Data Analytics#05
ch-02 Data to Insight to Decisions
Hello!!
I hope you enjoyed our previous newsletter of predictive Data Analytics Project Lifecycle: CRISP-DM and how it helps in future of predictive data analytics. Let’s continue to our previous discussion with new topic “Data Insight to Decision”.
Predictive data analytics projects are not handed to data analytics practitioners fully formed. Rather, analytics projects are initiated in response to a business problem, and it is our job— as analytics practitioners—to decide how to address this business problem using analytics techniques. In the previous episode we present an approach to developing analytics solutions that address specific business problems. This involves an analysis of the needs of the business, the data we have available for use, and the capacity of the business to use analytics. Taking these factors into account helps to ensure that we develop analytics solutions that are effective and fit for purpose. Now in this chapter we move our attention to the data structures that are required to build predictive analytics models, and in particular the analytics base table (ABT). Designing ABTs that properly represent the characteristics of a prediction subject is a key skill for analytics practitioners. We present an approach in which we first develop a set of domain concepts that describe the prediction subject and then expand these into concrete descriptive features. Throughout the chapter we return to a case study that demonstrates how these approaches are used in practice
In this episode we move our attention to converting Business Problem into Analytics Solution.
Analytics solution that the analytics practitioner will set out to build using machine learning. Defining the analytics solution is the most important task in the Business Understanding phase of the CRISP-DM process.
2.1 Converting a business problem into an analytics solution:
1.What is the business problem? What are the goals that the business wants to achieve?
These two questions are not always easy to answer. In many cases organization begin analytics projects because they have a clear issue that they want to address. Unless a project is focused on clearly stated goals, it is unlikely to be successful. The business problem and goals should be expressed in business terms and not yet be concerned with the actual work at this stage.
How does the business currently work?
Analytics practitioners must possess Situational fluency. This means that they understand enough about a business so that they can converse with partners in the business in a way that these business partners understand.
For example, in the insurance industry, insurance policyholders are usually referred to as members rather than customers. Although from an analytics perspective, there is really little difference, using the correct terminology makes it much easier for business partners to engage with the analytics project. Beyond knowing the correct terminology to use, an analytics practitioner who is situationally fluent will have sufficient knowledge of the quirks of a particular domain to be able to competently build analytics solutions for that domain.
In what ways could a predictive analytics model help to address the business problem?
For any business problem, there are a number of different analytics solutions that we could build to address it. It is important to explore these possibilities and, in conjunction with the business, to agree on the most suitable solution for the business. For each proposed solution, the following points should be described: (1) the predictive model that will be built; (2) how the predictive model will be used by the business; and (3) how using the predictive model will help address the original business problem.
Now, let’s discuss a case study of process of converting business problem into a set of candidate analytical solution.
Case Study: Motor Insurance Fraud:
Consider the following business problem: in spite of having a fraud investigation team that investigates up to 30% of all claims made, a motor insurance company is still losing too much money due to fraudulent claims. The following predictive analytics solutions could be proposed to help address this business problem:
[Claim prediction]: A model could be built to predict the likelihood that an insurance claim is fraudulent. This model could be used to assign every newly arising claim a fraud likelihood, and those that are most likely to be fraudulent could be flagged for investigation by the insurance company’s claims investigators. In this way the limited claims investigation time could be targeted at the claims that are most likely to be fraudulent, thereby increasing the number of fraudulent claims detected and reducing the amount of money lost to fraud.
[Member prediction]: A model could be built to predict the propensity of a member1 to commit fraud in the near future. This model could be run every quarter to identify those members most likely to commit fraud, and the insurance company could take a risk-mitigation action ranging from contacting the member with some kind of warning to canceling the member’s policies. By identifying members likely to make fraudulent claims before they make them, the company could save significant amounts of money.
[Application prediction]: A model could be built to predict, at the point of application, the likelihood that a policy someone has applied for will ultimately result in a fraudulent claim. The company could run this model every time a new application is made and reject those applications that are predicted likely to result in a fraudulent claim. The company would therefore reduce the number of fraudulent claims and reduce the amount of money they would lose to these claims.
[Payment prediction]: Many fraudulent insurance claims simply over-exaggerate the amount that should actually be paid out. In these cases the insurance company goes through an expensive investigation process but still must make a reduced payment in re lation to a claim. A model could be built to predict the amount most likely to be paid out by an insurance company after having investigated a claim. This model could be run whenever new claims arise, and the policyholder could be offered the amount predicted by the model as settlement as an alternative to going through a claims investigation pro cess. Using this model, the company could save on claims investigations and reduce the amount of money paid out on fraudulent claims.
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In next episode we will continue our topic with Assessing Feasibility of each solution of problem.