Introduction to Business Analytics, Second Edition

Marguerite L. Johnson, David L. Olson, Wesley S. Boyce

• Publisher: Business Expert Press
• Year: 2020
• ISBN:
  9781953349750
• Topic: Economía
• Subtopic: Estadísticas para los negocios y la economía

This book presents key concepts related to quantitative analysis in business.

It is targeted at business students (both undergraduate and graduate) taking an introductory core course. Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts.

This second edition adds material on Tableau, a very useful software for business analytics. This supplements the tools from Excel covered in the first edition, to include Data Analysis Toolpak and SOLVER.

CHAPTER 1

Business Analytics

Business analytics refers to the use of quantitative analysis to support managerial decision making. It is concerned with the process of managerial decision making, as well as the tools used to support it (management science). Information is a valuable asset requiring careful management, to make sure that businesses are able to accomplish their missions, no matter what those might be. In the agricultural domain, the production, processing, and marketing of food to keep us all going involves many participants, although there are a relatively small number of major producers such as ConAgra, ADM, Kellogg’s, and so on. These major producers have massive data processing challenges, concerning weather, markets, production of many commodities across the world, and many types of demand. In the retail domain, WalMart and its competitors deal with many products coming from many places, and stock these products in many retail outlets, served by a massive distribution network. Keeping on top of data is crucial to WalMart’s success, served by what is probably the world’s largest data processing system. Distribution requires a massive transportation network. One element is the airline industry, which has to keep track of many customers seeking to travel from one location to another. Governments also have massive data needs. Providing protection to citizens requires collecting and processing data on airline traffic, shipping, road networks, population movements, economic activity, and risks of nature in the form of weather, volcanic activity, tsunamis, and so on.

Information

Information can be viewed in a variety of ways. You might think of raw data as an initial form that humans need to process into understanding through various stages. But classifying these stages is probably unnecessary— our world today is flooded with data from a variety of forms. For almost centuries, the U.S. government has collected census data (as the Romans did millennia ago). In the last century, a sophisticated system of economic data collection was developed in the United States, followed by OECD in Europe, and UN data of various types. Businesses also collect data, seeking to gain competitive advantage by understanding what the opportunities are in their markets. Science has been in favor of open data, shared so that humanity as a whole can gain understanding more rapidly. The medical scientific field consists of a complex network of researchers seeking cures to cancer and other evils. The business domain overlaps this scientific endeavor in the form of the pharmaceutical industry, resulting in an interesting dichotomy of interest between open sharing of information for the sake of progress versus intellectual property protection to further pharmaceutical profitability.

There are two major developments in recent years that have revolutionized the field of information. Supply chain networks have evolved, enabling linking a multitude of vendors from practically anywhere across the globe with businesses processing or manufacturing products. Their production expertise can often be applied in many different locations, enabling them to find cheaper labor to make products, transported over a sophisticated distribution network. It is astounding to consider how fruits from Central or South America, grains from Africa as well as the Midwestern U.S., and meats from Australia and Argentina can find their way to our groceries in Europe or the United States. The resulting supply chain networks usually find it worthwhile to be open in sharing data with their supply chain partners, much as keiretsus and chaebols have operated in eastern Asia.

A second major development is the explosion of social media, which has led to linkages of people around the globe. Many countries find far more people using cell phones than landlines, and even places like the United States where landlines have been in place for over 100 years now see most people abandoning the old systems for cheaper and more mobile cell phones. These devices, along with a plethora of other platforms such as smartphones and tablets supported by free access to the World Wide Web, enable people to talk to each other over Facebook and to purchase products from organizations such as Amazon. This has led to masses of data being accessible to businesses to collect and analyze (a business form of Big Data).

Business Decision Making

Science seeks to gain complete understanding of whatever topic is under consideration, identifying the entire system of interacting parts, how they relate to each other and how this leads to outcomes. The field of physics has successfully been able to send rockets (some occupied by humans) to the moon, and more impressively, to return safely. Geology has studied the components of the earth and scientists are able to identify properties useful to growing crops in particular locations, as well as identify likely places for oil discovery or the discovery of rare earth elements. Chemistry has been able to develop combinations of atoms to propel vehicles, to blow things up, and to deliver pills making us feel better. Biology seeks to understand how organisms go through their life cycles.

Science has accomplished a great deal, but not that humans have not gained perfect understanding of anything. The degree of how well bodies of scientific theory can explain natural phenomena varies. In physics and geology, the scientific process has accomplished a great deal. Chemistry has also seen many useful gains, although the interactions of various drugs on humans are far from understood. Biology includes even greater uncertainty, and medical science has a long way to go to master human and other animal systems. When you get to economics and other human behavior, the mysteries deepen.

Business has always involved the interactions of many people. These people make assumptions, to cope with the complexities of their lives. Making assumptions is a part of theory building necessary to surviving everyday life. But many times assumptions are based on false speculation, resulting in incorrect understanding and poor decisions. The contention we propose is that gathering data and measuring what is going on in businesses can lead to better understanding, and consequently, better decision making. This is not expected by any means to be a cure-all. No matter how much we think we know, there will be mistakes in our theories of cause and effect, as well as our understanding what is currently going on. But we have to keep trying.

