vide a competitive edge and lead to sustainable business practices. Yet, many IT infrastructures
are extremely expensive to manage and too complex to easily adapt. A common solution is
cloud computing. Cloud computing is the general term for infrastructures that use the Inter-
net and private networks to access, share, and deliver computing resources. More specifically,
IBM defines cloud computing as “the delivery of on-demand computing resources—everything
from applications to data centers—over the Internet on a pay-for-use basis” (IBM, 2016).
Cloud computing is the delivery of computing and storage resources as a service to end-users
over a network. Cloud systems are scalable. That is, they can be adjusted to meet changes in
business needs. At the extreme, the cloud’s capacity is unlimited depending on the vendor’s offer-
ings and service plans. A drawback of the cloud is control because a third party manages it. Unless
the company uses a private cloud within its network, it shares computing and storage resources
with other cloud users in the vendor’s public cloud. Public clouds allow multiple clients to access
the same virtualized services and utilize the same pool of servers across a public network. In con-
trast, private clouds are single-tenant environments with stronger security and control for reg-
ulated industries and critical data. In effect, private clouds retain all the IT security and control
provided by traditional IT infrastructures with the added advantages of cloud computing.
Selecting a Cloud Vendor
Because cloud is still a relatively new and evolving business model, the decision to select a
cloud service provider should be approached with even greater diligence than other IT deci-
sions. As cloud computing becomes an increasingly important part of the IT delivery model,
assessing and selecting the right cloud provider also become the most strategic decisions that
business leaders undertake. Providers are not created equally, so it is important to investigate
each provider’s offerings prior to subscribing. When selecting and investing in cloud services,
there are several service factors a vendor needs to address. These evaluation factors are listed
in Table 2.6.
Data Centers and Cloud Computing 53
Vendor Management and Cloud Service Agreements (CSAs) The move to
the cloud is also a move to vendor-managed services and cloud service agreements (CSAs).
Also referred to as cloud service level agreements (SLAs), the CSA or SLA is a negotiated
agreement between a company and service provider that can be a legally binding contract or
an informal contract. You can review a sample CSA used by IBM by visiting http://www-05.
ibm.com/support/operations/files/pdf/csa_us .
Staff experienced in managing outsourcing projects may have the necessary expertise for
managing work in the cloud and policing SLAs with vendors. The goal is not building the best CSA
terms, but negotiating the terms that align most closely with the business needs. For example, if
a server becomes nonoperational and it does not support a critical business operation, it would
not make sense to pay a high premium for reestablishing the server within one hour. On the
other hand, if the data on the server support a business process that would effectively close
down the business for the period of time that it was not accessible, it would be prudent to nego-
tiate the fastest possible service in the CSA and pay a premium for that high level of service.
In April 2015, the Cloud Standards Customer Council (CSCC) published the Practical Guide
to Cloud Service Agreements, Version 2.0, to reflect changes that have occurred since 2012 when
it first published the Practical Guide to Cloud Service Level Agreements. The new guide provides
a practical reference to help enterprise IT and business decision-makers analyze CSAs from dif-
ferent cloud service providers. The main purpose of a CSA is to set clear expectations for service
between the cloud customer (buyer) and the cloud provider (seller), but CSAs should also exist
between a customer and other cloud entities, such as the cloud carrier, the cloud broker, and
even the cloud auditor. Although the various service delivery models, that is, IaaS, PaaS, SaaS,
and so on, may have different requirements, the guide focuses on the requirements that are
common across the various service models (Cloud Standards Customer Council, 2015, p. 4).
Implementing an effective management process is an important step in ensuring internal
and external user satisfaction with cloud services. Table 2.7 lists the 10 steps that should be
taken by cloud customers to evaluate cloud providers’ CSAs in order to compare CSAs across
multiple providers or to negotiate terms with a selected provider.
TA B L E 2 . 6 Service Factors to Consider when Evaluating Cloud Vendors or Service
Providers
Factors Examples of Questions to Be Addressed
Delays What are the estimated server delays and network delays?
Workloads What is the volume of data and processing that can be handled
during a specific amount of time?
Costs What are the costs associated with workloads across multiple
cloud computing platforms?
Security How are data and networks secured against attacks? Are data
encrypted and how strong is the encryption? What are network
security practices?
Disaster recovery and business
continuity
How is service outage defined? What level of redundancy is in
place to minimize outages, including backup services in differ-
ent geographical regions? If a natural disaster or outage occurs,
how will cloud services be continued?
Technical expertise and
understanding
Does the vendor have expertise in your industry or business
processes? Does the vendor understand what you need to do
and have the technical expertise to fulfill those obligations?
Insurance in case of failure Does the vendor provide cloud insurance to mitigate user
losses in case of service failure or damage? This is a new and
important concept.
Third-party audit or an unbiased
assessment of the ability to rely on
the service provided by the vendor
Can the vendor show objective proof with an audit that it can
live up to the promises it is making?
54 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
Cloud Infrastructure
The cloud has greatly expanded the options for enterprise IT infrastructures because any device
that accesses the Internet can access, share, and deliver data. Cloud computing is a valuable
infrastructure because:
1. It is dynamic, not static and provides a way to make applications and computing power
available on demand. Applications and power are available on demand because they are
provided as a service. For example, any software that is provided on demand is referred
to as software as a service (SaaS). Typical SaaS products are Google Apps and www.
Salesforce.com. Section 2.5 discusses SaaS and other cloud services.
2. Helps companies become more agile and responsive while significantly reducing IT costs
and complexity through improved workload optimization and service delivery.
Move to Enterprise Clouds A majority of large organizations have hundreds or thou-
sands of software licenses that support business processes, such as licenses for Microsoft
Office, Oracle database management, IBM CRM (customer relationship management), and var-
ious network security software. Managing software and their licenses involves deploying, provi-
sioning, and updating them―all of which are time-consuming and expensive. Cloud computing
overcomes these problems.
Issues in Moving Workloads from the Enterprise
to the Cloud
Building a cloud strategy is a challenge, and moving existing applications to the cloud is stress-
ful. Despite the business and technical benefits, the risk exists of disrupting operations or cus-
tomers in the process. With the cloud, the network and WAN (wide area network) become an
even more critical part of the IT infrastructure. Greater network bandwidth is needed to sup-
port the increase in network traffic. And, putting part of the IT architecture or workload into
the cloud requires different management approaches, different IT skills, and knowing how to
manage vendor relationships and contracts.
