Data Collection Plan For Six Sigma: How to Create One?

The term ‘data’ became popular in the age of computers and was used to refer to computer information that was either stored or transmitted. However, that is not the complete definition. Data is information in the form of texts, numbers kept on paper or in bits and bytes in the memory of electronic devices or even stored in a human mind.

What is Data? 

Businesses today are helpless without data. The gathered data helps them to understand their customers and business as well.  A Deloitte survey reveals the following:

• 49% of the respondents said data analytics helps them make better business decisions.
• 16% mention it helps them understand the key strategic initiatives.
• 10% think that it helps them improve relationships with customers and business partners as well.

 What is a Data Collection Plan?

 A Data collection plan is a detailed document that describes the exact steps and sequence that must be followed in gathering data for a project. Data collection plan Six Sigma is a statistical approach aiming at attaining breakthrough improvement by reducing variation and defects. Six Sigma signifies 3.4 defects in a million opportunities. In other words, the higher the sigma value, the lower the defect rate is.

 The most common Six Sigma data collection framework is the DMAIC approach, also called the DMAIC data collection plan, where DMAIC signifies D-Define, M-Measure; A-Analyze, I-Improve; C-Control. The Six Sigma Foundations course describes in detail about the DMAIC and its approach.

The Importance of Data Collection Plan

The detailed data collection document is important because people who designed the data collection plan may not be the same who are involved in collecting the data. Such a document ensures:

• Everyone involved in the Six Sigma project team is on the same page regarding the data plan.
• The information is corrected and transmitted to the people in the company who will provide the needed data.

Why Do You Need Data Collection Plan?

 As discussed, gathering all kinds of data and looking at them is not likely to bring great results unless one can derive meaningful insight from them. Besides, a proper data collection plan helps save resources as gathering data takes a lot of time and may be expensive toesides, it may not be possible to get all the data, and whenever required. By creating a data collection plan, a business can focus its effort on answering specific questions that have business value.

When and How to Use a Data Collection Plan?

A proper data collection plan ensures that the collected data is appropriately sorted and useful. The plan is used to analyze the current state of a process or to improve a project or process. It is typically completed before collecting and analyzing process performance data. Additionally, it is useful at the completion stage of a project while establishing new metrics and the procedures needed for evaluating those metrics.

Proper data collection involves a systematic approach to:

1. Identify the data that needs to be collected.
2. How the data will be collected.
3. Collection of the data.
4. Revision whenever needed.

The below-mentioned simple data collection plan example will illustrate a common approach to data collection and also includes how the data collection plan is incorporated into the process.

5 Methods of   Data Collection Plan with Examples 

Data gathering can be done using different methods that include straightforward, conventional and more advanced data collection and analysis techniques. The specific method to be used will depend on the type of business; hence, all types of data collection methods may not be suitable for all types of projects. Some Data collection plan methods are described in brief: 

1. Questionnaires and surveys

This method obtains data from targeted respondents and aims to generalize the results to a broader audience. Business and academic sectors use this method. The advantages are: 

• Surveys can be done online, with ease, and can be accessed anytime and from anywhere. 
• Less expensive method with the flexibility of analyzing data which is much easier than other methods. 

Below is an example: 

2. Interviews

The method is used for collecting personal and other information and acquiring insight into personal skills and is done by way of a formal meeting between two individuals. The advantages are: 

• Can reveal more data about the subject and help explain, understand, and explore perspectives, behavior and experiences of the person interviewed. 
• Offers an open-ended conversation and is more accurate as such data cannot be denied or falsified later. 

An example is mentioned below: 

3. Observations

Researchers use them especially to study individual behaviors or surroundings they are trying to analyze. It includes seeing or observing people in a certain place, time and day. The advantages are: 

• Ensures ease of data gathering and the detailed collection as well, without the need for any technical skills of data gathering and can be as detail-oriented as desired. 
• Independent of proactive participation of the participant as it does not need people to actively share data about themselves that they may not be comfortable disclosing. 

