Decision Support, Analytics, and Business Intelligence, Third Edition

Daniel J. Power, Ciara Heavin

• Editorial: Business Expert Press
• Año: 2017
• ISBN:
  9781631573927
• Categoría: Business
• Categoría: Information Management

Rapid technology change is impacting organizations large and small. Mobile and Cloud computing, the Internet of Things (IoT), and "Big Data" are driving forces in organizational digital transformation. Decision support and analytics are available to many people in a business or organization. Business professionals need to learn about and understand computerized decision support for organizations to succeed. This text is targeted to busy managers and students who need to grasp the basics of computerized decision support, including: What is analytics? What is a decision support system? What is "Big Data"? What are "Big Data" business use cases? Overall, it addresses 61 fundamental questions. In a short period of time, readers can "get up to speed" on decision support, analytics, and business intelligence. The book then provides a quick reference to important recurring questions.

CHAPTER 1

Modern Decision Support

Managers must make decisions in an increasingly complex, rapidly changing, volatile, and ambiguous environment. This environmental turbulence and the challenges of global digitalization increase the risks for managers and organizations. To help reduce and manage risk, now is an opportune time to implement more and better computerized decision support. Managers should implement or update systems to provide better business intelligence (BI), analytics, big data, and other types of computerized decision support. This turbulent environment should motivate managers to evaluate computerized decision support projects to ensure they are serving the purpose for which they were intended. What has changed? Modern decision support is more useful and more sophisticated and decision support technology has become very sophisticated.

In the past 35 years, software vendors have regularly used new terms for capabilities associated with decision support. For some vendors, legacy terms such as decision support system (DSS) were rejected as too general, while for others legacy terms reminded potential customers of failed projects or unrealistic expectations. Terms such as analytics and big data provided a new way to sell decision support capability. Despite the changing terminology, managers continue to want and need computerized information systems to support their decision making.

Decision support does not ensure correct decisions. One hopes vendors have realized it is important to identify and better manage customer expectations. Decision support applications differ widely depending upon the purpose of the system and perceived need. Current technologies can support a wide range of decision-making tasks. Decision support consultants, designers, and researchers have learned much about using information technology (IT) solutions to support decision making and that knowledge can benefit managers and their organizations.

Prior research and experience support two fundamental premises associated with computerized decision support. First, computers and IT can improve the timeliness, accuracy, completeness, and availability of information. Subsequently, this can help people make better decisions. Second, computerized decision support keeps people at the center of the decision-making process by assisting and supporting managers in their role as organizational decision makers. The overriding goal of computerized decision support developers is unchanged—improve human decision-making effectiveness and efficiency with IT solutions.

Many organizations have integrated computerized decision support into day-to-day operating activities and use systems for performance monitoring. Frequently, managers download and analyze sales data, create reports, and analyze and evaluate forecasting results. DSS can help managers perform tasks, such as allocating resources, comparing budget to actual results, drilling down in a database to analyze operating results, projecting revenues, and evaluating scenarios. Data warehouses were initially created as large clearing houses for transaction processing and routine reporting, providing the organization with a “single version of the truth” or a unified single view of their data. More recently, data warehouses are used with sophisticated predictive models and analytic tools for advanced ad hoc querying and sophisticated data analytics.

Decision support research began in the 1960s and the concepts of decision support, decision support systems, and the acronym DSS remain understandable, intuitively descriptive, and even obvious in meaning.

Related terms such as analytics, BI, knowledge management (KM), and big data are of more recent origin and are interpreted in different ways by vendors and consultants. Decision support is a broad concept that prescribes using computerized systems and other tools to assist in individual, group, and organization decision making. One goal in this and succeeding chapters is to make some sense out of the decision support jargon.

The eight questions included in this chapter discuss the need for decision support, the theory behind decision support, a characterization of organizational decisions, the importance of framing decisions, the typical features and users of a modern DSS, and the technology skills and the knowledge needed by managers and targeted users. The final question examines why some managers resist DSS.

What Is the Need for Decision Support?

Today decision making is more difficult: the need for decision-making speed has increased, overload of information is common, and there is more distortion of information. The rapidly changing pace of technology and the exponential increase in the speed and volume by which data is generated on a daily basis have compounded the challenges faced by decision makers. On the positive side, there is a greater emphasis on fact-based decision making. A complex decision-making environment creates a need for computerized decision support. Research and case studies provide evidence that a well-designed and appropriate computerized DSS can encourage fact-based decisions, improve decision quality, and improve the efficiency and effectiveness of decision processes. Most managers want more and better analyses and fact-based decision-relevant reports quickly. Managers do have many and increasing information needs. The goal of many DSS is to create and provide decision-relevant information. There is a pressing need to use technology to help make decisions better. Decision makers perform better with the right information at the right time. In general, computerized decision support can help acquire, codify, and store data and information and transfer and organize information and knowledge. Effective decision support provides managers with more independence to retrieve and analyze data and information in the form of documents, and more recently rich media such as audio and video, as they need them.