LaPlace once contended that if he knew the starting conditions, he could calculate how anything would behave. This represents a type of reductionism, which pure scientists often adopt. It is a Newtonian view of strict causality, which implies that if you can’t measure and explain, it isn’t scientifically understood. This view complies with the idea of determinism, that everything that unfolds in the world is describable by natural law.

This idea is not necessarily wrong, but in the domain of business seems impractical, either due to the complexity involved in our world, or the continual change endemic to natural as well as human activity. Thus it is necessary to view life more flexibly, to understand that systems are complex and change, and that what has happened in a particular context in the past is not necessarily going to be the same in the future. Business has to face changes in laws and regulations, in societal attitudes regarding what is acceptable behavior, and the high levels of uncertainty involved in markets.

Thus there is not “only one best way” in any business decision context. Management is getting things done through people, and whenever you have people involved, you find changes in mood in individuals, and different attitudes across people, and different market behavior for different profiles of customers. Management thus has to combine a scientific-like process of theory verified by observation along with an art of getting along with a wide variety of people. Managerial decision making is the most fundamental function of management, and a very difficult task where demanding stakeholders are never satisfied.

Scientific Method

Science is a process, seeking to carefully study a problem and evolve over time with a complete mathematical description of a system. This seeks to be completely objective, measuring everything accurately and developing theories with no emotional bias. This scientific approach, as we have alluded to before, has served humanity well in many environments. We think it seems to do better when human choice is not involved, as we humans have always reserved the right to change our minds and behavior.

The scientific method can be viewed as a process consisting of the following elements:

• Define the problem (system)

• Collect data

• Develop hypotheses of cause and effect in the interaction of system elements

• Test these hypotheses

• Analyze results

• Refine our mental models or theories of cause and effect

This scientific method has led to impressive understanding of the laws of physics. Astronomers continue to expand our understanding of the universe, through more powerful telescopic tools as well as spectral analysis of elements. Einstein was able to formulate a mathematical expression of the relationship between energy, mass and light. USSR cosmonauts and NASA in the United States were able to send rockets into orbit, and to enable men to walk on the moon.

It does make sense to try to apply this same mindset to business. Businesses are complex collections of individuals with different backgrounds, training, and roles, each with their own set of ambitions and agenda. Thus there will often be differing theories, biased by perceptions of self-interest, among the people in any organization. We expect that those that operate more objectively will prevail (although life doesn’t guarantee that), and that businesses will do better if they are guided by leaders seeking to be as scientific as they can be, given that they understand that economic activities are very complex and changeable.

Management Decision Process

Keeping in mind that we don’t expect that you can be completely scientific in business, a sound objective approach would be expected to serve businesses better than a chaotic decision making environment that operates at random. Thus an analogy to the scientific method outlined earlier might be:

• Define the decision problem (what products to carry)

• Search for data and information (what demand has been observed in this area)

• Generate alternative actions (gather prospective vendor products and prices)

• Analyze feasible alternatives (consider price, quality, and delivery)

• Select best action (select products and vendors)

• Implement (stock your outlets)

This process provides a means to be as scientific as possible within the confines of the chaos and uncertainty of business. Defining decision problems is often imposed by the job business people have. Getting data is critical. Organizational information systems are designed to provide reports that are expected to provide key information enabling everybody in the organization to do their job. But these reports rarely include everything that matters, so employees have to supplement ERP reports with other information. Observation and talking to people is a good source. But we can also go find other information, on the Web, in libraries, even creating experiments to try to understand the systems we have to deal with. Statistical analysis provides valuable tools to monitor performance.

Generating alternative actions is often a matter of creativity. Part of the job of management is to monitor your responsibilities to identify where action is required. This is hard, because it is human nature to see maybe nine problems for every real problem. Systems often self-correct, and micromanaging can be highly detrimental. Experience is extremely valuable in this context, learning when to take action and when to leave things alone. If action is taken, there are many things that could be done. We will look at models as means to anticipate expected outcomes, or in special circumstances, suggest optimal solutions. They are valuable tools for analysis of alternative actions.

Selecting the action to take should not be surrendered to models. Every model involves assumptions, and the models that appear the most powerful usually involve the most assumptions. Thus human judgment needs to be the final decision maker. Models provide useful input, and means to experiment. But we still live in a world where humans are in charge of computers.

Once action is taken, the results need to be monitored. If you make a decision to raise prices on your product, you need to monitor the impact in demand (which probably will go down, but you won’t know by how much until you observe and measure).

Management Science

Management science is the field of study seeking to apply quantitative models to common management decisions. It is a parallel field to operations research, which developed in the engineering field. The modeling tools used are common to both fields. These models allow decision makers to experiment, intending to learn more about their operations.

Management science involves purpose-oriented decision making, following the management decision process we have just outlined. Systems have to be un...