Why is Survey Data Processing Important?

Designing and implementing the perfect customer survey can take time and resources. 

So, it’s crucial to get a high Return On Investment in the form of insights that act as the upthrust in getting you a step closer to your business objective. And insights come after you analyze your data. But, how can you ensure the analysis is smooth and productive? 

But first and foremost is – “Survey data processing,” an unskippable element before you start analyzing. 

We will walk you through the what, why, and how of survey data processing. Let’s start! 

What is Survey Data Processing?

Survey data processing is converting raw data into structured information that can be analyzed for extracting insights. 

Precisely, responses from surveys come in different formats with inconsistent replies, missing values, and a lot more. And by processing the survey data, you are fixing those challenges and formatting the information to ensure it’s consistent. 

But, it’s important to note the difference between data cleaning and survey data processing.   

Data cleansing is an element of the data processing phase, whereas survey data processing encompasses more than just cleaning. Besides, data processing is also related to converting your raw data into a usable format. 

So, when you process survey data rightly, you will save time exponentially. Let’s dig a little deeper into why that is now! 

Why is Survey Data Processing Crucial?

When done before analysis, Survey Data Processing is important for extracting consumer insights to make changes and improvements in your organization. 

This is because the analysis is most effective when organized, clean, and well-structured data. In contrast, raw data is the opposite of all this and consists of bad or dirty data. The garbage in, garbage out analogy is a straightforward approach to see if you used not-so-good quality data; despite using machine learning tools or analyzing survey data manually, bad quality data make their way through. 

Insights from survey data are beneficial for making essential decisions. Moreover, the best cost-effective decision is to prevent insufficient data from entering your analysis process. 

How To Use Survey Data?

The below-mentioned 4 steps will help you survey data effectively and efficiently. 

# Step 1: Quality Assurance 

Quality assurance should be an unskippable part of your survey process from the initial stage, especially during survey data processing. 

This step is here to check the first quality of your survey results. What you find in this step will inform any corrections or changes you need to make later. Simultaneously, it’s likely to report how you gathered data in the beginning, to keep bad quality results to a minimum. 

But, you need to be systematic while performing your quality assurance to eclipse everything. This includes the following steps: 

• Ensure all respondents have answered the questions correctly. 
• Review for incorrect information and trust
• Identify mismatched data kinds.
• Check for missing information or fields.
• Discover data outliers
• You can also check the sample deviation index measuring how much the sample diverges from the target population based on your data. 

#Step 2: Data Cleaning 

Data cleaning is the step where changing the data for analysis gets started. It will occupy a significant amount of time when processing survey data. But, on the contrary, it’ll ensure your research runs much smoother. 

So, when cleaning your survey data, you should pay attention to these 5 points: 

• Time Put In Answering The Survey

This area is crucial because respondents move too quickly through the survey indicating that they are not engaged. Besides, it also shows that they are not reading your questions attentively.  

Most of the time, survey tools provide an average time respondents spent completing your survey. But, you should review all responses against this time, and if any seem suspicious, take a closer look and remove them. 

2. Duplicates

Duplicates occur because of numerous reasons. For example, it might be that respondents pressed submit one too many times, or the page didn’t load correctly. 

Duplicates can easily be discovered when you filter your results. Besides, it’s essential to remove them, or else you’ll risk inaccurately skewing your results or probably adding noise that obscures insights. 

3. Data Outliers

Outliers are defined as survey results that don’t comply with the rest of the results. So, for example, if 4 of your survey respondents are male but one is female, you did not need to consider females’ answers. 

Data outliers are detected by plotting your responses on a scatter plot. This will present to you the reactions falling apart from the majority. Concisely, the regression line on a scatter plot offers the connection between scattered data points in a data set. The outlier can be spotted because it’s farther away from the regression line. 

4. Gibberish Data 

This might sound illogical. 

Gibberish data is the result of respondents’ errors. It can also happen when the respondents try to fill the fields by scrolling through the survey as quickly as possible. But, you need to access these on a case-by-case basis. 

Then, to eliminate nonsense data, you can tag all of the answers that make sense and filter out the gibberish responses without tags. 

5. The Missed Data 

The missed data is a common challenge when analyzing survey data. 

This usually happens if they want to scroll quickly through the survey. It also indicates that sometimes, surveys can be too long, the questions are challenging to answer, or respondents are not equipped to answer the questions. 

Note: All responses with missing data should not be removed altogether. 

Identifying and managing missing fields is often a question of filtering your results, then analyzing them in every case to determine whether you can use the results.

6. Varied Responses 

Inconsistent responses come when respondents give contradictory answers. These answers need to be analyzed precisely as they may not be required and need to be deleted.

# Step 3: Data Transformation 

This is where all the required changes to the data are done to make it ready for analysis. 

You can use the following techniques to achieve this: 

• Aggregation 

Aggregation is collecting and combining all the data in a uniform format. 

• Normalization

This technique scales your data in a range and leads to standardized data with no duplicate entries. 

• Feature Selection

Here you decide which variables are crucial for your analysis and which you will use to train your machine learning models. 

• Concept Hierarchy Generation 

It includes adding a hierarchy to your results. An instance can be if you had respondents living in Belgium, Italy, or Ireland, you can add the hierarchy of Europe to the results. 

• Discretization  

This classifies data into different groups and intervals. 

For instance, if you have different ages, from millennials to Gen Z, you could quickly sort them into groups of 7-25 years to 25-55 years old. to 

#Step 4: Data Reduction 

The more the data, the more difficult it’s to process it, especially if you have hundreds and thousands of survey results. 

You can reduce your survey data by adhering to the final goals when you create your survey. Then, if some fields are not necessary to meet those goals, you might be able to eliminate them. 

An unfair advantage in reducing your data is that you also reduce the needed storage capacity and costs. In some scenarios, you won’t be able to reduce the amount of data, which is also acceptable. 

Finally

Survey data analysis helps you in improving your offerings and measuring results to stay at top of the competition. Furthermore, by using AI to conduct your surveys, you can easily simplify the process by sending and analyzing data at scale.

But, to extract the value of survey data analysis in the easiest and quickest way , you need to run different kinds of text analysis techniques like sentiment  analysis and keyword extraction.