A Data Collection Plan specifies the data to collect in order to measure how the process is performing. You develop the Data Collection Plan by identifying the data needed to:

  1. Establish a baseline measurement of the process performance, and

  2. Find possible clues to root causes that impact this performance.

Ready to use the Data Collection Plan and start improving?


Implementation of the Data Collection Plan provides you insight into problems to solve to optimize your process and provides a measure to quantify the performance impact once you implement improvements.

Your Data Collection Plan should always include gathering the project Goal Statement performance (your Y). In addition, identify measures of things that you believe impact the Goal Statement performance (the Xs).

You are looking for enough data to establish a baseline and find clues for potential root causes. A general guideline is a minimum sample of 25 to 30 data points, if feasible. Some processes may take weeks or months to produce this level of output. If possible, collect as much data as you can.

If you do find some valuable clues to root causes, your sample size may be big enough. However, if you see no clues, you may need to gather more data.

If possible, it’s faster to gather credible historical data. If you have to collect information manually or through direct observation, be highly selective. Ultimately, obtain as much information as practical while keeping your project moving forward in a timely manner.

Consider how much data you need, who will collect it, and when and where it will be collected. Then, precisely determine how you will collect specific types of data by developing operational definitions.


How do I use the Data Collection Plan?

The Data Collection Plan tool specifies the data to collect to measure how the process is performing. Track performance in sequence over a period of time. You identify the units of the time sequence to be used, such as minutes, days, dates, days of the week, or work numbers. These units of performance are the Baseline Data.

Consider what other data is available to measure process performance under different conditions. These conditions are called stratification factors and represent additional information that is gathered along with the performance. They are typically easy to identify “tags” that you can log as the numerical Goal Statement performance is collected—think who, where, when, and what kind. Examples include the day of the week, shift, location, type of order, etc.—anything that makes sense. The baseline performance over time and the stratification factors make up your Data Collection Plan.

While we do not wish to “blame” people, using people or teams as stratification factors is acceptable, especially if different participants are doing the work. If we see a difference in performance between people or teams, we avoid blaming and try to understand the reason for the difference, such as method, knowledge or experience, etc.

You may gather any other information that you feel may shed light on the process or its problems. Remember to include stratification factors where possible. Consider the stratification factors to be just as crucial as the Goal Statement performance—they can be an essential source of clues to your Root Causes—and might be the key to discovering what you might otherwise miss!

Data Entry

Once your plan is complete, Kure will create a form in the Data Entry tool. After data collecting and inputting your data into the Data Entry tool, Kure will plot the performance in the Baseline Tool. Here you can observe the performance and look for non-random patterns that may be evident, such as:

  • Extreme high and low values

  • Clustering of data

  • Trends

  • Cycles

  • Any other clearly non-random pattern

In addition to non-random patterns, you may find clues within the stratification factors. For example, significant performance differences due to day of the week, location, shift, type of order, etc., provide insight into potential root causes.

Whether you recognize any clues or not, Kure will analyze your baseline performance looking for clues and identifying them when present.


Kure guides you through each step in creating your Data Collection Plan by asking simple questions and providing guidance along the way. Powered by our Process Optimization Path™ (artificial intelligence), Kure will help you and your teams collaborate to complete process improvement projects together.

Ready to use the Data Collection Plan and start improving?

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