Module 02: Critical Thinking? QUESTION: Improving Data Governance? Corporations are increasingly moving their data to the clo

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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 Classifications

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

ay

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

20 15

Q 2

20 15

Q 3

20 15

Q 4

20 15

Q 1

20 16

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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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.

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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 …

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