In the Measure phase of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) improvement methodology, data is collected and analyzed to provide a performance baseline for the process under study. Asking questions is one of the primary ways of collecting data. But as author Edward Hodnett noted, “If you don’t ask the right questions, you don’t get the right answers. A question asked in the right way often points to its own answer. Asking questions is the A-B-C of diagnosis. Only the inquiring mind solves problems.” It seems simple enough, but it’s not always as easy as it seems. There is an art to asking good questions.

This point was beautifully illustrated during the opening remarks at a telecom conference last year. The speaker began by telling this story:

Early one morning, an IT technician manning the help desk received a frantic call. An executive assistant explained she had accidentally spilled a cup of coffee on the keyboard of her boss’s new computer. The young woman was quite distressed and asked if she had ruined the keyboard. The technician considered this for a moment. Then, deciding that a $38 keyboard could be replaced relatively easily and inexpensively if necessary, he instructed the young woman to unplug the keyboard and rinse it with clear water. The technician told her to set the keyboard aside to dry thoroughly before plugging it in once more. Greatly relieved, the woman thanked the technician and hung up. Within the hour, the help desk received a follow-up call from an irate executive demanding to know what “%#*$” instructed his executive assistant to put his brand new $3,000 laptop computer in the sink and douse it with water.

The story was met with the expected laughter. But it really makes the point that one must ask the right questions in order to get the information needed to take appropriate action.

In some cases, data collection is a fairly sophisticated and highly mechanized process. Frequently, companies and organizations have loads of historical data that if sorted, shifted and extracted properly will provide the team with the information necessary; no additional data collection effort is required. However, Six Sigma improvement teams sometimes find the data they need does not exist, or is simply too difficult or expensive to extract. They are faced with the need to collect data manually. How can they ensure that they are going to collect useful information?

Data Collecting Tips for Six Sigma Teams

Here are some tips that may be helpful for gathering data.

• Be sure the people involved in gathering or providing the data know how it will be used. This will help to eliminate misunderstandings and potential fear about how the information is going to be used that could possibly bias the results.
• Utilize an objective party to help collect the data whenever possible to help eliminate bias.
• Be sure “good” questions are specific to the problem under study. The better the question, the more useful the data.
• If you know that factors such as shift, location, person doing the work, machine, suppliers, etc., are important to the problem under study, make sure your data collection plan and form considers these stratification factors. The last thing a team wants to do is have to repeat a data collection effort because they failed to think about appropriate stratification factors up front. This adds time to the project and folks willing to provide data initially may not be so amenable to participating in the data collection effort on the second time around.
• Use the KISS principle (keep it simple, stupid) when designing your collection form. The more complex you make your collection tool, the more opportunity you create for problems.
• Design your collection forms so that data is collected in columns versus rows. Software spreadsheets are arranged in columns. By collecting data in columns, you can help to prevent transposition hassles later on.
• Train your data collectors. This can be a simple as providing guidelines on the data collection form itself to hands-on instruction. The level of training really depends upon the complexity of the collection effort and the expertise of the data collectors themselves.
• Make sure there are add spaces on your data collection form for the data collector’s name, and for the date and time the data was collected. This seems to be a no-brainer; however, sometimes the simplest things are those that are inadvertently overlooked. If you have questions later, or if some of the data collected is not what you expected, this information can be invaluable to the team’s follow-up efforts.
• Try it out. Your team may want to do a trial run of the data collection effort to determine if they are collecting the type of data they expected. The results of the trial may surprise the team. Frequently, teams realize that questions need to be further refined and focused in order to provide the data the team is looking for. Pay special attention to “missing” information, anything that looks out of the ordinary, or to questions that the team receives from the data collectors. Any of these may provide an opportunity to improve the data collection process. Forms and data collection instructions should be modified as appropriate based on the trial run.

Just some good, basic things to think about when collecting data, but the most important thing to remember, as illustrated in the story about the IT technician, is to learn to ask the right questions. Asking the right questions will provide useful information. Asking good questions is the key to solving problems.