BUS610: Advanced Business Intelligence and Analytics

This article outlines the structure, purpose, and use of decision trees. The nice thing about them is that they allow for chance, which can throw other analytic techniques into a tailspin. Remember our hapless weatherman, who carried out his analysis to a perfect outcome but was thwarted when his boss smelled french fries and ended up at the same fast food restaurant where the weatherman was hiding? Follow the logic and build a decision tree to decide something simple, but to which you can add a lot of variables, like what to have for dinner tonight. Chance occurrences include your child or roommate bringing a friend home unexpectedly.

This article provides a step-by-step process on how to build a decision tree. Follow the logic and build a decision tree to decide something simple, but to which you can add a lot of variables, like what to have for dinner tonight. Chance occurrences include your child or roommate bringing a friend home unexpectedly. As noted, decision trees are inherently built on the cost-benefit analysis model but carry it out to the furthest degree possible. Decision trees can also include a financial component. Create a decision tree to decide what your next vehicle should be. This may inform your actual purchase or be a fantasy. Remember to stick to facts when you do your analysis exercises and try to remove any biases. There are true impediments to purchasing a Lamborghini when you are a student or have several small children.

This decision tree model uses more data types and provides ways to classify data. These graphics clarify how and why decision trees can be used. Follow the examples closely to see how these might be useful in your work. Have you used decision trees in your work? Does your firm have software or other drawing tools to help you create decision trees? Without a program, the analyst has to depend on their bias reduction skills, so it is best to have a team work together to ensure all possibilities are considered and inherent biases do not creep in to rot your tree.

This article provides a nice overview of analytic tools and their value. While SACH is one of the best-known for reducing bias and ensuring all information is included, it is onerous for analysts to learn. Still, once they do, it is one of the best for ensuring a complete audit trail and keeping analysts "honest" by ensuring they include all evidence without bias. Adding automation to these tools makes them much easier to use, and as a result, they have increased, and their use has become more ubiquitous in the past decade. What structured methods have you used? Which have proven easy and which difficult? Which would you recommend to a new analyst?

ACH is one of the best for ensuring a complete audit trail and keeping analysts "honest" by ensuring they include all evidence without bias. A country stability report is one of the most basic studies new intelligence studies students complete. They use ACH to determine whether an assigned country will likely be stable in the next 18 months. Yes or No are the only options. This does not help a decision-maker trying to ensure a region of the world remains stable; they need more information. Structured ACH allows the analyst to repeat the ACH exercise, drilling deeper into the analysis until the available evidence has been exhausted. For instance, the nation of Diania is expected to become unstable in the next 18 months. So what? The analyst can repeat the analysis with hypotheses positing that instability will be caused by H1: Inflation or by H2: Unemployment. H2 is disproven because the country has had high unemployment for the past ten years, and the informal economy now functions well enough that the official numbers no longer matter.

H1, however, is of concern. Diania is in a tense standoff with neighboring Ruania over their shared main river access, with Diania starting to build a hydroelectric dam for a self-sufficient energy source. The dam will not be operational for five years. In the meantime, Ruania is threatening to double the price of oil it now sells to Duania, and winter is coming. People are likely to be unable to afford fuel to heat their homes. In this case, the long-term energy independence strategy will have little value if people freeze to death next month due to inflated oil prices. NOW the decision-maker has something to work with beyond "likely to be unstable in 18 months". They can either support the dam building, provide a subsidy for the increased cost of oil, or offer to broker an agreement between the two nations. Adding structure helps ACH to provide decision-makers with far more actionable intelligence.

Have you used ACH? Can you think of a recent decision at work or home in which using a SACH matrix might have helped you decide? Use an ACH matrix to decide between two open positions you have saved on your favorite job announcement app. Does ACH help you keep your bias from the process and make it more objective? Or did it only show you that there is one more attractive position for reasons you had not articulated before you undertook your analysis? Now you know what they are, and even if your ACH shoots them down, you may still want them... Even if ACH does not eliminate bias, when applied to personal decisions, it can at least reveal what they are. Acknowledgment is half the battle against irrational decision-making.

