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As you reflect on this week's readings, which specific topic or issue resonates the most with you? Why? (Note: Ensure your selected topic or issue is pertinent to this week's topic.)
What primary question remains unanswered? The question can be about something that you want more information about, or it can be something that takes the conversation in an entirely different direction.
What did you gain from reading these articles?

Chapter 1 An Overview of Business Intelligence, Analytics, and Data ScienceLearning Objectives

Understand the need for computerized support of managerial decision making

Recognize the evolution of such computerized support to the current state-analytics/data science

Describe the business intelligence (BI) methodology and concepts

Understand the different types of analytics and see selected applications

Understand the analytics ecosystem to identify various key players and career opportunities

The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these, in the framework of the needed decisions, must be done quickly, frequently in real time, and usually requires some computerized support.

This book is about using business analytics as computerized support for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support, as well as on the commercial tools and techniques that are available. This book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE approach to introducing these topics: Exposure, Experience, and Exploration. The book primarily provides exposure to various analytics techniques and their applications. The idea is that a student will be inspired to learn from how other organizations have employed analytics to make decisions or to gain a competitive edge. We believe that such exposure to what is being done with analytics and how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications. The book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata University Network (TUN) and other sites that include team-oriented exercises where appropriate.

1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics

The opening vignette illustrates how an entire industry can employ analytics to develop reports on what is happening, predict what is likely to happen, and then also make decisions to make the best use of the situation at hand. These steps require an organization to collect and analyze vast stores of data. From traditional uses in payroll and bookkeeping functions, computerized systems have now penetrated complex managerial areas ranging from the design and management of automated factories to the application of analytical methods for the evaluation of proposed mergers and acquisitions. Nearly all executives know that information technology is vital to their business and extensively use information technologies.

Computer applications have moved from transaction processing and monitoring activities to problem analysis and solution applications, and much of the activity is done with cloud-based technologies, in many cases accessed through mobile devices. Analytics and BI tools such as data warehousing, data mining, online analytical processing (OLAP), dashboards, and the use of the cloud-based systems for decision support are the cornerstones of today's modern management. Managers must have high-speed, networked information systems (wireline or wireless) to assist them with their most important task: making decisions. In many cases, such decisions are routinely being automated, eliminating the need for any managerial intervention.

Besides the obvious growth in hardware, software, and network capacities, some developments have clearly contributed to facilitating growth of decision support and analytics in a number of ways, including the following:

Group communication and collaboration. Many decisions are made today by groups whose members may be in different locations. Groups can collaborate and communicate readily by using collaboration tools as well as the ubiquitous smartphones. Collaboration is especially important along the supply chain, where partners-all the way from vendors to customers-must share information. Assembling a group of decision makers, especially experts, in one place can be costly. Information systems can improve the collaboration process of a group and enable its members to be at different locations (saving travel costs). More critically, such supply chain collaboration permits manufacturers to know about the changing patterns of demand in near real time and thus react to marketplace changes faster.

Improved data management. Many decisions involve complex computations. Data for these can be stored in different databases anywhere in the organization and even possibly outside the organization. The data may include text, sound, graphics, and video, and these can be in different languages. Many times it is necessary to transmit data quickly from distant locations. Systems today can search, store, and transmit needed data quickly, economically, securely, and transparently.

Managing giant data warehouses and Big Data. Large data warehouses (DWs), like the ones operated by Walmart, contain humongous amounts of data. Special methods, including parallel computing, Hadoop/Spark, and so on, are available to organize, search, and mine the data. The costs related to data storage and mining are declining rapidly. Technologies that fall under the broad category of Big Data have enabled massive data coming from a variety of sources and in many different forms, which allows a very different view into organizational performance that was not possible in the past.

Analytical support. With more data and analysis technologies, more alternatives can be evaluated, forecasts can be improved, risk analysis can be performed quickly, and the views of experts (some of whom may be in remote locations) can be collected quickly and at a reduced cost. Expertise can even be derived directly from analytical systems. With such tools, decision makers can perform complex simulations, check many possible scenarios, and assess diverse impacts quickly and economically. This, of course, is the focus of several chapters in the book.

Overcoming cognitive limits in processing and storing information. According to Simon (1977), the human mind has only a limited ability to process and store information. People sometimes find it difficult to recall and use information in an error-free fashion due to their cognitive limits. The term cognitive limits indicates that an individual's problem-solving capability is limited when a wide range of diverse information and knowledge is required. Computerized systems enable people to overcome their cognitive limits by quickly accessing and processing vast amounts of stored information.

Knowledge management. Organizations have gathered vast stores of information about their own operations, customers, internal procedures, employee interactions, and so forth, through the unstructured and structured communications taking place among the various stakeholders. Knowledge management systems have become sources of formal and informal support for decision making to managers, although sometimes they may not even be called KMS. Technologies such as text analytics and IBM Watson are making it possible to generate value from such knowledge stores.

Anywhere, anytime support. Using wireless technology, managers can access information anytime and from anyplace, analyze and interpret it, and communicate with those involved. This perhaps is the biggest change that has occurred in the last few years. The speed at which information needs to be processed and converted into decisions has truly changed expectations for both consumers and businesses. These and other capabilities have been driving the use of computerized decision support since the late 1960s, but especially since the mid-1990s. The growth of mobile technologies, social media platforms, and analytical tools has enabled a different level of information systems (IS) support for managers. This growth in providing data-driven support for any decision extends to not just the managers but also to consumers. We will first study an overview of technologies that have been broadly referred to as BI. From there we will broaden our horizons to introduce various types of analytics.

Questions

What are some of the key system-oriented trends that have fostered IS-supported decision making to a new level?

List some capabilities of information systems that can facilitate managerial decision making.

How can a computer help overcome the cognitive limits of humans?