4. Documents and records

The method involves analyzing a business’s existing records or documents to track changes happening over a specific period. The data or records include email logs, staff reports, call logs, databases, information logs and minutes of meetings. The advantages are: 

• The data is available almost readily without going for active research or interviews. 
• Rechecking or tracking the collected data is easy. 

Some examples are: 

5. Focus Groups

In this method, six to twelve people with comparable qualities or shared interests are interviewed by a moderator who leads the group through a series of planned topics. The moderator is also responsible for creating a proper atmosphere for the participants to express themselves freely. However, this is a qualitative data collection method and cannot be quantified statistically. 

How to Create Six Sigma Data Collection Plan? [In 8  Steps] 

In Six Sigma, a data collection plan is made in the DMAIC framework, and a project manager who has done a  Six Sigma Green Belt Certification course will be able to handle it well. The data collection plan steps are mentioned explaining how to create a data collection plan. The process of the data gathering plan involves 8 steps. 

Step 1: Identification of the questions

The data to be collected needs to be relevant to the project. Answering these questions will help: 

• What is the project hypothesis? 
• What are the questions that we need to get answers to? 

The answers to these questions should be centered around the actual reality of the process since the main objective of DMAIC is to improve a process. The best approach would be to use SIPOC (Supplier, Input, Process, Output, and customer) as a guide to data collection. 

The data collection should meet certain criteria. Here is a checklist of them: 

1. Is it feasible to collect the data on time, within budget and with optimum effort? 
2. Does the data consider related and influencing conditions? 
3. Does the data provide good insight into the process? 
4. Can it be framed on the Input-Process-Output diagram of the SIPOC? 

Step 2: Data identification and listing

Next is to break the above questions into parts and understand what kind of data exists that can give answers to these questions (part or whole). Often a particular piece of data can give multiple answers, and also, we may need to explore a piece of data about other data pieces. 

Then a list needs to be made of all the available data needed to answer the questions. 

Step 3: Data Type

Earlier, we discussed briefly different data types. Here we need to understand the ‘data type’ that will be measured. For example, in continuous data, it is better to use a histogram or a run chart, while for Attributes, a Pareto chart or pie chart will help. 

A ‘data collection form’ (a way of recording an approach to obtaining the data that need to perform the analysis) will be required at this stage.  An example of a data collection form is given here: 

Step 4: Data volume

In order to see patterns and trends enough data will be required. Therefore, for each data element on the list how much data will be required needs to be written down. 

Step 5: Measuring method

Various methods are there to measure data, like survey answers, check sheets and more. This will be discussed in detail in the subsequent section. The method used will depend on the kind of data that is needed. The checklist to follow could be: 

1. Deciding on an operational definition for each measurement 
2. Identifying the specification of the measurement and this should be based on a customer’s approved limits of acceptability. 
3. Defining the target values plus which direction the process needs to go. 
4. Allocating a real, objective value for each target. 

Step 6: Sampling decisions

Measuring an entire population of data when it is not needed would be an impractical step when the needed data can be obtained well from a sample. However, here again, the answers to some questions need to be figured out. These are: 

1. How much sample from the parent population would be needed to make statistically sound decisions or judgements? 
2. How does the sampling of the data happen? 
3. How can measurement bias be avoided? 

Step 7: Data display

Data can be displayed in various ways like Pareto diagrams, run charts, control charts and others. It needs to be decided which type of display tool will be the best suited. 

Step 8: Data gathering responsibility

Whether data needs to be collected physically via actually liaising with the person(s) in charge or be collected through automated software needs to be ascertained. In the case of automated software, the person in charge needs to be identified who will be ensuring the data availability on the stipulated time and in the correct format. 