TA B L E 2 . 7 Ten Steps to Evaluate a CSA
1. Understand roles and responsibilities of the CSA customer and provider
2. Evaluate business-level policies and compliance requirements relevant to the CSA customer
3. Understand service and deployment model differences
4. Identify critical performance objectives such as availability, response time, and processing speed.
Ensure they are measurable and auditable
5. Evaluate security and privacy requirements for customer information that has moved into the
provider’s cloud and applications, functions, and services being operated in the cloud to provide
required service to the customer
6. Identify service management requirements such as auditing, monitoring and reporting,
measurement, provisioning, change management, and upgrading/patching
7. Prepare for service failure management by explicitly documenting cloud service capabilities and
performance expectations with remedies and limitations for each
8. Understand the disaster recovery plan
9. Develop a strong and detailed governance plan of the cloud services on the customer side
10. Understand the process to terminate the CSA
Cloud Services and Virtualization 55
Infrastructure Issues There is a big difference because cloud computing runs on a
shared infrastructure, so the arrangement is less customized to a specific company’s require-
ments. A comparison to help understand the challenges is that outsourcing is like renting an
apartment, while the cloud is like getting a room at a hotel.
With cloud computing, it may be more difficult to get to the root of performance problems,
like the unplanned outages that occurred with Google’s Gmail and Workday’s human resources
apps. The trade-off is cost versus control.
Increasing demand for faster and more powerful computers, and increases in the number
and variety of applications are driving the need for more capable IT architectures.
Questions
1. What is a data center?
2. What is the difference between on-premises data centers and cloud computing?
3. What is an SDDC?
4. What are the advantages of using an SDDC?
5. How can cloud computing solve the problems of managing software licenses?
6. What factors should be considered when selecting a cloud vendor or provider?
7. When are private clouds used instead of public clouds?
8. Explain three issues that need to be addressed when moving to cloud computing or services.
2.5 Cloud Services and Virtualization
Managers want streamlined, real-time, data-driven enterprises, yet they may face budget cuts.
Sustaining performance requires the development of new business applications and analytics
capabilities, which comprise the front end and the data stores and digital infrastructure, or back
end, to support them. The back end is where the data reside. The problem is that data may have
to navigate through a congested IT infrastructure that was first designed decades ago. These
network or database bottlenecks can quickly wipe out the competitive advantages from big
data, mobility, and so on. Traditional approaches to increasing database performance―manu-
ally tuning databases, adding more disk space, and upgrading processors―are not enough
when you are you are dealing with streaming data and real-time big data analytics. Cloud ser-
vices help to overcome these limitations. Cloud services are outsourced to a third-party cloud
provider who manages the updates, security, and ongoing maintenance.
At first glance, virtualization and cloud computing may appear to be quite similar. How-
ever, cloud computing and virtualization are inherently different. Unlike cloud computing that
involves multiple computers or hardware devices sending data through vendor-provided net-
works, virtualization is the replacement of a tangible physical component with a virtual one.
Each of these concepts are described and discussed in the following sections.
Anything as a Service (XAAS) Models
The cloud computing model for on-demand delivery of and access to various types of com-
puting resources also extends to the development of business apps. Figure 2.21 shows four
“as a service” (XaaS) solutions based on the concept that the resource―software, platform,
infrastructure, or data—can be provided on demand regardless of geolocation. As these as ser-
vice solutions develop, the focus is changing from massive technology implementation costs to
business-reengineering programs that enable XaaS platforms (Fresht, 2014).
Cloud services are services made available to users on demand via the Internet from
a cloud computing provider’s servers instead of being accessed through an organization’s
56 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
on-premises servers. Cloud services are designed to provide easy, scalable access to applica-
tions, resources, and services, and are fully managed by a cloud services provider.
Cloud computing is often referred to as a “stack” or broad range of services built on top of
each other under the name cloud. These cloud services can be defined as follows:
• Software as a service (SaaS) is a widely used model in which software is available to users
from a service provider as needed. A provider licenses a SaaS application to customers as
an on-demand service, through a subscription, a pay-as-you-go model, or free of charge
(where revenue can be generated by other means, such as through sale of advertisements).
• Platform as a service (PaaS) is a computing platform that enables the quick and easy
creation, testing, and deployment of web applications without the necessity of buying and
maintaining the software and infrastructure underneath it. It is a set of tools and services
that make coding and deploying these applications faster and more efficient.
• Infrastructure as a service (IaaS) is a way of delivering servers, storage, networks, work-
load balancers, and OSs as an on-demand service.
• Data as a service (DaaS) is an information provision and distribution model in which data
files (including text, images, sounds, and videos) are made available to customers over a
network by a service provider.
Software as a Service (SaaS) SaaS is a rapidly growing method of delivering soft-
ware and is particularly useful in applications in which there are considerable interactions bet-
ween the organization and external entities that do not confer a competitive advantage, for
example, e-mail and newsletters. It is also useful when an organization is going to be needing
a particular type of software for a short period of time or for a specific project, and for software
that is used periodically, for example, tax, payroll, or billing software. SaaS is not appropriate
for accessing applications that require fast processing of real-time data or applications where
regulation does not permit data being hosted externally.
Other terms for SaaS are on-demand computing and hosted services. The idea is basically
the same: Instead of buying and installing expensive packaged enterprise applications, users
can access software applications over a network, using an Internet browser. To use SaaS, a
service provider hosts the application at its data center and customers access it via a standard
Web browser.
The SaaS model was developed to overcome the common challenge to an enterprise of
being able to meet fluctuating demands on IT resources efficiently. It is used in many business
functions, primarily customer relationship management (CRM), accounting, human resources
(HR), service desk management, communication, and collaboration.
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FIGURE 2.21 Four as a service solutions: software, platform,
infrastructure, and data as a service.
Cloud Services and Virtualization 57
There are thousands of SaaS vendors. www.Salesforce.com is one of the most widely
known SaaS providers. Other examples are Google Docs and collaborative presentation soft-
ware Prezi. For instance, instead of installing Microsoft Word on your own computer, and
then loading Word to create a document, you use a browser to log into Google Docs. Only the
browser uses your computer’s resources.
Platform as a Service (PaaS) PaaS provides a standard unified platform for devel-
oping, testing, and deploying software over the Web. This computing platform allows the
creation of Web applications quickly and easily without the complexity of buying and main-
taining the underlying infrastructure. Without PaaS, the cost of developing some applications
would be prohibitive. Examples of PaaS include databases, Web servers, development tools,
and execution runtime. PaaS is particularly useful when multiple software developers are
working on a software development project of when other external parties need to interact
with the development process and for when developers want to automate testing and deploy-
ment services. It is less useful in those instances where application performance needs to be
customized to the underlying hardware and software or an application needs to be highly por-
table in terms of where it is hosted. Some examples of PaaS include Microsoft Azure Service,
www.Force.com, and Google App Engine.
