Management Decision-Making, Big Data and Analytics

Simone Gressel, David J. Pauleen, Nazim Taskin

• Publisher: SAGE Publications Ltd
• Year: 2020
• ISBN:
  9781529737387
• Edition: 1
• Topic: Business
• Subtopic: Gestione
• Index: Business

Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a 'critical incidents' feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including:
• Big data
• Analytics
• Managing emerging technologies and decision-making
• Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor's Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.

1 Professional Mindsets

Contents

• Key Lessons
• Introduction
• Management in the Age of Big Data
• The Different Mindsets of Managers and Information Technologists
• The Importance of Communication and Shared Mental Models between Managers and Information Technologists
• Addressing the Talent Shortage
• Chapter Summary

Highlight Box 1.1 Professional Mindsets: Who Is Thinking What?

What do managers think when they hear about big data and analytics? Do they understand these technologies and how they can be used in decision-making? And what about those who work with data and analytics, the information technologists … do they know what managers need to make better decisions? The following observations by working professionals, based on recent research (Gressel, 2020), hint at what’s on managers’ and technologists’ minds:

Head of Data/Analytics Department: ‘Some people absolutely get it, but the level of engagement varies. And largely, business people feel that actually data is hard, and analytics is hard, and they need help. And until we get them help, and get them data and analytics, there is no point really thinking about where they fit into the business.’

CEO of a Small Company: ‘I think a lot of the challenges come down to education. To a lot of people, data can mean – at an extreme level, “That’s what I use to top up on my phone,” to “Oh, I absolutely understand the power of data and I know the distinction between the management of data, using tools to analyze it, and extracting insights.”’

Business Analyst: ‘If the CEO is telling me to go back and look at the data again, sometimes I feel this is coming from someone who has a fear, a mistrust or a misunderstanding of what this data actually represents. And in that case, I may need to reemphasize or sell the point a little bit better and maybe do a better job at explaining what the data represents. But then on the other hand, if the CEO comes from an analytic background, then he understands the business and he understands the data. So if he wants it, it’s not just because he’s the CEO, but because he understands both sides.’

Head of Data/Analytics Department: ‘The real challenge is knowing the right questions, and those questions should be driven from understanding how data can create value in your business. So in a lot of cases I meet business people who say “I’ve got a need or an issue, and I believe that data can help” – and I say “Cool, let’s talk, articulate your need.”’

Chapter 1 Key Lessons

1. Data-based decision-making is now a critical tool in management decision-making and organizational success.
2. Managers must have enough knowledge of data and analytics to ask the right questions about how they can be used in decision-making.
3. Managers need to bridge the current divide between themselves and information technologists.

Introduction

Managers and technologists are educated and trained to think and speak differently about technology and business. In an age where data-driven decision-making is becoming the new norm, where technology and business overlap, the gap resulting from the differences between technologists and managers needs to be bridged. In this first chapter, we want to set the stage for the book by delving deeper into these differences, their origins and how to bridge the gap. In essence, managers and technologists perceive the world differently and think about problems differently. This often results in them having difficulty finding common ground when it comes to making decisions about business or technology-based problems. In Highlight Box 1.1 we underscore these issues by using some of the quotes we have collected during our research. Of course, we must point out that managers, particularly in IT-related departments, can have technology-based backgrounds – however, in most organizations, these are a minority.

In this book, when we speak of managers we refer to those working in organizations from line managers to C-level (CEO, CFO, CIO, etc) executives. Decision-making is at the core of what these managers do (Intezari and Pauleen, 2019). Indeed, Herbert Simon (1960) considers ‘decision-making’ and the whole process of management to be synonymous. When managers engage in decision-making, they need to weigh up choices and consider consequences. They have to consider how stakeholders will respond. In this sense, managers see the world as a set of competing interests in which they must seek mutually acceptable solutions. Management decision-making will be explored in greater detail in Chapter 4.

Information technologists is the term we use in this book to describe as a group all for those in the organization who work primarily with technology, particularly data and information systems. Over the years, and as technology has developed, they have included systems analysts, software engineers, network engineers, IT specialists, web designers, mobile app developers and so on. More recently, in the age of big data and analytics, they are often referred to as data scientists, data analysts, data engineers, data architects and the like. Highlight Box 1.2 lists some of these positions and describes their responsibilities. It is probably safe to say that in general, information technologists tend to be more immediate and hands-on, putting their focus on engineering solutions to the problems they encounter. This is different from managers on the business side who tend to engage in more conjectural thinking when approaching problems as they consider the relationships between people, context and the problem.

Highlight Box 1.2

Table 1.1

Source: Granville, 2013, 2014a, 2014b, 2014c; Mazenko, 2016; Schmarzo, 2018; DataFlair, 2019

Data science can be defined as a field possessing attributes from other fields such as computer science, software engineering, statistics, mathematics, data mining, machine learning (ML), management science and operations research/industrial engineering. With recent advances in technology, data collection and analysis, new roles have emerged, such as: data scientist, data analyst, data engineer and data architect. Data scientists normally excel in statistics, mathematics, data engineering, machine learning, business, software engineering, visualization and spatial data. They explore data collected from different sources, gain insights and look for patterns to predict future events. Statisticians generally design surveys or conduct experiments to collect data, then analyze and interpret the data by applying statistical theories, methods, techniques and analysis, before applying visualization techniques to present the results. Data architects and data engineers possess extensive knowledge of databases and data warehouses and focus on developing the architecture for the data or data warehousing and data integration. Data engineers may also possess deep knowledge of software engineering. Data analysts usually work with structured or organized data. They convert the collected data, usually from enterprise systems, into different formats before conducting their analysis and creating visual representations of them to address business issues. Business analysts manage business processes and produce scheduled as well as ad hoc reports for decision-makers. They are often responsible for database design, ROI assessment, managing budgets and risks, and planning marketing and finance activities. They work closely with business managers.

