Data Science Methodology 101 – Business Understanding Concepts and Case Study

Data Science Methodology 101 – Business Understanding Concepts and Case Study


Welcome to Data Science Methodology 101 From
Problem to Approach Business Understanding! Has this ever happened to you? You’ve been called into a meeting by your
boss, who makes you aware of an important task one with a very tight deadline that
absolutely has to be met. You both go back and forth to ensure that
all aspects of the task have been considered and the meeting ends with both of you confident
that things are on track. Later that afternoon, however, after you’ve
spent some time examining the various issues at play, you realize that you need to ask
several additional questions in order to truly accomplish the task. Unfortunately, the boss won’t be available
again until tomorrow morning. Now, with the tight deadline still ringing
in your ears, you start feeling a sense of uneasiness. So, what do you do? Do you risk moving forward or do you stop
and seek clarification. Data science methodology begins with spending
the time to seek clarification, to attain what can be referred to as a business understanding. Having this understanding is placed at the
beginning of the methodology because getting clarity around the problem to be solved, allows
you to determine which data will be used to answer the core question. Rollins suggests that having a clearly defined
question is vital because it ultimately directs the analytic approach that will be needed
to address the question. All too often, much effort is put into answering
what people THINK is the question, and while the methods used to address that question
might be sound, they don’t help to solve the actual problem. Establishing a clearly defined question starts
with understanding the GOAL of the person who is asking the question. For example, if a business owner asks: “How
can we reduce the costs of performing an activity?” We need to understand, is the goal to improve
the efficiency of the activity? Or is it to increase the businesses profitability? Once the goal is clarified, the next piece
of the puzzle is to figure out the objectives that are in support of the goal. By breaking down the objectives, structured
discussions can take place where priorities can be identified in a way that can lead to
organizing and planning on how to tackle the problem. Depending on the problem, different stakeholders
will need to be engaged in the discussion to help determine requirements and clarify
questions. So now, let’s look at the case study related
to applying “Business Understanding” In the Case Study, the question being asked
is: What is the best way to allocate the limited healthcare budget to maximize its use in providing
quality care? This question is one that became a hot topic
for an American healthcare insurance provider. As public funding for readmissions was decreasing,
this insurance company was at risk of having to make up for the cost difference,which could
potentially increase rates for its customers. Knowing that raising insurance rates was not
going to be a popular move, the insurance company sat down with the health care authorities
in its region and brought in IBM data scientists to see how data science could be applied to
the question at hand. Before even starting to collect data, the
goals and objectives needed to be defined. After spending time to determine the goals
and objectives, the team prioritized “patient readmissions” as an effective area for review. With the goals and objectives in mind, it
was found that approximately 30% of individuals who finish rehab treatment would be readmitted
to a rehab center within one year; and that 50% would be readmitted within five years. After reviewing some records, it was discovered
that the patients with congestive heart failure were at the top of the readmission list. It was further determined that a decision-tree
model could be applied to review this scenario, to determine why this was occurring. To gain the business understanding that would
guide the analytics team in formulating and performing their first project, the IBM Data
scientists, proposed and delivered an on-site workshop to kick things off. The key business sponsors involvement throughout
the project was critical, in that the sponsor: 1. Set overall direction
2. Remained engaged and provided guidance
3. Ensured necessary support, where needed Finally, four business requirements were identified
for whatever model would be built. Namely: 1. Predicting readmission outcomes for those
patients with Congestive Heart Failure 2. Predicting readmission risk
3. Understanding the combination of events that
led to the predicted outcome 4. Applying an easy-to-understand process to
new patients, regarding their readmission risk. This ends the Business Understanding section
of this course. Thanks for watching!

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About the Author: Oren Garnes

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