Infrastructure as a Service (IaaS) Rather than purchasing all the components of
its IT infrastructure, organizations buy their computing resources as a fully outsourced Infra-
structure as a Service (IaaS) on demand. Generally, IaaS can be acquired as a Public or Private
infrastructure or a combination of the two (Hybrid). A public IaaS is one that consists of shared
resources deployed on a self-service basis over the Internet. On the other hand, a private IaaS is
provided on a private network. And, a hybrid IaaS is a combination of both public and private.
IaaS is useful where organizations experience significant highs and lows in terms of demand on
the infrastructure, for new or existing organizations who have budgetary constraints on hardware
investment and in situations where an organization has temporary infrastructure needs. Some
IaaS providers you may be familiar with include Amazon Web Services (AWS) and Rackspace.
Data as a Service (DaaS)—The New Kid on the Block DaaS is the newest
entrant into the XaaS arena. DaaS enables data to be shared among clouds, systems, apps, and
so on regardless of the data source or where they are stored. Data files, including text, images,
sound, and video, are made available to customers over a network, typically the Internet. DaaS
makes it easier for data architects to select data from different pools, filter out sensitive data,
and make the remaining data available on demand.
A key benefit of DaaS is that it transfers the risks and responsibilities associated with data
management to a third-party cloud provider. Traditionally, organizations stored and managed
their data within a self-contained storage system, however, as data become more complex, it is
increasingly difficult and expensive to maintain using the traditional data model. Using DaaS,
organizational data are readily accessible through a cloud-based platform and can be delivered
to users despite organizational or geographical constraints. This model is growing in popularity
as data become more complex, difficult, and expensive to maintain. Some of the most common
business applications currently using DaaS are CRM and enterprise resource planning (ERP).
For an example of Daas, see IT at Work 2.3.
IT at Work 2.3
Slack
Slack, the successful social chat app for companies and their
executives and/or employees, has announced a “deep product
partnership” with Salesforce (Lunden, 2016). The partnership
includes a new data sharing platform for businesses to easily share
information about conversations they are having within the app.
More specifically, businesses will be able to share details about
client accounts in real time with automatic updates for new leads
about the accounts. The new partnership will allow Slack and its
users to be even more effective in collaboration and data sharing
across many platforms and departments (Lunden, 2016).
58 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
As a Service Models Are Enterprisewide and Can Trigger Lawsuits The
various As a Service models are used in various aspects of business. You will read how these
specific services, such as CRM and HR management, are being used for operational and strategic
purposes in later chapters. Companies are frequently adopting software, platform, infrastructure,
data management, and starting to embrace mobility as a service and big data as a service because
they typically no longer have to worry about the costs of buying, maintaining, or updating their
own data servers. Both hardware and human resources expenses can be cut significantly. Ser-
vice arrangements all require that managers understand the benefits and trade-offs―and how
to negotiate effective SLAs and CSAs. Regulations mandate that confidential data be protected
regardless of whether the data are on-premises or in the cloud. Therefore, a company’s legal
department needs to get involved in these IT decisions. Put simply, moving to cloud services is
not simply an IT decision because the stakes around legal and compliance issues are very high.
Going Cloud
Cloud services can advance the core business of delivering superior services to optimize busi-
ness performance. Cloud can cut costs and add flexibility to the performance of critical busi-
ness apps. And, it can improve responsiveness to end-consumers, application developers, and
business organizations. But to achieve these benefits, there must be IT, legal, and senior man-
agement oversight because a company still must meet its legal obligations and responsibilities
to employees, customers, investors, business partners, and society.
Virtualization and
Virtual Machines
There are many types of virtualization, such as virtual storage devices, virtual desktops, virtual
OSs, and virtual servers for network virtualization. You can think of virtualization as a model for
a physical component that is built into computer code, to create a software program that acts
in the same way as the physical component it is modeling. For example, a virtual machine is
a software representation of a computer, rather than an actual computer and a virtual server
sends and receives signals just like a physical one, even though it doesn’t have its own circuitry
and other physical components.
You might ask why organizations want to virtualize their physical computing and net-
working devices. The answer is a gross underutilization of inefficient use of resources. Computer
hardware had been designed to run a single OS and a single app, which leaves most computers
vastly underutilized. Virtualization is a technique that creates a virtual (i.e., nonphysical) layer
and multiple virtual machines (VMs) to run on a single physical machine. The virtual (or virtual-
ization) layer makes it possible for each VM to share the resources of the hardware. Figure 2.22
shows the relationship among the VMs and physical hardware.
Application
Virtualization Layer
Hardware Layer
Operating
System
Application
Operating
System
Application
Operating
System
Virtual Machines
FIGURE 2.22 Virtual machines running on a
simple computer hardware layer.
Cloud Services and Virtualization 59
What Is a Virtual Machine? Just as virtual reality is not real, but a software-created
world, a virtual machine is a software-created computer. Technically, a virtual machine
(VM) is created by a software layer, called the virtualization layer, as shown in Figure 2.22.
That layer has its own Windows or other OS and apps, such as Microsoft Office, as if it
were an actual physical computer. A VM behaves exactly like a physical computer and con-
tains its own virtual―that is, software-based―CPU, RAM (random access memory), hard
drive, and network interface card (NIC). An OS cannot tell the difference between a VM
and a physical machine, nor can applications or other computers on a network tell the
difference. Even the VM thinks it is a “real” computer. Users can set up multiple real com-
puters to function as a single PC through virtualization to pool resources to create a more
powerful VM.
Virtualization is a concept that has several meanings in IT and therefore several defini-
tions. The major type of virtualization is hardware virtualization, which remains popular and
widely used. Virtualization is often a key part of an enterprise’s disaster recovery plan. In gen-
eral, virtualization separates business applications and data from hardware resources. This
separation allows companies to pool hardware resources―rather than dedicate servers to
applications―and assign those resources to applications as needed.
Different types of virtualization include:
• Storage virtualization is the pooling of physical storage from multiple network storage
devices into what appears to be a single storage device managed from a central
console.
• Server virtualization consolidates multiple physical servers into virtual servers that run on
a single physical server.
• Desktop virtualization is software technology that separates the desktop environ-
ment and associated application software from the physical machine that is used to
access it.
• Application virtualization is the practice of running software from a remote server rather
than on the user’s computer.