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Data Collection Plan Template

Giving a focused approach to data collection is the main aim of the data collection plan. It specifies the objective of data collection, what kind of data would be needed, how the data would be collected and responsible individual to oversee the collection and present it in the required format. Besides, it helps to understand whether a process is capable and stable enough, as also if the measurement system is accurate and efficient. 

A data collection plan worksheet can be made in different variations using Microsoft Excel or Google worksheets. Two types of templates are shown below. One captures the basic data and the other is more detailed that allows recording detailed information and can be used by Six Sigma practitioners as well. However, for both, first specifying the objectives of the data collection and identifying the main study variables will be needed. 

1. Data collection plan template Excel (Compact)

Find the data collection plan template

2. Data collection plan template Excel (Comprehensive)

Find the comprehensive data collection plan template 

Importance of Data

To enumerate the importance of data, let us understand the five ways data can help companies of all sizes, big or tiny, especially SMBs. 

1. Data helps in making better decisions

Any business with a website, presence in social media, and accepting electronic payments need to collect data about customers, their habits, demographics, psychographics and others; web traffic and bounce rate (the list is almost endless) can derive huge benefit from such potential data. The benefits could include the following: 

• Finding new clients 
• Increasing and improving customer service and customer retention 
• Make better marketing efforts 
• Making the best use of social media interaction, comments, likes, shares 
• Predicting future business or sale trends 

 2. Data helps in problem-solving

Tracking, gathering and analyzing data from business processes, for example, noticing slow sales or unsatisfactory performance of a marketing campaign, help to understand what went wrong and how to fix them. 

 3. Data helps in understanding performance

The Sports sector is a great example, where collecting performance data and analyzing help to make sports teams perform better. Similarly, different teams in different departments of a company, marketing efforts, customer service, dispatch and shipping or other aspects, a business can learn and do a lot to improve its performance by collecting and reviewing all data. Also, a department head or a team manager must know how the team members perform. 

 4. Data helps in process improvement

Only data helps to improve business processes to reduce any waste, which every company must know and prevent, like depleting resources and wasting time and energy that ultimately affect the bottom line. For example, an inappropriate or thoughtless advertisement decision can be a massive waste of resources for a company. But by analyzing competitor advertisements and how their different marketing channels are performing, the same company can gather which types of marketing effort will gather the highest ROI and thus focus on it. 

 5. Data helps in better customer understanding

Only through data can a business learn more about its customers and what kind of product or service they are looking for. Only then will a company be able to strategize its product or service offer and make effective sales and marketing efforts. 

However, too much data could be difficult to deal with. To take full advantage of data and analytics, businesses must know how to derive the most value from the existing data. As the data grows, a business often looks for Business Intelligence solutions to better understand and interpret consumer data and leverage it for higher profits and better business. 

Types of Data

We are creating data every moment, with or without realizing it. For example, just text a message, posting a photo on social media, or even browsing websites (the sites use cookies to gather visitors’ browsing history). In 2020, 2.5 quintillions of data were generated every second! 

From business to project, data is the backbone of everything. Overall, various data types can be classified: 

• structured and unstructured 
• qualitative and quantitative data 
• discrete and continuous data.  

Businesses and projects use various types of data depending on the situation and requirement. 

The much-used term Big Data is used to describe the range of data that is in the petabyte range or even higher and is also described in terms of 5Vs- Variety, Volume, Value, Veracity, and Velocity. Businesses like web-based eCommerce like Amazon have thrived and flourished using this type of Big Data, which is the pillar and the asset of such businesses. The benefits of Big Data include increased sales, improved customer service and enhanced efficiency, among others.

Conclusion

 We have dealt in depth with the importance of data, types of data, Six Sigma data collection plan and its importance, steps to create a data collection plan with methods and examples and templates. However, it is important to understand that a data collection plan in Six Sigma needs special expertise which can only be learnt by attending some professional Six Sigma courses. KnowledgeHut’s best Six Sigma courses could be the best option for anyone interested in practicing or participating in any Six Sigma project.