From a different perspective, we need decision support because we have decision-making biases. Biases distort decisions. Reducing human decision-maker bias has been a secondary motivation for decision support, but it is an important one. Most managers accept that some people are biased when making decisions, but doubt a computerized solution will significantly reduce bias. Evidence shows information presentation and information availability influence and bias a decision maker’s thinking both positively and negatively. Evidence shows system designers can reduce the negative bias. Also, evidence shows decision makers “anchor” on the initial information they receive and that influences how they interpret subsequent information. In addition, decision makers tend to place the greatest attention on more recent information and either ignore or forget historical information.¹ Good decision support software can reduce these and other biases such as confirmation bias, that is, a decision maker’s overreliance on positive cases to inform a decision, overestimating the likelihood of being right, and overgeneralizing from too little or minimum available information.

Managerial requests for more and better information, today’s fast paced and technology-oriented decision environments, and significant decision-maker limitations create the need for more and better computerized decision support. Managers should strive to provide computerized decision support when two conditions associated with a decision situation are met: (a) good information is likely to improve the quality of a decision and subsequently the decision outcome, and (b) potential users recognize a need for and want to use computerized support in that situation.

Introducing more and better decision support in an organization does create changes and challenges for managers. For example, mobile computing, using smart phones with decision support applications or tablet devices connected to the Internet and corporate databases is a big change. The pervasive use of these technologies requires managers to have new skills and new knowledge. The increased focus on sensor technology and the growth of the Internet of Things (IoT), which, with heightened pervasive connectedness, is morphing into the Internet of Anything (IoA), has opened the door for new types of applications and services, including new opportunities for computerized decision support. These technologies are beginning to fundamentally change the way we do things. Far from the single unified view of data in a traditional data warehouse, mobile and IoT are pushing managers and organizations to take a more holistic view of the availability, processing, and analysis of data/information in an increasingly global digital ecosystem.

What Is the Theory of Computerized Decision Support?

Past practice and experience often guide computerized decision support development more than theory and general principles do. Some developers have concluded each decision situation is different so no theory is possible. Some academics argue that we have conducted insufficient research to develop theories. For these spurious reasons, a theory of decision support has received limited discussion in the literature. Nobel Laureate Economist Herbert Simon’s writings provide a starting point for a theory of decision support. From his classic book, Administrative Behavior,² are derived three propositions:

Proposition 1: If information stored in computers is accessible when needed for making a decision, it can increase human rationality.

Proposition 2: Specialization of decision-making functions is largely dependent upon developing adequate channels of communication to and from decision centers.

Proposition 3: When a particular item of knowledge is needed repeatedly in decision making, an organization can anticipate this need and, by providing the individual with this knowledge prior to decision making, can extend his or her area of rationality. Providing this knowledge is particularly important when there are time limits on decisions.

From Simon’s article³ “Applying Information Technology to Organization Design” are three additional propositions:

Proposition 4: In a postindustrial society, the central problem is not how to organize to produce efficiently, but how to organize to make decisions—that is, to process information. Improving efficiency will always remain an important consideration.

Proposition 5: From the information processing point of view, division of labor means factoring in the total system of decisions that need to be made into relatively independent subsystems, each one of which can be designed with only minimal concern for its interactions with the others.

Proposition 6: The key to the successful design of information systems lies in matching the technology to the limits of the attention of users. In general, an additional component, person or machine, for an information-processing system will improve the system’s performance when it:

1. has a small output in comparison with its input, so that it conserves attention instead of making additional demands on attention

2. incorporates effective indexes of both passive and active kinds. Active indexes automatically select and filter information; they promote the rapid discovery of data/information.

3. incorporates analytic and synthetic models, using datasets, variables, and business assumptions and mathematical algorithms, that are c...

Índice

• Cover
• Half-title Page
• Title Page
• Copyright
• Abstract
• Contents
• Acknowledgments
• Introduction
• Chapter 1 Modern Decision Support
• Chapter 2 Decision Support Concepts
• Chapter 3 Recognizing Types of Decision Support
• Chapter 4 Using Big Data for Decision Support
• Chapter 5 Business Intelligence and Data-Driven DSS
• Chapter 6 Predictive Analytics and Model-Driven Decision Support
• Chapter 7 Decision Support Benefits and Trade-Offs
• Chapter 8 Identifying Decision Support Opportunities
• List of Questions with Links to Answers
• Glossary
• Notes
• Bibliography
• Index
• Other Titles in Our Information Systems Collection
• Backcover