• Network virtualization combines the available resources in a network by splitting the
network load into manageable parts, each of which can be assigned (or reassigned) to a
particular server on the network.
• Hardware virtualization is the use of software to emulate hardware or a total computer
environment other than the one the software is actually running in. It allows a piece of
hardware to run multiple OS images at once. This kind of software is sometimes known as
a virtual machine.
Virtualization Characteristics and Benefits Virtualization increases the flexi-
bility of IT assets, allowing companies to consolidate IT infrastructure, reduce maintenance
and administration costs, and prepare for strategic IT initiatives. Virtualization is not primarily
about cost-cutting, which is a tactical reason. More importantly, for strategic reasons, virtual-
ization is used because it enables flexible sourcing and cloud computing.
The characteristics and benefits of virtualization are as follows:
1. Memory-intensive VMs need a huge amount of RAM (random access memory, or pri-
mary memory) because of their massive processing requirements.
2. Energy-efficient VMs minimize energy consumed running and cooling servers in the
data center―representing up to a 95% reduction in energy use per server.
3. Scalability and load balancing When a big event happens, such as the Super Bowl,
millions of people go to a website at the same time. Virtualization provides load balanc-
ing to handle the demand for requests to the site. The VMware infrastructure automati-
cally distributes the load across a cluster of physical servers to ensure the maximum
performance of all running VMs. Load balancing is key to solving many of today’s IT
challenges.
60 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
Virtualization consolidates servers, which reduces the cost of servers, makes more effi-
cient use of data center space, and reduces energy consumption. All of these factors reduce the
total cost of ownership (TCO). Over a three-year life cycle, a VM costs approximately 75% less to
operate than a physical server. IT at Work 2.4 describes one example of how virtualization can
help organizations provide higher levels of customer service and improve productivity.
Key Terms
ad hoc report 34
batch processing 33
cloud computing 52
cloud service agreements (CSAs) 53
customer-centric 47
data 30
data as a service (DaaS) 56
data center 38
data governance 46
data silo 43
database 33
decision support systems (DSS) 32
dirty data 26
enterprise architecture (EA) 26
exception report 34
executive information systems (EISs) 32
goal seeking 35
information 30
information management 42
information systems (ISs) 28
infrastructure as a service (IaaS) 56
IT infrastructure 38
IPOS 28
knowledge 30
management information systems (MIS) 34
master data 46
master data management (MDM) 47
IT at Work 2.4
Business Continuity with Virtualization
Liberty Wines supplies to restaurants, supermarkets, and indepen-
dent retailers from its headquarters in central London. Recipient
of multiple international wine awards—including the Interna-
tional Wine Challenge on Trade Supplier of the Year for two years
running—Liberty Wines is one of the United Kingdom’s foremost
wine importers and distributors.
IT Problems and Business Needs
As the business expanded, the existing servers did not have the
capacity to handle increased data volumes, and maintenance of
the system put a strain on the IT team of two employees. Existing
systems were slow and could not provide the responsiveness that
employees expected.
Liberty Wines had to speed up business processes to meet
the needs of customers in the fast-paced world of fine dining. To
provide the service their customers expect, employees at Liberty
Wines needed quick and easy access to customer, order, and stock
information. In the past, the company relied on 10 physical servers
for applications and services, such as order processing, reporting,
and e-mail.
Virtualized Solution
Liberty Wines deployed a virtualized server solution incorporating
Windows Server 2008 R2. The 10 servers were replaced with 3 physical
servers, running 10 virtual servers. An additional server was used as
part of a backup system, further improving resilience and stability.
By reducing the number of physical servers from 10 to 4, power
use and air conditioning costs were cut by 60%. Not only was the
bottom line improved, but the carbon footprint was also reduced,
which was good for the environment.
The new IT infrastructure cut hardware replacement costs by
£45,000 (U.S. $69,500) while enhancing stability with the backup
system. Applications now run faster, too, so employees can pro-
vide better customer service with improved productivity. When
needed, virtual servers can be added quickly and easily to support
business growth.
Questions
1. What is SaaS?
2. What is PaaS?
3. What is IaaS?
4. How might companies risk violating regulation or compliance requirements with cloud services?
5. In what ways is a virtualized information system different from a traditional information system?
6. Describe the different types of virtualization.
7. What is load balancing and why is it important?
Assuring Your Learning 61
master file 47
model 26
online transaction processing (OLTP) 33
platform as a service (PaaS) 56
private cloud 52
public cloud 52
real-time processing 33
service level agreement (SLA) 61
software as a service (SaaS) 54
software-defined data center (SDDC) 50
stack 56
structured decisions 35
transaction processing systems (TPS) 32
unstructured decisions 35
virtualization 59
virtual machine (VM) 59
what-if analysis 35
wisdom 31
Assuring Your Learning
Discuss: Critical Thinking Questions
1. Why is a strong market position or good profit performance only
temporary?
2. Assume you had:
a. A tall ladder with a sticker that lists a weight allowance only
five pounds more than you weigh. You know the manufacturer
and model number.
b. Perishable food with an expiration date two days into the
future.
c. A checking account balance that indicates you have sufficient
funds to cover the balance due on an account.
In all three cases, trusting the data to be exactly correct could have
negative consequences. Explain the consequences of trusting the data
in each instance. How might you determine the correct data for each
instance? Which data might not be possible to verify? How does dirty data
impact your decision-making?
3. If business data are scattered throughout the enterprise and not
synched until the end of the month, how does that impact day-to-day
decision-making and planning?
4. Assume a bank’s data are stored in silos based on financial
product―checking accounts, saving accounts, mortgages, auto
loans, and so on. What problems do these data silos create for
the bank’s managers?
5. Why do managers and workers still struggle to find information that
they need to make decisions or take action despite advances in digital
technology? That is, what causes data deficiencies?
6. According to a Tech CEO Council Report, Fortune 500 companies
waste $480 billion every year on inefficient business processes. What
factors cause such huge waste? How can this waste be reduced?
7. Explain why organizations need to implement EA and data
governance.
8. What two problems can EA solve?
9. Name two industries that depend on data governance to comply
with regulations or reporting requirements. Given an example of each.
10. Why is it important for data to be standardized? Give an example
of unstandardized data.