Key organizational actors perceive decision situations differently and think differently about how to solve problems; this can have significant implications for interpersonal and organizational effectiveness. In this chapter we will look at what has changed for management in the age of big data and how these changes can be addressed. We also look in more depth at the mindsets of managers and information technologists, how these can be understood and how they can be bridged through the building of shared mental models and other strategies. Finally, we highlight that there is currently a shortage of managers and information technologists with the knowledge and skills to effectively fuse business decision-making with big data and analytics. We explain how this book can help develop essential skills necessary to address this shortage.

Management in the Age of Big Data

Google, Facebook and Amazon are examples of successful companies that were started in and have thrived since the Dot-Com age. The founders of these Internet-based companies grew up and were educated and socialized with computers and the early Internet. Their experiences, education and interests allowed them to see the potential of Internet-based technology, including the promise of big data and analytics, and made it possible for them to design and engineer technical platforms that could create and address business and social needs. In the early days of these companies, the founders would hire like-minded employees, who all spoke the same ‘language’.

The Dot-Com bubble of 2000 changed everything. It laid bare the fact that while technologists had great ideas and could develop technology, they were not always the most suitable people to manage businesses. Examples of companies that failed due to bad management abound and include Boo.com – an online fashion company, Pets.com – an online pet supplies company, and WebVan – an online grocery service. The founders of the companies that survived realized that they needed to bring in business-oriented managers and executives – people trained to run businesses. For example, Apple brought in Tim Cook in 1998 and Google hired Eric Schmidt in 2001 to help manage their business operations.

Businesses that existed before the Dot-Com era, so-called ‘bricks and mortar’ companies, saw the changes being wrought by emerging technologies and realized that they needed to incorporate them into their operations. The evolution of data and analytics-based technologies and their impact on management decision-making are covered in more detail in Chapters 2 and 3, but through the early Dot-Com era and certainly by the time social media and mobile technologies became ascendant it became clear to most businesses that big data and analytics could provide significant business opportunities (Bholat, 2015).

While Dot-Com companies evolved in tandem with these technologies, traditional businesses were always trying to incorporate the latest technology into their operations, from computers in the 1960s to enterprise systems in the 1990s (see Chapter 2 – Technological Evolution of Data and Its Management). These technologies usually required significant changes in organizational processes and culture. Not only did they have to develop their technological infrastructure, but they needed to reconsider their human resources. Of course, they needed information technologists to develop, implement and operate the new technologies and business analysts to help make the data more relevant to the business, but they also needed managers and executives who understood the potential of technology and knew the right questions to ask about what technology was needed and how it could be used to greatest advantage. In cases where the business side did not understand the technology, money was wasted, opportunities were missed and businesses often failed. With the advent of the Internet, mobile applications, social media and other recent technologies, the pace of change, the sophistication of these technologies, and their effects on business have all accelerated. Even companies that started in the Dot-Com age may have trouble evolving at current rates of change. Facebook, the most dominant social media site for a generation, is now confronting social, technical and political challenges that may very well spell the beginning of the end for it.

In the age of big data, business managers will need to be able to work with business analysts, who will need to be able to work with data scientists: ideally, they will all be able to work together (Davenport and Dyché, 2013). It will be challenging to integrate these people and technologies into a seamless whole that will be able to exchange relevant information and knowledge in an effective decision-making process. Let’s look more closely at how technology might be integrated in management decision-making.

A decision situation, or decision-demanding situation, refers to a situation when decision-making is required (Intezari and Pauleen, 2019). There are all kinds of decisions made in business from operational to strategic, each with its own set of challenges. In this book we tend to focus on strategic decision-making, because strategic decisions are usually the most complex and challenging kinds of decisions managers must make. Because of the considerable ambiguity, uncertainty and risk associated with strategic decisions, the effective use of big data and analytics requires high levels of human judgment, experience, expertise and knowledge to judge the value of the data and understand the implications of the analysis for the decision situation (Pauleen and Wang, 2017). Human knowledge can bring focus to the complex environments that global business must grapple with today, while analyzing and integrating data can inform and illuminate in great detail many areas of the global environment. In the field of marketing, for example, multinational consumer data and multi-source marketing data can be generated through the integration of population censuses, product types and regional industrial profiles. The use of big data at these levels will require data scientists and analysts who can collect data from different sources, extract meaning from the data through sophisticated analysis, combine analyzed data in ways that add value to the business, and present it in ways that managers can understand and use.

Information technologists bring different perspectives and kinds of knowledge and experience when they analyze big data with either a specific purpose in mind or when exploring new opportunities (Pauleen and Wang, 2017). In such cases, a data scientist would apply their knowledge to extract relevant information from big data but also when choosing the analytic tools to be used in analyzing the data. For example, key search words via text mining may be presented by data attributes of frequency, region and gender. The data scientist will decide which key words will be analyzed. This combination of data analysis and human input can generate new knowledge, which can provide insight into how to address previously defined problems or to initiate new organizational initiatives. A good example of creating knowledge based on big data and analytics to initiate new actions is Amazon’s recommender system, which uses customer data and dedicated analytics to suggest products to customers.

Finally, the information and knowledge derived from the d...