How to approach the problem by using Framework CRISP-DM and choose appropriate models for the analysis?

Introduction

This article will answer the following questions:

How a data analyst approach business problems by using CRISP-DM?

How to define appropriate models for the decision?

Framework CRISP-DM

Firstly, by using CRISP-DM which is a problem-solving framework, we generalize common approaches to defining and analyzing a problem

The framework is made up of 6 steps:

1. Business Issue Understanding
2. Data Understanding
3. Data Preparation
4. Analysis/Modeling
5. Validation
6. Presentation/Visualization

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Business Problem

“We’re run a big campaign on the e-commerce platform to grow revenue in the following days. It’s critical that we must ensure enough stock for the campaign. We need to predict how many items will be sold during the campaign, do our current stocks are enough?”

1. Business Issue Understanding

• Does the current stock enough for the campaign? If not, how much should be added more?
• Information for this decision: How many items will be bought on the campaign day? Unfortunately, it is a future story, we don’t have exact data at the time we propose the decision. So, we need to predict. But how to forecast?

2. Data Understanding

• To predict, let’s define which factors drive the number of items sold for the campaign?
• What data is needed?

3. Data Preparation:

• Gathering: When gathering data — you may need to collect data from multiple sources within your organization.
• Cleansing: The data set you are working with may have issues that you want to resolve prior to your analysis. This can be in the form of incorrect or missing data.
• Formatting: You may need to format the data by changing the way a date field appears, renaming a field, or even rotating the data, similar to using a pivot table.
• Blending: You may want to blend, or combine, your data with other datasets to enrich it with additional variables, similar to using the vlookup function in Excel.
• Sampling: Lastly, you may want to sample the dataset and work with a more manageable number of records.

4. Define appropriate modeling/analysis by using Methodology Map

• Determine what methodology to use to solve the problem
• Determine the important factors or variables that will help solve the problem
• Build a model to solve the problem
• Run the model and move to the validation phase

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5. Validation

• Observe the key results on the model
• Ensure the results make sense within the context of the business problem
• Determine whether to proceed to the next step or return to a previous phase
• Repeat as many times as necessary

6. Presentation/Visualization

• Determine the best method of presenting insights based on the analysis
• Determine the best method of presenting insights based on the audience
• Make sure the amount of information shared is not overwhelming
• Use the results to tell a story to the audience
• For more complex analyses, you may want to walk the audience through the analytical problem-solving process
• Always reference the data sources used
• Make sure your analysis supports the decisions that need to be made