11. Why are TPSs critical systems?
12. Discuss why the cloud acts as the great IT delivery frontier.
13. What are the functions of data centers?
14. What factors need to be considered when selecting a cloud vendor?
15. What protection does an effective SLA or CSA provide?
16. Why is an SLA or a CSA a legal document?
17. How can virtualization reduce IT costs while improving performance?
Explore: Online and Interactive Exercises
1. When selecting a cloud vendor to host your enterprise data and
apps, you need to evaluate the service level agreement (SLA).
a. Research the SLAs of two cloud vendors, such as Rackspace,
Amazon, or Google.
b. For the vendors you selected, what are the SLAs’ uptime per-
centages? Expect them to be 99.9% or less.
c. Does each vendor count both scheduled downtime and
planned downtime toward the SLA uptime percentage?
d. Compare the SLAs in terms of two other criteria.
e. Decide which SLA is better based on your comparisons.
f. Report your results and explain your decision.
2. Many organizations initiate data governance programs because of
pressing compliance issues that impact data usage. Organizations may
need data governance to be in compliance with one or more regula-
tions, such as the Gramm−Leach Bliley Act (GLB), HIPAA, Foreign Cor-
rupt Practices Act (FCPA), Sarbanes−Oxley Act, and several state and
federal privacy laws.
a. Research and select two U.S. regulations or privacy laws.
b. Describe how data governance would help an enterprise com-
ply with these regulations or laws.
3. Visit www.eWeek.com Cloud Computing Solutions Center for news
and reviews at www.eweek.com/c/s/Cloud-Computing. Select one
of the articles listed under Latest Cloud Computing News. Prepare an
executive summary of the article.
4. Visit Rackspace.com and review the company’s three types of cloud
products. Describe each of those cloud solutions.
62 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
5. Visit Oracle.com. Describe the types of virtualization services of-
fered by Oracle.
6. Visit YouTube.com and search for two videos on virtualization. For
each video, report what you learned. Specify the complete URL, video
title, who uploaded the video and the date, video length, and num-
ber of views.
Case 2.2
Business Case: Data Chaos Creates Risk
Data chaos often runs rampant in service organizations, such as health
care and the government. For example, in many hospitals, each line
of business, division, and department has implemented its own IT
applications, often without a thorough analysis of its relationship with
other departmental or divisional systems. This arrangement leads
to the hospital having IT groups that specifically manage a particu-
lar type of application suite or data silo for a particular department
or division.
Data Management
When applications are not well managed, they can generate terabytes
of irrelevant data, causing hospitals to drown in such data. This data
chaos could lead to medical errors. In the effort to manage excessive
and massive amounts of data, there is increased risk of relevant infor-
mation being lost (missing) or inaccurate—that is, faulty or dirty data.
Another risk is data breaches.
• Faulty data By 2015, 96% of health-care organizations had
adopted electronic health records, or EHRs (Office of the National
Coordinator for HIT, 2016). It is well known that an unintended
consequence of EHR is faulty data. According to a study pub-
lished in the Journal of the American Medical Association, data in
EHR systems may not be as accurate and complete as expected
(Conn, 2016). Incorrect lab values, imaging results, or physi-
cian documentation lead to medical errors, harm patients, and
damage the organization’s accreditation and reputation.
• Data breaches More than 25 million people have been affected
by health-care system data breaches since the Office for Civil
Rights, a division of the U.S. Department of Health and Human
Services, began reporting breaches in 2009. Most breaches
involved lost or stolen data on laptops, removable drives, or
other portable media. Breaches are extremely expensive and
destroy trust.
Accountability in health-care demands compliance with strong
data governance efforts. Data governance programs verify that data
input into EHR, clinical, financial, and operational systems are accu-
rate and complete—and that only authorized edits can be made
and logged.
Vanderbilt University Medical Center Adopts EHR
and Data Governance
Vanderbilt University Medical Center (VUMC) in Nashville, TN, was
an early adopter of EHR and implemented data governance in 2009.
VUMC’s experience provides valuable lessons.
VUMC consists of three hospitals and the Vanderbilt Clinic, which
have 918 beds, discharge 53,000 patients each year, and count 1.6 mil-
lion clinic visits each year. On average, VUMC has an 83% occupancy
rate and has achieved HIMSS Stage 6 hospital EHR adoption. HIMSS
(Healthcare Information and Management Systems Society, himss.org)
is a global, nonprofit organization dedicated to better health-care out-
comes through IT. There are seven stages of EHR adoption, with Stage
7 being a fully paperless environment. That means all clinical data are
part of an electronic medical record and, as a result, can be shared
Analyze & Decide: Apply IT Concepts to Business Decisions
1. Financial services firms experience large fluctuations in business
volumes because of the cyclical nature of financial markets. These
fluctuations are often caused by crises―such as the subprime mort-
gage problems, the discovery of major fraud, or a slowdown in the
economy. These fluctuations require that executives and IT leaders
have the ability to cut spending levels in market downturns and
quickly scale up when business volumes rise again. Research SaaS
solutions and vendors for the financial services sector. Would invest-
ment in SaaS help such firms align their IT capacity with their business
needs and also cut IT costs? Explain your answer.
2. Despite multimillion-dollar investments, many IT organizations
cannot respond quickly to evolving business needs. Also, they cannot
adapt to large-scale shifts like mergers, sudden drops in sales, or
new product introductions. Can cloud computing help organizations
improve their responsiveness and get better control of their IT costs?
Explain your answer.
3. Describe the relationship between enterprise architecture and
organizational performance.
4. Identify four KPIs for a major airline (e.g., American, United, Delta)
or an automobile manufacturer (e.g., GM, Ford, BMW). Which KPI
would be the easiest to present to managers on an online dashboard?
Explain why.
Case 2.3 63
across and outside the enterprise. At Stage 7, the health-care organi-
zation is getting full advantage of the health information exchange
(HIE). HIE provides interoperability so that information can flow back
and forth among physicians, patients, and health networks (NextGen
Healthcare, 2016).
VUMC began collecting data as part of its EHR efforts in 1997. By
2009, the center needed stronger, more disciplined data management. At
that time, hospital leaders initiated a project to build a data governance
infrastructure.
Data Governance Implementation
VUMC’s leadership team had several concerns.
1. IT investments and tools were evolving rapidly, but they were
not governed by HIM (Healthcare Information and Manage-
ment) policies.
2. As medical records became electronic so they might be trans-
mitted and shared easily, they became more vulnerable
to hacking.
3. As new uses of electronic information were emerging, the medi-
cal center struggled to keep up.
Health Record Executive Committee
Initially, VUMC’s leaders assigned data governance to their traditional
medical records committee, but that approach failed. Next, they hired
consultants to help develop a data governance structure and organ-
ized a health record executive committee to oversee the project. The
committee reports to the medical board and an executive commit-
tee to ensure executive involvement and sponsorship. The commit-
tee is responsible for developing the strategy for standardizing health
record practices, minimizing risk, and maintaining compliance. Mem-
bers include the chief medical information officer (CMIO), CIO, legal
counsel, medical staff, nursing informatics, HIM, administration, risk
management, compliance, and accreditation. In addition, a legal
medical records team was formed to support additions, corrections,
and deletions to the EHR. This team defines procedures for removal of
duplicate medical record numbers and policies for data management
and compliance.
