Improving Data Governance 

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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 of 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?

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Information Systems,
IT Architecture, Data Governance,
and Cloud Computing
Case 2.1 Opening Case: Detoxing Location-Based
Advertising Data at MEDIATA
2.1 IS Concepts and Classifications
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 IT Infrastructure, IT Architecture, and
Enterprise Architecture
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
2.3 Information Management and Data
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 Data Centers and Cloud Computing
2.4 Understand the concepts of data centers and cloud computing
and understand how they add value in an organization.
2.5 Cloud Services and Virtualization
2.5 Describe the different types of cloud services and the various
forms of virtualization and understand how they add value in
an organization.
Case 2.2 Business Case: Data Chaos Creates Risk
Case 2.3 Video Case: Cloud Computing at
Coca-Cola Is Changing Everything
Information Systems, IT Architecture, Data Governance, and Cloud Computing
Dirty data are data of such poor
quality that they cannot be trusted
or relied upon for decisions.
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 performance 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 enterprise 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 guidance 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 information 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. Companies 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 inaccurate 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.
Introduction 27
Courtesy of Billy Ray
Case 2.1 Opening Case
Detoxing Location-Based Advertising Data
TA B L E 2.1
Opening Case Overview
MEDIATA was launched as Valued Interactive
Media (VIM) in 2009. Rebranded in 2013 as
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
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 managed, 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, Carlsberg, Honda, ACCOR Hotels, Air New Zealand, Heinz, Woolworths, Citibank, 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 incorrect location and MEDIATA realized that this “dirty data” was adversely
affecting their business. Location-based advertising provides organizations 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 geographic 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
Information Systems, IT Architecture, Data Governance, and Cloud Computing
Location-based advertising.
improve the effectiveness of MEDIATA’s user profile data by more precisely 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 advertisements 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.
Information systems (ISs) is
a combination of information
technology and people’s activities
using the technology to support
business processes, operations,
management, and decisionmaking at different levels of the
IPOS is the cycle of inputting,
processing, outputting, and
storing information in an
information system.
Creating and employing this data governance system allowed MEDIATA
to clean its datasets and create new, effective methods to reach target
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
Before we being to explore the value of information systems (ISs) to an organization, it’s useful to understand what an IS is, what it does, and what types of ISs are typically found at different 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 create new products. ISs collect (input) and manipulate data (process), and generate and distribute 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 website (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).
IS Concepts and Classification
Hard Drive
Error Report
Performance Metrics
IPOS cycle.
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 managed to provide maximum benefit to an organization (see Figure 2.3).
Components of an IS.
1. Hardware Any physical device used in a computerized IS. Examples include central processing 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 component 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
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
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
New Vehicle Sales by Quarter
(who, what, where, when)
Knowledge adds understanding,
experience, accumulated learning,
and expertise as they apply to a
current problem or activity, to
Information is data that have
been processed, organized, or
put into context so that they have
meaning and value to the person
receiving them.
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. Generally speaking, ISs process data into meaningful information that produces corporate knowledge 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.
17, 25, 54, 12, 68, 19, 39, 42, 72
Number of new vehicles sold
Examples of data, information, knowledge, and wisdom.
(Raw figures)
IS Concepts and Classification
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 determining 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 evaluating 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.
Temporary memory (RAM), hard disks, flash memory, cloud
Data collected,
snapped from
Working with
results on
hardcopy, digital
copy, archive
Users, clients, customers, operators, technicians, governments, companies
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 business 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 connecting its traders to business information in real time. Immediate throughput in combination 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 information in ways not possible before, according to Matt McClelland, manager of information
Wisdom is a collection of
values, ethics, moral codes, and
prior experiences that form an
evaluated understanding or
common-sense judgment.
Information Systems, IT Architecture, Data Governance, and Cloud Computing
governance for Blue Cross Blue Shield of North Carolina. “With proper support, information governance can bridge the gaps among the need to address regulation and litigation 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 organizations, the typical level of workers who use them and the types of input/output (I/O) produced 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 corporate strategies.
Executive Information Systems
Decision Support Systems
Senior Managers
Management Information Systems
Middle Managers
Transaction Processing Systems
Line Workers
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 control, 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 organization, 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 customers, suppliers, regulators, distributors, and financing institutions.
TPSs are essential systems. Transactions that are not captured can result in lost sales, dissatisfied customers, unrecorded payments, and many other types of data errors with financial
© Jan_Neville/iStockphoto
IS Concepts and Classification
FIGURE 2.7 Scanners automate the input of data into a
transaction processing system (TPS).
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 statements 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 processed 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. Payroll 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 inaccurate 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
Information Systems, IT Architecture, Data Governance, and Cloud Computing
to evaluate an organization’s performance by comparing current with previous outputs. Functional 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 websites (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 contamination 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.
© Damir Karan/iStockphoto
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 knowledge 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 systems support unstructured and se

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