1.1 Cause and Effect

What causes one business to be successful and another to fail? This is the question that drives the field of business management. Business management (and particularly information systems) is actually a very creative discipline. There are no rules other than "do not break the law." Therefore, organizations have very wide parameters within which they can develop creative ways of achieving "above-average" gains in a market. There are a few ways to come up with good ideas: (1) take a complete random guess and hope that it works, (2) draw from your past experience (and that of your co-workers and employees) to base new ideas upon, or (3) gather data about your performance and use it to determine cause and effect.

Can you guess which one we're going to study in this chapter? Obviously not number 1. How about number 2? Well, past experience of a smart business manager is nothing to laugh at. Some people have made incredible lives for themselves off of one good idea. But if they don't adapt and change, that one good idea may never produce good results again and may be a terrible solution to future problems. That's because the past experience of just one person is VERY limited. Even the experience of two, three, or one thousand employees is quite limited. In addition, a person's experiences are interpreted (or often "mis"-interpreted) through a narrow lens of our own biases, beliefs, values, and desires. Beliefs can be distorted very easily. The truth can be stretched until it is more false than true.

That leaves us with data and good data is worth a lot. Accurate and timely data doesn't lie. It isn't biased. It doesn't care about gender, race, religion, or politics. It simply represents an objective view of the facts. Therefore, all of the very best decisions (at least in business) are generally based on data. Perhaps most importantly, it allows us to establish cause and effect. The "effect" that we are interested in is business success. That's obvious (even though success can be broken down into many, many specific and measurable outcomes). What's less obvious is the "cause" of success. This is where you have to be careful. Data allows us to measure hypothesized causes and desired successes. However, it cannot determine the true cause of each effect. It only gives you "support" for a theorized cause/effect relationship. Consider for example this chart depicting accurate data:

Does organic food cause autism? Probably not. In fact, this ridiculous chart was made as an example of how data can be terribly misinterpreted as well if you don't know how to use it. So why is there such a strong relationship in the figure above between organic food sales and autism?

In summary, data can be valuable but it can also be dangerous if interpreted "in the wrong hands" so to speak. So who are the "right hands" to interpret data? The answer to that question is those who undertstand and help to create the theories that explain organization success. However, the purpose of this book is not teach you the theories that can guide you into proper data interpretation. Rather, our focus will be on teaching you the proper techniques to analyze and make use of data. It's up to you (and the other courses you take) to learn the theories that 1) identify the relevant variables, and 2) explain the relationships among those variables that establish cause and effect

The remained of this chapter will review the high level components of the "BI stack", or the set of technologies that make data analytics possible.

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