Costs of Data Failure
Data failures incur the following costs:
• Rework
• Loss of business
• Patient safety errors
• Malpractice lawsuits
• Delays in receiving payments because billing or medical codes
data are not available.
Benefits Achieved from Data Governance
As in other industries, in health care, data are the most valuable asset.
The handling of data is the real risk. EHRs are effective only if the data
are accurate and useful to support patient care. Effective ongoing data
governance has achieved that goal at VUMC.
Questions
1. What might happen when each line of business, division, and
department develops its own IT apps?
2. What are the consequences of poorly managed apps?
3. What two risks are posed by data chaos? Explain why.
4. What are the functions of data governance in the health-care
sector?
5. Why is it important to have executives involved in data gover-
nance projects?
6. List and explain the costs of data failure.
7. Why are data the most valuable asset in health care?
Case 2.3
Video Case: Cloud Computing at Coca-Cola Is
Changing Everything
When organizations say they are “using the cloud,” they can mean
a number of very different things. Using an IaaS service such as
Amazon EC2 or Terremark is different from using Google Apps to
outsource e-mail, which is different again from exposing an API
in Facebook.
In this video Alan Boehme, CIO of the Coca-Cola Company discusses
how Coca-Cola uses cloud computing to more effectively interact with its
customers and describes the challenges Coca-Cola is facing in establish-
ing SaaS partnerships with new start-ups.
Complete these three steps:
1. Visit https://www.youtube.com/watch?v=hCxmsSED2DY
2. View the 13-minute video.
3. Answer each of the three parts of the following question.
Question
1. Explain the value of Coca-Cola’s cloud partnerships with start-up
companies to:
a. Coca-Cola
b. The start-up companies
c. Coca-Cola’s customers
Sources: Compiled from NextGen Healthcare (2016), Office of the National Coor-
dinator for HIT (2016), and Conn (2016).
64 C H A P T E R 2 Information Systems, IT Architecture, Data Governance, and Cloud Computing
IT Toolbox
Accurately Measuring the Value of Data
Governance
When developing a data governance program, it’s important to pre-
sent a strong business case to get buy-in from top executives and
stakeholders. A crucial part of the business case is an estimate of
the data governance program’s return on investment (ROI) to show
how it will add value to the company. You will need to justify the
ROI based on both business and IT strategy to ensure that available
funds are used to best meet the business objectives.
To do this you will need to carefully analyze the IT infrastruc-
ture with regard to how different components of the IT infrastructure
work together to support business processes, how data needed by
one system can be received and used by another, how easily data can
be communicated and/or repurposed. You will also need to factor in
risks and adverse events such as costs associated with rework in data
collection, costs associated with unreliable or unfit data, and delays
associated with untimely or unavailable data. Now, all of these costs
must be quantified and your level of confidence in the corporate data
has to be calculated to ensure your business case accurately reflects
the value of a data governance program.
One metric used to make this calculation is the confidence in data-
dependent assumptions metric, or CIDDA (Reeves & Bowen, 2013). The
CIDDA identifies specific areas of deficiency.
So, to sum up, when building a data governance model, it is
necessary to:
1. Establish a leadership team
2. Define the program’s scope
3. Calculate the ROI using the CIDDA.
CIDDA is computed by multiplying three confidence estimates
using the following formula:
CIDDA G M TS
where
G = Confidence that data are good enough for their intended
purpose
M = Confidence that data mean what you think they do
TS = Confidence that you know where the data come from and
trust the source.
CIDDA is a subjective metric for which there are no industry
benchmarks, yet it can be evaluated over time to gauge improve-
ments in data quality confidence.
To ensure your understanding of this IT Toolbox item, calculate
the CIDDA of Company A over time, using the stated levels of confi-
dence in the different aspects of its corporate data over Q1–Q4 2017:
Q1_ 2017 : 40%, 50%, 20%
Q2 _ 2017 : 50%, 55%, 30%
Q3_ 2017 : 60%, 60%, 40%
Q4 _ 2017 : 60%, 70%, 45%
G M TS
G M TS
G M TS
G M TS
References
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up-with-salesforce-in-a-deep-platform-partnership
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NextGen Healthcare. “Health Information Exchange (HIE).” NextGen
Healthcare, March 31, 2016.
Office of the National Coordinator for Health Information Technology.
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the Cloud Computing Adoption: A Systematic Study on Cloud Migra-
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Schneider, M. “Solving the Dirty Data Problem in Location-Based
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IT for Management: On-Demand Strategies for Performance, Growth, and Sustainability
Eleventh Edition
Turban, Pollard, Wood
Chapter 2
Information Systems, IT Architecture, Data Governance, and Cloud Computing
Learning Objectives (1 of 5)
2
Copyright ©2018 John Wiley & Sons, Inc.
Figure 2.2 IPOS Cycle
3
Copyright ©2018 John Wiley & Sons, Inc.
Figure 2.3: Components of an Information System
4
Copyright ©2018 John Wiley & Sons, Inc.
Data, Information, Knowledge, & Wisdom
Raw data describes products, customers, events, activities, and transactions that are recorded, classified, and stored.
Information is processed, organized, or put into context data with meaning and value to the recipient.
Knowledge applies understanding, experience, accumulated learning, and expertise to current problem.
Wisdom applies a moral code and prior experiences to form a judgement.
5
Copyright ©2018 John Wiley & Sons, Inc.
Figure 2.4: Examples of Data, Information, Knowledge, and Wisdom
6
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Figure 2.5 Input-Processing-Output Model
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Figure 2.6 Hierarchy of Information Systems, Input/Output and User Levels
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Transaction Processing Systems (TPS)
Internal transactions: originate or occur within the organization (payroll, purchases, etc.)
External transactions: originate outside the organization (customers, suppliers, etc.)
Improve sales, customer satisfaction, and reduce many other types of data errors with financial impacts
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Batch v. Online Real-Time Processing
Batch Processing: collects all transactions for a time period, then processes the data and updates the data store
OLTP: processes each transaction as it occurs (real-time)
Batch processing costs less than OLTP, but may be inaccurate from update delays
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Management Information Systems (MIS)
General-purpose reporting systems that provide reports to managers for tracking operations, monitoring, and control.
