Business Intelligence, Analytics and Data Science: A Managerial Perspective

For courses on Business Intelligence or Decision Support Systems.

A managerial approach to understanding business intelligence systems.

To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.

See the decision-making aspects: Managerial Approach. This text takes a managerial approach to Business Intelligence (BI), emphasizing the applications and implementations behind the concepts. This approach allows students to understand how BI works in a way that will help them adopt these technologies in future managerial roles.

Put the concepts into action: Access to the Teradata Network. Teradata University Network (TUN) is a free learning portal sponsored by Teradata, a division of NCR, whose objective is to help faculty learn, teach, communicate, and collaborate with others in the field of BI. Teradata also supports a student portal (teradatastudentnetwork.com) that contains a variety of learning resources such as cases, Web seminars, tutorials, exercises, links to sources, and more. Business Intelligence is interconnected with TUN via various hands-on assignments provided in all chapters and is accessible to students through the portal.

Understand the context: Real-world Orientation. Extensive, vivid examples from large corporations, small businesses, and government and not-for-profit agencies make the difficult concepts more accessible and relevant. International examples of global competition, partnerships, and trade are also provided throughout. These real-world case studies show students the capabilities of BI, its cost and justification, and the innovative ways real corporations are using BI in their operations.

Opening Vignette: Real world case that presents a challenge, solution, and results that introduce the chapter.Each opening vignette is paired with questions for students to dig into the details and think critically about the case.

Application Cases: Real world cases that emphasize concepts in the chapter, paired with discussion questions.

Section Review Questions: Checkpoints for students on key concepts they should have learned in the section.

Color charts, graphs, and figures: Help students visualize data, processes, and stay engaged with the content.

Technology Insights: Boxed features focusing on the benefits of available technology.

Resources, Links, and the Teradata University: Appear at the end of chapter and provide students additional reading, information, and cases to explore.

End of Chapter: Includes a list of Chapter Highlights, Key Terms, Discussion Questions, Exercises, and an additional Application Case to help students review, test, and apply their understanding.

New to this Edition

With the goal of improving the text, this edition marks a major reorganization of the text to reflect the focus on business analytics. This edition is now organized around three major types of business analytics (i.e., descriptive, predictive, and prescriptive). The new edition has many timely additions, and the dated content has been deleted. The following major specific changes have been made:

New Organization – The book recognizes three types of analytics: descriptive, predictive, and prescriptive, a classification promoted by INFORMS. Chapter 1 introduces BI and analytics with an application focus in many industries. This Chapter also includes an overview of the analytics ecosystem to help the user explore all the different ways one can participate and grow in the analytics environment. It is followed by an overview of statistics, importance of data, and descriptive analytics/visualization in Chapter 2. Chapter 3 covers data warehousing and data foundations including updated content, specifically data lakes. Chapter 4 covers predictive analytics. Chapter 5 extends the application of analytics to text, Web, and social media. Chapter 6 covers Prescriptive Analytics, specifically linear programming and simulation. It is totally new content for this book. Chapter 7 introduces Big Data tools and platforms. The book concludes with Chapter 8, emerging trends and topics in business analytics including location analytics, Internet of Things, cloud-based analytics, and privacy/ethical considerations in analytics. The discussion of analytics ecosystem recognizes prescriptive analytics as well.

New Chapters – The following chapters have been added:

Chapter 2: “Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization”

This chapter aims to set the stage with a thorough understanding of the nature of data, which is the main ingredient for any analytics study. Next, statistical modeling is introduced as part of the descriptive analytics. Data visualization has become a popular part of any business reporting and/or descriptive analytics project; therefore, it is explained in detail in this chapter. The chapter is enhanced with several real-world cases and examples (75% new material).

Chapter 6: “Prescriptive Analytics: Optimization and Simulation”

This chapter introduces prescriptive analytics material to this book. The chapter focuses on optimization modeling in Excel using the linear programming technique. It also introduces the concept of simulation. The chapter is an updated version of material from two chapters in our DSS book, 10th edition. For this book it is an entirely new chapter (99% new material).

Chapter 8: “Future Trends, Privacy and Managerial Considerations in Analytics”

This chapter examines several new phenomena that are already changing or are likely to change analytics. It includes coverage of geospatial in analytics, Internet of Things, and a significant update of the material on cloud-based analytics. It is also updates some coverage from the last edition on ethical and privacy considerations (70% new material).

Revised Chapters – The remaining chapters have been revised and updated:

Chapter 1: “An Overview of Business Intelligence, Analytics, and Data Science”

This chapter has been rewritten and significantly expanded. It opens with a new vignette covering multiple applications of analytics in sports. It introduces the three types of analytics as proposed by INFORMS: descriptive, predictive, and prescriptive analytics. As noted earlier, this classification is used in guiding the complete reorganization of the book itself (earlier content but with a new figure). Then it includes several new examples of analytics in healthcare and in retail industry. Finally, it concludes with a significantly expanded and updated coverage of analytics ecosystem to give the students a sense of the vastness of the analytics and data science industry (about 60% new material).

Chapter 3: “Descriptive Analytics II: Business Intelligence and Data Warehousing”

This is an old chapter with some new subsections (e.g., data lakes) and new cases (about 30% new material).

Chapter 4: “Predictive Analytics I: Data Mining Process, Methods, and Algorithms”

This is an old chapter with some new content organization/flow and some new cases (about 20% new material).

Chapter 5: “Predictive Analytics II: Text, Web, and Social Media Analytics”

This is an old chapter with some new content, organization/flow, and some new cases (about 25% new material).

Chapter 7: “Big Data Concepts and Analysis”

This was Chapter 6 in the last edition. It has been updated with a new opening vignette and cases, coverage of Teradata Aster, and new material on alternative data (about 25% new material).