When using the DMAIC methodology, you should create a data collection plan during the Measure phase. You will find that the data collection plan is a useful tool, especially when trying to focus your data collection efforts.

Why Do You Need a Data Collection Plan?

Achieve Context

Simply getting “all of the data” and looking at it overwhelms even the best analysts. In addition to being difficult, you will find that this approach does not yield great results either. Another pitfall: you could interpret the data in an incredibly unhelpful way. Here’s a brilliant 5 min TED talk that illustrates great data analysis flawed by not setting the great context:

Save Resources

How many people work on projects with unlimited resources, no deadlines, and an unlimited budget? Not very many, right? Getting data takes a lot of time and may be expensive. It seems the rest of us need a better way.

Bounds of Reality

It may not be possible to get all the data we want.

Why you need a data collection plan.
Why you need a data collection plan.

This is Six Sigma! We want to be efficient! By creating a data collection plan, you can focus on answering questions with business value. The directed approach helps you avoid locating & measuring data just for the sake of doing so.

“Acknowledging what you don’t know is the dawning of wisdom.” —Charlie Munger

Data Integrity

Data integrity is crucial for data collection and is a huge benefit to creating a data collection plan. You want to ensure the data you’re gathering is complete, accurate, reliable, and consistent. Additionally, a well-assembled plan will give you a sense of security, indicating that you collected the data while adhering to standards and compliance.

Data Integrity ensures that the data accurately reflects the actual observations or measurements and is free from errors, omissions, or biases. Maintaining data integrity is crucial for drawing valid conclusions and making informed decisions based on the collected data.

To ensure data integrity, a well-defined data collection plan typically includes detailed procedures, training for data collectors, data validation checks, and mechanisms to address and correct errors. Implement regular monitoring and quality assurance processes to maintain data integrity throughout the data collection period.

Accuracy


Collect data precisely, reflecting the actual values of the variables being measured. This involves minimizing errors in data entry, measurement, or observation. Data accuracy is essential in data collection.

Creating a data collection plan helps ensure you collect data with minimal error. Some examples of how data collection plans can provide more accurate data include creating a centralized database, establishing procedures to protect data permissions, syncing data sources, etc. If the data occurs in a time sequence, record the order in which it happened. Remove data entry errors, digital data transportation errors, and unnecessary rounding errors.

Processing Tools

Finally, data collection plans are great avenues to implement and manage processing tools within Six Sigma. Certainly, you can purchase and install any tool you want. Various data processing tools cater to different aspects of data handling, manipulation, and analysis.

The choice of tools often depends on the data’s specific needs, scale, and nature. Various online tools are available for data collection across different purposes and industries. The choice of tool often depends on the type of data you need to collect and the specific requirements of your project. However, without a plan that outlines the responsibilities and expectations of the processing tools, they can skew and alter data.

How to Create a Data Collection Plan

Step 1: Identify the Questions we want to answer.

Our data must be relevant to the project. What is your project’s hypothesis? And what are we trying to answer? The entire reason to have a DMAIC project is to improve a process so these questions should be centered around what the reality of your process is. And that’s best discovered by defining the current state.

What happens if we gather data instead of making a data collection plan?

Collecting data ‘just to see what’s out there’ is a poor approach that leads to bloat and wasted effort. It may lead you to collecting the wrong data – or collecting the data a wrong manner. By starting with the questions you want to ask you can then determine what kind of data (and in what manner) would help you definitively answer those questions. This will lead you to higher-quality solutions.

Don’t Forget: A data collection plan begins and ends with people.

To better avoid errors, you should talk to people who disagree with you and you should talk to people who are not in the same emotional situation you are.” — Daniel Kahneman

“When a possibility is unfamiliar to us, we do not even think about it.” — Nate Silver

A great best practice is to use the SIPOC as a guide for data collection.

Figure out the type of measurements we want to include.

2-3 Output measures

1-2 Input measures

1 process measure

Use the critical-to-quality tree to reduce the number of outputs brainstormed while Process Mapping down to two or three as part of the Data Collection Plan creation steps.

A Good Checklist for Your Data Collection Plan

Your data should meet these criteria.

  • Answer specific questions that are linked to your project’s goals.
  • Is feasible to collect (on time, with your budget, appropriate effort)
  • Considers related & influencing conditions.
  • Provides insight to the process
  • Can be found on the Input-Process-Output diagram of your SIPOC.

Step 2: What kind of data is available?

Now we break those questions down into their parts. What data exists that can give us these answers – or part of these answers.
Sometimes a particular piece of data can give us multiple answers. Often we need to explore that data in relation to other data.
 
 
Make a list of all of the data that is needed to answer the questions the project is centered on.

Step 3: What form does that data come in?

Determine what Type of data we are measuring. Create a Data Collection Form.  Is it continuous data or discrete?

 

Indicate on your data needs list the type of data each item is.

Step 4: How Much Data Do We Need?

We want to get enough data so what we can see patterns and trends. For each data element on the list write down how much is needed.

Step 5: How are we going to measure this data?

Data can be measured in different ways; check sheets, survey answers, etc. The way we measure will be dependent upon the kind of data we seek.

  • Decided on an operational definition for each measurement.
  • Identify the specification  of the measurement. (Should be based on the customer’s limits of acceptability.)
  • Define the target values (And what direction do we want the process to go in?)
  • Put a real, objective  value for each target.
 
How will we be consistent in our measurements? Is a gauge R & R required?

Step 6: To Sample or Not to Sample

Sometimes it is impractical to measure an entire population of data and instead you have to take a sample. How much do you need to sample of the parent population to make statistically-sound judgements? And how are you going to sampling the data? How will you avoid measurement bias?

Step 7: How will we display the data?

We can display data in many ways; control charts, pareto diagrams, run charts, etc.  Which graphical display tool is best suited to answer our questions?

Data Collection Plan Videos

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Comments (6)

I am looking for available data set to apply lean six sigma on that. Would you please advise where I can find data?

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