Periodic: reports created or run according to a pre-set schedule.
Exception: generated only when something is outside designated parameters.
Ad Hoc, or On Demand: unplanned, generated as needed.
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Decision Support Systems (DSS) (1 of 2)
Interactive, knowledge-based applications that support decision making
Support unstructured and unstructured decisions with the following characteristics:
Easy-to-use interactive interface
Models or formulas that enable sensitivity analysis
Data from multiple sources
Can be used for open-ended What-if analysis and more structured Goal-seeking
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Executive Information Systems (EIS)
Strategic-level information systems that help executives and senior managers analyze the environment in which the organization exists
Used to identify log-term trends and plan appropriate actions
Weakly structured data from both internal and external sources
Designed to be easily operated by executives
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Information Systems Components and Classification
Name the six components of an IS.
Describe the differences between data, information, knowledge, and wisdom.
Define TPS and give an example.
Explain why TPSs need to process incoming data before they are stored.
Define MIS and DSS and give an example of each.
What characteristics distinguish a DSS from an MIS?
What level of personnel typically use an EIS?
What factors determine IS value?
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Learning Objectives (2 of 5)
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Figure 2.10
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IT Infrastructure
Inventory of the physical IT devices that an organization owns and operates
Describes organization’s entire collection of hardware, software, networks, data centers, facilities and related equipment
Does not include people or process components of an IS
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IT Architecture
Guides the process of planning, acquiring, building, modifying, interfacing and deploying IT resources in a single department within an organization
Should offer a way to systematically identify technologies that work together to satisfy the needs of the departments’ users
Blueprint for how future technology acquisitions and deployment will take place
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Enterprise architecture (EA)
Reviews all the information systems across all departments in an organization to develop a strategy to organize and integrate the organization’s IT Infrastructures
Helps meet the current and future goals of the enterprise and maximize the value of technology to the organization.
The way IT systems and processes are structured
Solves two critical challenges: where are we going; how do we get there?
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Decision Support Systems (DSS) (2 of 2)
Strategic Focus
IT systems’ complexity
Poor business alignment
Business and IT Benefits of EA
Cuts IT costs; increases productivity with information, insight, and ideas
Determines competitiveness, flexibility, and IT economics
Aligns IT capabilities with business strategy to grow, innovate, and respond to market demands
Reduces risk of buying or building systems and enterprise apps
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Measuring EA Success: KPIs
Key Performance Indicators (KPIs) are measures that demonstrate the effectiveness of a business process at achieving organizational goals. They present data in easy-to-comprehend and comparison-ready formats.
KPIs measure financial, social media, sales and marketing, operations and supply chain, or environmental data.
KPI examples are current ration; accounts payable turnover; net profit margin; new followers per week; cost per lead; order status.
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EA Components
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IT Infrastructure, IT Architecture, and Enterprise Architecture
What is the purpose of the IT infrastructure?
How is the IT infrastructure different from the IT architecture?
What is the purpose of an enterprise architecture?
What are the business benefits of EA?
Explain why it is necessary for the EA to maintain alignment between IT and business strategy?
Explain KPIs and give an example.
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Learning Objectives (3 of 5)
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Information Management
The use of IT tools and methods to collect, process, consolidate, store, and secure data from sources that are often fragmented and inconsistent
Why a continuous plan is needed to guide, control, and govern IT growth
Information management is critical to data security and compliance with continually evolving regulatory requirements, such as the Sarbanes-Oxley Act, Basel III, the Computer Fraud and Abuse Act (CFAA), the USA PATRIOT Act, and the Health Insurance Portability and Accountability Act (HIPAA)
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Reasons for Information Deficiencies
Information deficiencies are still a problem, caused by:
Data Silos, trapping information in stand alone data stores not accessible by other information systems
Lost or bypassed data, due to flaws in the data collection process
Poorly designed interfaces
Nonstandardized data formats, impeding efficient analysis
Cannot hit moving targets, because data tracking requirements keep changing
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Reasons for Information Deficiencies: Data Silos
Figure 2.14 Data (or information) silos are ISs that do not have the capability to exchange data with other ISs, making timely coordination and communication across functions or departments difficult.
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Information Management Benefits
Business benefits of information management
Improves decision quality
Improves the accuracy and reliability of management predictions
Reduces the risk of noncompliance
Reduces time and cost
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Enterprise Data Governance
Data governance is the control of enterprise data through formal policies and procedures to help ensure data can be trusted and are accessible.
Enterprise-wide Data Governance
Crosses boundaries and used by people through the enterprise.
Reduces legal risks associated with unmanaged or inconsistently managed information.
Copyright ©2018 John Wiley & Sons, Inc.
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Data Governance: Master Data & Management
Master Data & Management (MDM)
Synchronizes critical data from disparate systems into one master file
Creates high-quality trustworthy data:
Running the business with transactional or operational use
Improving the business with analytic use
Requires strong data governance to manage availability, usability, integrity, and security
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Information Management and Data Governance
What is information management?
What is a the “silo effect” and how does it affect business performance?
What three factors are driving collaboration and information sharing?
What are the business benefits of information management?
Explain why it is important to develop an effective data governance program?
Explain the purposes of master data management.
Why has interest in data governance and MDM increased?
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Learning Objectives (4 of 5)
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Data Centers and Cloud Computing
Data Centers and Cloud Computing are types of IT infrastructures or computing systems.
Data Center also refers to a physical facility.
Houses large numbers of network servers used for the storage, processing, management, distribution, and archiving of data, systems, Web traffic, services, and enterprise applications.
National Climatic Data Center U.S. Nation Security Agency Apple
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When a Data Center Goes Down, So Does Business
Business is Reliant Upon data
Uber (car-hailing service)
Users flooded social media with complaints when they experienced an hour-long outage
WhatsApp (smartphone text-messaging service)
Competition added 2 million new registered users within 24 hours of WhatsApp outage (a record)
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Data Virtualization
Data Virtualization
Cisco’s single solution integrating computing, storage, networking, virtualization, and management into a single (unified) platform
Virtualization gives greater IT flexibility and cutting costs:
Instant access to data any time in any format
Respond faster to changing data analytic needs
Cut complexity and cost
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Data Virtualization Benefits
Data Virtualization compared to traditional data integration and replication methods:
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The Software–Defined Data Center (SDDC)
An SDDC facilitates the integration of the various infrastructure silos within organizations.
Optimizes the use of resources, balances workloads
Maximizes operational efficiency by dynamically distributing workloads and provisioning networks
SDDC Goals: decrease costs and increase agility, policy compliance and security by deploying, operating, managing and maintaining applications.
