One cannot underestimate how important this first step of the process is in achieving success. It is the foundational step and failure or success here will likely determine failure or success for the rest of the project. The purpose of this step is to identify the requirements of the business so that you can translate them into analytical objectives. It has the following four tasks:

After enduring the all-important pain of the first step, you can now get your hands on the data. The tasks in this process consist of the following:

This step is the classic case of ETL is Extract, Transform, Load. There are some considerations here. You need to make an initial determination that the data available is adequate to meet your analytical needs. As you explore the data, visually and otherwise, determine if the variables are sparse and identify the extent to which the data may be missing. This may drive the learning method that you use and/or whether the imputation of the missing data is necessary and feasible.

Verifying the data quality is critical. Take the time to understand who collects the data, how it is collected, and even why it is collected. It is likely that you may stumble upon an incomplete data collection, cases where unintended IT issues led to errors in the data, or there were planned changes in the business rules. This is critical in the time series where often business rules change over time on how the data is classified. Finally, it is a good idea to begin documenting any code at this step. As a part of the documentation process, if a data dictionary is not available, save yourself the heartache later on and make one.

Almost there! This step has the following five tasks:

These tasks are relatively self-explanatory. The goal is to get the data ready to input in the algorithms. This includes merging, feature engineering, and transformations. If imputation is needed, then it happens here as well. Additionally, with R, pay attention to how the outcome needs to be labeled. If your outcome/response variable is Yes/No, it may not work in some packages and will require a transformed or no variable with 1/0. At this point, you should also break your data into the various test sets if applicable: train, test, or validate. This step can be an unforgivable burden, but most experienced people will tell you that it is where you can separate yourself from your peers. With this, let's move on to the money step.

This is where all the work that you've done up to this point can lead to fist-pumping exuberance or fist-pounding exasperation. But hey, if it was that easy, everyone would be doing it. The tasks are as follows:

Oddly, this process step includes the considerations that you have already thought of and prepared for. In the first step, one will need at least a modicum of an idea about how they will be modeling. Remember, that this is a flexible, iterative process and not some strict linear flowchart such as an aircrew checklist.

The cheat sheet included in this chapter should help guide you in the right direction for the modeling techniques. A test design refers to the creation of your test and train datasets and/or the use of cross-validation and this should have been thought of and accounted for in the data preparation.

Model assessment involves comparing the models with the criteria/criterion that you developed in the business understanding, for example, RMSE, Lift, ROC, and so on.

With the evaluation process, the main goal is to confirm that the work that has been done and the model selected at this point meets the business objective. Ask yourself and others, have we achieved the definition of success? Let the Netflix prize serve as a cautionary tale here. I'm sure you are aware that Netflix awarded a $1 million prize to the team that could produce the best recommendation algorithm as defined by the lowest RMSE. However, Netflix did not implement it because the incremental accuracy gained was not worth the engineering effort! Always apply Occam's razor. At any rate, here are the tasks:

In reviewing the process, it may be necessary—as you no doubt determined earlier in the process—to take the results through governance and communicate with the other stakeholders in order to gain their buy-in. As for the next steps, if you want to be a change agent, make sure that you answer the what, so what, and now what in the stakeholders' minds. If you can tie their now what into the decision that you made earlier, you are money.

If everything is done according to the plan up to this point, it might just come down to flipping a switch and your model goes live. Assuming that this is not the case, here are the tasks of this step:

After the deployment and monitoring/maintenance is underway, it is crucial for yourself and those that will walk in your steps to produce a well-written final report. This report should include a white paper and briefing slide. I have to say that I resisted the drive to put my findings in a white paper as I was an indentured servant to the military's passion for PowerPoint slides. However, slides can and will be used against you, cherry-picked or misrepresented by various parties for their benefit. Trust me, that just doesn't happen with a white paper as it becomes an extension of your findings and beliefs.

Now for the all-important process review. You may have your own proprietary way of conducting it, but here is what it should cover, whether you conduct it in a formal or informal way:

That concludes the review of the CRISP-DM process, which provides a comprehensive and flexible framework to guarantee the success of your project and make you an agent of change.

The purpose of this section is to create a tool that will help you not just select the possible modeling techniques but also to think deeper about the problem. The residual benefit is that it may help you frame the problem with the project sponsor/team. The techniques in the flowchart are certainly not comprehensive but are exhaustive enough to get you started. It also includes techniques not discussed in this book.

The following figure starts the flow of selecting the potential modeling techniques. As you answer the question(s), it will take you to one of the four additional charts:

Figure 1

If the data is a text or in the time series format, then you will follow the flow in the following figure:

Figure 2

In this branch of the algorithm, you do not have a text or the time series data. Additionally, you are not trying to predict what category the observations belong to.

Figure 3

To get to this section, you would have data that is not text or time series. You want to categorize the data, but it does not have an outcome label, which brings us to clustering methods, as follows:

Figure 4

This brings us to a situation where we want to categorize the data and it is labeled, that is, classification:

Figure 5

This chapter was about how to set yourself and your team up for success in any project that you tackle. The CRISP-DM process is put forward as a flexible and comprehensive framework in order to facilitate the softer skills of communication and influence. Each process step and the tasks in each step were enumerated. More than that, the commentary provides some techniques and considerations to help in the process execution. By taking heed of the process, you can indeed become an agent of positive change to any organization.

The other item put forth in this chapter was an algorithm flowchart; a cheat sheet to help in identifying the proper techniques to apply in order to solve the business problem. With this foundation in place, we can now move on to applying these techniques to real-world problems.