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Data Collection PowerPoint Presentation

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The following video shows this training material in action. The full video is available as a complementary product (DVD, Corporate License Video, or Independent Study Pack) to this instructor pack.

PowerPoint Class

Data Collection PowerPoint Lean Training

  • License Type: Corporate
  • Number of Slides: 21
  • Target Time: 30 (+30 minutes for optional exercise)
  • Format: PowerPoint (PPTX)
  • Delivery: Immediate Download

Every facet of Lean requires knowing where you are starting, and if the changes you make are actually improving the process. For that reason, data collection is a fundamental Lean skill that lays the foundation for successful improvement efforts. Poor data collection sets managers and their teams up for failure. Good data collection makes improvement faster and easier.

This PowerPoint presentation shows the basics of data collection, and has a few built in exercises to drive points home. There are also a few optional companion exercises that can increase the engagement and retention of your trainees.

This Data Collection PowerPoint presentation covers the following topics:

1. Purpose of data collection

a. Provide basis for decision making

2. Costs of data collection

a. Planning costs

b. Collection cost

c. Usage costs

3. How much data should you collect

a. Too little

b. Too much

4. Where to determine data needs

a. Project Charter

b. Current Process

c. Quality information

5. Types of data

a. Quantitative data

i. Continuous (or variable) data

ii. Discrete data

b. Qualitative date

i. Attribute data

1. Ordinal data

2. Nominal data

ii. Open data

6. Data collection methods

a. Tools for collecting data

i. Enterprise software systems / databases

ii. Checksheets

iii. Travelers

iv. Logs

7. Turning data into information

a. Identifying data needs

b. Reviewing existing data

c. Developing a data collection plan

i. Balancing the need to predict data requirements with the risk of steering teams toward specific solutions

8. Data collection steps

9. Avoiding bias in data collection

a. Sources of bias

10. Sampling

11. Statistical significance

12. How people react to data

13. Data perception exercises

14. Pitfalls to avoid


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