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Figure 2.18 SDDC Infrastructure
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Cloud Computing
What is “The Cloud”?
A general term for infrastructure that uses the Internet and private networks to access, share, and deliver computing resources
Scalable delivery as a service to end-users over a network
Should be approached with greater diligence than other IT decisions as a new technology including Vendor Management and Cloud Service Agreements (CSAs)
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Cloud Computing Types
Private Cloud: Single-tenant environments with stronger security and control (retained) for regulated industries and critical data.
Public Cloud: Multiple-tenant virtualized services utilizing the same pool of servers across a public network (distributed).
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Cloud Computing CSAs
Cloud Service Agreements
A negotiated agreement between a company and service provider that can be a legally binding contract or an informal contract.
The goal is not building the best CSA terms, but getting the terms that are most meaningful to the business needs.
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Cloud Infrastructure
The Value of the Cloud Infrastructure:
Dynamic, not static
Provides a way to make apps and computing power available on demand because they are provided as a service
Referred to as Software As A Service, or SaaS. (examples: Google Apps and Salesforce.com)
Helps companies become more agile and responsive while significantly reducing IT costs and complexity
Large organizations are moving to Enterprise Clouds.
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Data Centers, Cloud Computing, and Virtualization (1 of 2)
What is a data center?
What is the difference between on premise data centers and cloud computing?
What is an SDDC?
What are the advantages of using an SDDC?
How can cloud computing solve the problems of managing software licenses?
What factors should be considered when selecting a cloud vendor or provider?
When are private clouds used instead of public clouds?
Explain three issues that need to be addressed when moving to cloud computing or services.
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Learning Objectives (5 of 5)
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Cloud Services and Virtualization
Sustaining performance requires new business apps and analytics capabilities, which comprise the front end,― and the data stores and digital infrastructure, or back end, to support them.
The back end is where the data reside.
Data may have to navigate through a congested IT infrastructure that was first designed decades ago.
Cloud Services are services made available to users on demand via the Internet from a cloud computing provider’s servers instead of being accessed through an organization’s on-premises servers.
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Anything-As-A-Service Models
There are different types of Cloud Service Model
Software as a Service (SaaS)
End-user apps, like SalesForce
Platform as a Service (PaaS)
Tools and services making coding and deployment faster and more efficient, like Google App Engine
Infrastructure as a Service (IaaS)
Hardware and software that power computing resources, like EC2 & S3 (Amazon Web Services)
Data as a Service (DaaS)
Data shared among clouds, systems, apps, regardless the data source or storage location
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Data Centers, Cloud Computing, and Virtualization
Figure 2.17 Virtual machines running on a simple computer hardware layer. Virtualization: created by a software layer (virtualization layer) containing its own operating system and applications as a physical computer.
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Virtualization Concepts
A virtual machine is a software-created computer. Technically, a virtual machine (VM) is created by a software layer, called the virtualization layer.
That layer has its own Windows or other OS and apps, such as Microsoft Office, as if it were an actual physical computer.
A VM behaves exactly like a physical computer and contains its own virtual―that is, software-based―CPU, RAM (random access memory), hard drive, and network interface card (NIC).
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Virtualization Benefits
Characteristics & Benefits
Memory-intensive
Huge amounts of RAM due to massive processing requirements
Energy-efficient
Up to 95% reduction in energy use per server through less physical hardware
Scalability and load balancing
Handles dynamic demand requests like during the Super Bowl or World Series
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Data Centers, Cloud Computing, and Virtualization Questions (2 of 2)
What is SaaS?
What is PaaS?
What is IaaS?
How might companies risk violating regulation or compliance requirements with cloud services?
In what ways is a virtualized information system different from a traditional information system?
Describe the different types of Virtualization.
What is load balancing and why is it important?
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Copyright
Copyright © 2018 John Wiley & Sons, Inc.
All rights reserved. Reproduction or translation of this work beyond that permitted in Section 117 of the 1976 United States Act without the express written permission of the copyright owner is unlawful. Request for further information should be addressed to the Permissions Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages, caused by the use of these programs or from the use of the information contained herein.
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Question and writing rules
· Elzein, I. A., & Kurdi, M. (2019). Analyzing the Challenges of Security Threats and Personal information in Mobile Cloud Computing Infrastructure. 2019 International Conference on Digitization (ICD), Digitization (ICD), 2019 International Conference On, 202–206.
Recommended:
Module 02 PowerPoint slides
Meet the following requirements:
· Be 5 pages in length, which does not include the title page, abstract, or required reference page, which is never a part of the content minimum requirements.
· Use APA (7th ed) style guidelines.
· Support your submission with course material concepts, principles, and theories from the textbook and at least five scholarly, peer-reviewed journal articles.
Writing rules
· Use a standard essay format for responses to all questions (i.e., an introduction, middle paragraphs, headline (and conclusion).
· Make sure to include all the key points within conclusion section, which is discussed in the assignment. Your way of conclusion should be logical, flows from the body of the paper, and reviews the major points.
· I would like to see more depth for the question
· Responses must be submitted as a MS Word Document only, typed double-spaced, using a standard font (i.e. Times New Roman) and 12 point type size.
· Plagiarism All work must be free of any form of plagiarism.
· Written answers into your own words. Do not simply cut and paste your answers from the Internet and do not copy your answers from the textbook
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Page number
Paper title
Bolded
Capital Case
Author(s)
Affiliation line
Course info
Instructor
Due date
Title page
Inset page break
For full APA formatting
tutorial click here!
Paper title No “introduction”
heading before the first
paragraph.
Title of your paper
signifies the
introduction.
Parenthetical citation,
one author
Narrative citation,
one author
Parenthetical citation,
multiple authors
Parenthetical citation,
direct quote
Parenthetical citation,
two authors
For full in-text citation
tutorial click here!
First page/
in-text citations
Main section heading
Subsection of Level 1
Subsection of Level 2
Centered
Bolded
Title Case
Flush left
Bolded
Title Case
Flush left
Bolded italic
Title Case
Indented
Bolded
Title Case
End with period
Indented
Bolded italic
Title Case
End with period
Subsection of Level 3
Subsection of Level 4
For full section heading
tutorial click here!
Section headings
Inset page break
Reference List
For full reference list tutorial
click here!
Centered &
Bolded
4 elements all references
Author. (Date). Title. Source
Inset page break
https://www.youtube.com/playlist?list=PLTM3Z6z6KvXpsYVQAkMGasSe2E6tWboD2
Centered & bolded
DO NOT indent first line
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ABSTRACT/
Running Head
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