One of the most important lessons of effective use of ACH and many other analytic methods is to ensure you are not wedded to proving your brilliant hypotheses. The most effective approach to achieving analytic objectivity and, thus, accuracy is to work hard to disprove them. Having a diverse analytic team will help with this, especially if all members are competent enough to have confidence in their contributions and collaborative enough to invite conflict and disagreement about what the findings may seem to indicate. 

The relationship between Alice and George illustrates the aggravating, frustrating team that, if they learn to communicate and check their biases and egos at the door, can ensure accuracy and, more importantly, the seeds for effective advocacy that ensures changes and correct decisions are made. The pressure George placed on Alice daily to prove that her findings along the way and in the end were accurate despite his disputation allowed her to have the certainty and confidence to keep challenging the common wisdom about the safety of x-rays on fetuses to ultimately save the lives of millions of children. Our work may not have that consequential or widespread result, but the results will have the same relative value in the context of our teams and our organizations.

Have you ever tried to disprove your hypothesis or someone else's? Taking this approach as a single member of a team is akin to playing the "Devil's advocate" and can make you wildly unpopular, hence the name. Have you ever resented a team member who played that role? If so, did you ever recognize their value in the project? It has been proven that just playing at being an oppositional advocate is not a truly effective method. They were being contrary for contrariness' sake and risked team cohesiveness. If the person playing the role is doing so for the right reasons, to ensure a lack of bias and full accuracy, the effect on the overall findings can be enormously positive, as in the case of Alice and George. One of the most important lessons of effective use of ACH and many other analytic methods is to ensure you are not wedded to proving your brilliant hypotheses. The most effective approach to achieving analytic objectivity and, thus, accuracy is to work hard to disprove them. Having a diverse analytic team will help with this, especially if all members are competent enough to have confidence in their contributions and collaborative enough to invite conflict and disagreement about what the findings may seem to indicate.

This brief overview is a good introduction to thinking about predictive modeling.

This fascinating talk describes big data and how it can be used today to ensure everyone in your family can have their favorite pie. Is this why Big Data exists? Well, sort of... It can help find the tiniest niche products and connect them to the people who want them, so in a way, it helps you get the pie you want and not make you always get stuck with apple. However, it has and will have many other uses, some of which may sound scary but still worth knowing about. How likely could it be that learning machines eliminate your job? What can you do to prepare yourself to deal with this future scenario? You might be able to apply a decision tree or ACH to help support your predictive analysis.

Review this presentation and learn from its many examples of preparing data for improved analysis. However, it is optional, only provided for additional information, and is not central to your learning about predictive analytics.

Here is a small list of various analytic methods. How many of these methods have you used? How could these ideas be applied in your work?

This blog entry speaks about role-playing in intelligence analysis in the context of a historically-oriented game. Write about a time when you have found yourself unable to rely on a key component of a complex plan, whether it was the weather, a ride, a working computer, an accomplice, etc. Were you able to adapt? If so, you have shown the creative insight needed to be a successful intelligence analyst.

This article gives an overview of what BI is and how it can provide actionable information for a business. It provides a brief testimonial on a single case study and a well-done video giving examples of how it can be used. A key point from the article is that the activity of conducting BI can be more broadly dispersed throughout organizations rather than left to a small cadre of analysts with special technical skills who "owned" the data but did not necessarily understand how different departments and departments and managers could best use it. Constantly evolving dashboards and data packages make analysis more accessible. However, it is still important not to let the data be used randomly by anyone with access, as the results may not be sound.

Consider when you could have used big data to answer a manager's question. Did you have instant access to all the information you needed, or did you have to ask an IT or other area specialist? Was it difficult to obtain? Did you end up getting what you needed? Did you have to spend a lot of time manipulating the data to put it into a usable form? How does your organization collect, organize and disseminate its data?