Improvement as Data Analyst presents business problems, different problem-solving tools (5 Why, Action Priority Chart, Fishbone, and Flow Mapping), and data analysis process.

1. Data Analyst
Oleh:
Improvement as Data
Analyst
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Elyada Wigati Pramaresti

2. #RintisKarirImpian
Course Summary
Topics Summary
Data Analysis Fundamental • Data analytics is the concept and practice of all activities related to
data.
• Data analysis is the process of data collection, data cleaning,
transformation, data visualization, and data modeling to help
decision-making.
• Data analysis can be a validation of certain information.
• The contribution of data analysis:
- Creates a better decision
- Lessens the business’ risks
- Increases transparency and objectivity
- Improves business control

3. Topics Summary
Data Analysis
Fundamental
• Most popular tools for data analysis:
1. SQL
- Used every day by data analyst
- Used by data analysts to interact with data in the database
2. Python
- Applied to process big data
- Create statistical models and machine learning
3. BI Tools
- Used to create a dashboard for data visualization
Understanding Business
Problem
• General steps of problem-solving:
1. Understand the hypothetical factors and context
2. Determine the stakeholders to be asked for information
3. Create the framework to determine causal factors

4. Topics Summary
Understanding Business
Problem
• Problem solving tools:
1. 5 Why
Repeatedly asking about the cause of the problems until
an objective, clear, and right answer is obtained
2. Action priority chart
It is used to prioritize the problems based on their impacts and
benefits to the organization’s goal.
3. Fishbone diagram
It is used to seek and explain the root causes of different
point of views
4. Flowchart/Algo
Creates pseudo algorithms to determine the problems and
systematically seek the solutions

5. Topics Summary
Understanding Business
Problem
• Problem solving tools:
1. 5 Why

6. Topics Summary
Understanding Business
Problem
• Problem solving tools:
2. Action Priority Chart

7. Topics Summary
Understanding Business
Problem
• Problem solving tools:
3. Fishbone

8. Topics Summary
Understanding Business
Problem
• Problem solving tools:
4. Flowchart/ Algo

9. Topics Summary
Data Analysis Process • Plan = Identify the problem and make some hypothesis
• Do = Testing the hypothesis
• Check = Analyze the test result
• Act = Implementing the suitable new standard

10. #RintisKarirImpian
Case Study
Sebuah perusahaan telekomunikasi yang ada di
Indonesia ingin meningkatkan retensi
pelanggan. Tentu saja mereka membutuhkan
seorang data analyst untuk mengetahui dan
memahami pola perilaku pelanggan lainnya
yang melakukan retensi. Analisalah masalah ini
menggunakan pendekatan PDCA.

11. #RintisKarirImpian
Framework PDCA
Plan Do Check Action for Data Analyst
Plan Do
Action
Check
Planning by identifying
the problems and making
some hypothesis
Applying the plan
through testing
Evaluate the result to
prevent repeated errors
Applying the new
business standards and
monitoring the results

12. #RintisKarirImpian
Analysis using PDCA Framework
PDCA Analysis
Plan • Plan the objective for increasing retention and target its escalation number.
• Determine the problems and create some hypotheses about the factors that
affect the customers’ retention like customer service, price, product quality, and
promotion
• Collecting the required data for the testing and analysis. These include the
customers’ data like age, gender, and residency; the transaction data such as
transaction date and how many items per purchase; the number of repeated
purchases; and the customers; satisfaction data.
• Planning for the hypothesis verification.

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Analysis using PDCA Framework
PDCA Analysis
Do • Carrying out the data collection. It can be obtained from the company’s
database or by applying questionnaires to get the new data that is not yet
stored in the database.
• Screening the data to identify their structure and quality.
• Processing the data by cleaning the invalid data. This is necessary to
prevent bias during the checking process. The data analyst can also
combine the relevant data from other sources.

14. #RintisKarirImpian
Analysis using PDCA Framework
PDCA Analysis
Check • Implementing statistical methods to create predictive models. In this phase,
the data analysts assess the behavior patterns of the consumers and identify
the relevant variables that affect their behaviors.
• Evaluate the predictive models by using evaluation metrics such as accuracy
and F1 score.
• Assess whether the results meet the retention target.

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Analysis using PDCA Framework
PDCA Analisis Kamu
Act • Giving recommendations to the users. These can be adjusted pricing, product
quality improvement, improving customer service, and strategic product
promotion.
• Carry out monitoring to see the new standard implementation results in the
customers’ retention. See if the results meet the desired target.

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Bootcamp Data Analysis
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