Data collection is based on crucial aspects of what to know, from whom to know and what to do with the data. Factors which ensure that data is relevant to the project includes
- Person collecting data like team member, associate, subject matter expert, etc.
- Type of Data to collect like cost, errors, ratings etc.
- Time Duration like hourly, daily, batch-wise etc.
- Data source like reports, observations, surveys etc.
- Cost of collection
Few types of data collection methods includes
- Manual data collection refers to things like simple tally sheets and tick sheets. Advantages of this type of collection are that they are very simple to execute and they use very simple tools. They can also be very helpful in determining which issues you ought to pursue inside of a process, without an exhaustive round of data collection.
- Direct observation – Direct observation is always very powerful because you get to watch the actual interaction of the customer. Direct observation allows you to gather information about what that customer experience looks like, including the negative and positive aspects of the observation and nonverbal signals. You can also identify issues that may not otherwise be obvious or even things that the customer may not even see themselves.
- Interviews – Interviews are a powerful technique and a great way to gather individual perspectives versus the perspective of a group. However, there are some limitations to interviews. Interviews are one of the most expensive ways to get an idea of what customers think. It’s costly to convert written data, then transcribe it, and then convert it to a form that you can analyze using Lean Six Sigma tools. One of the core ideas for in-person interviews is metatalk. Metatalk involves the things that are not said with words. Here you want to pay attention to the kind of rapport are you are developing, identify visual clues, and make eye contact. Phone interviews allow you to gain access to a wide variety of people and can be done at a low cost because you don’t have to travel anywhere or provide a place to meet.
- Focus groups – Focus groups are another method where you can gather feedback from a lot of customers at the same time. They’re good for dynamic brainstorming and gathering a number of ideas and inputs to the problem you’re trying to address. They are not nearly as restrictive as surveys or individual interviews, which require the use of a script, and you can use visual aids to promote the conversation.
- Surveys – Surveys are very useful, and are arguably the most inexpensive way to gather quantifiable data. You can use surveys in conjunction with other data collection methods. Surveys are very good for basic issues and are a great way to collect a lot of data in a short time.
- Check sheets – It is a structured, well-prepared form for collecting and analyzing data consisting of a list of items and some indication of how often each item occurs. There are several types of check sheets like confirmation check sheets for confirming whether all steps in a process have been completed, process check sheets to record the frequency of observations with a range of measurement, defect check sheets to record the observed frequency of defects and stratified check sheets to record observed frequency of defects by defect type and one other criterion. It is easy to use, provides a choice of observations and good for determining frequency over time. It should be used to collect observable data when the collection is managed by the same person or at the same location from a process.
- Coded data- It is used when presence of too many digits are to be recorded into small blocks or during data capturing of large sequences of digits from a single observation or rounding off errors are observed whilst recording large digit numbers. It is also used if numeric data is used to represent attribute data or data quantity is not enough for a statistical significance in the sample size. Various types of coded data collection are
- Truncation coding for storing only 3,2 or 9 for 1.0003, 1.0002, and 1.0009
- Substitution coding – It stores fractional observation, as integers like expressing the number 32 for 32-3/8 inches with 1/8 inch as base.
- Category coding – Using a code for category like “S” for scratch
- Adding/subtracting a constant or multiplying/dividing by a factor – It is usually used for encoding or decoding
- Automatic measurements – In it a computer or electronic equipment performs data gathering without human intervention like radioactive level in a nuclear reactor. The equipment observes and records data for analysis and action.
Data Accuracy and Integrity
Data integrity and accuracy have a crucial in the data collection process as they ensure the usefulness of data being collected. Data integrity determines whether the information being measured truly represents the desired attribute and data accuracy determines the degree to which individual or average measurements agree with an accepted standard or reference value.
Data integrity is doubtful if the data collected does not fulfill the purpose like data collected on finished good departure gathers data from truck departures but if the data is recorded on computing device present in the warehouse then integrity is doubtful. Similarly data accuracy is doubtful if the measurement device does not conforms to the laid down device standards.
Bad data can be avoided by following few precautions like avoiding emotional bias relative to tolerances, avoiding unnecessary rounding and screening data to detect and remove data entry errors.
Reliability and validity are essentials for quality data collection. If your data is consistent, stable, and repeatable, you know you can rely on those results. You also need to know the method that you use to collect the data, what’s actually being measured, and what’s meant to be measured. Effective data collection relies on reliability, validity and margin of error.
