How to Connect SurveyMonkey to BigQuery? | 2 Easy Steps

Individual perspectives and experiences can be assessed through surveys. When you conduct surveys, it can provide you with statistics on people’s behaviors and actions which can be utilized to make crucial choices.

SurveyMonkey’s analysis capabilities give adequate analytical data to understand better and make valuable decisions based on your survey replies. However, if you want to go beyond SurveyMonkey’s analysis and unearth further insights, connecting your data from SurveyMonkey to BigQuery is the way to go.

With your SurveyMonkey data optimized with a high-performance Cloud Warehouse like BigQuery, you can write everything from complex ad-hoc queries to basic reports. By combining SurveyMonkey data with information from other sources, you can expand your analytics to aid in the growth of your organization through smarter management choices. 

This article will walk you through the steps to connect SurveyMonkey to BigQuery smoothly. It also provides a quick overview of SurveyMonkey and BigQuery before delving into the steps needed for connecting SurveyMonkey to BigQuery. Read along to understand the step-by-step procedure to connect SurveyMonkey to BigQuery.

Prerequisites

To get the most out of this article, we recommend that you understand the fundamentals of data integration.

What is SurveyMonkey?

SurveyMonkey is an online survey site that allows users to create and publish surveys. It is typically used for market analysis but may also be used for surveys in various fields, including biomedical research, marketing research, customer behavior research, etc. Surveys are essential for gathering customer feedback, views, criticism, and ideas. Collecting feedback has become much easier with the emergence of internet surveys, as you eliminate the manual process of conducting surveys. 

The Basic plan of SurveyMonkey is free, although it has restricted features. It comes with 15 different question types and over 20 different survey templates. You can make as many surveys as possible, but each can have 10 questions and 100 respondents.

However, there are three premium subscription options: SELECT, GOLD, and PLATINUM. The GOLD package is the most popular, with endless questions, unlimited replies, custom survey design, skip-logic, and other sophisticated capabilities such as text analysis for open responses and interaction with SPSS.

On the other hand, the PLATINUM plan gives you ‘full brand control,’ with different survey URLs, complete control over the survey’s appearance, and the ability to add a logo and trademark colors. Prices vary by nation, but in Canada, the price ranges from $228 to $828 annually.

Features of SurveyMonkey

• Question Bank: Hundreds of questions written by survey scientists are available in the question bank. You can access the question bank from the sidebar when designing a survey. You may also navigate various categories to find the right question.
• Survey Logic: The several types of survey logic available in SurveyMonkey are:
  • Question Logic: This allows you to direct respondents to a specific page or a question on a different page, depending on the feedback users provide to a closed-ended question.
  • Advanced Branching: This allows you to skip questions or pages based on a single response and a range of “conditions.” The replies, the survey takers’ custom data, and the custom variables are all examples of these circumstances. If you utilize respondents’ email addresses as a custom data field, you may create a condition that prevents respondents with a specific address from viewing a particular page.
  • Question and Answer Piping: This allows you to customize your survey by incorporating responses to previous questions into future question suggestions. This makes respondents feel acknowledged and transforms the survey into a more intimate conversation. Furthermore, contextualizing your survey will allow you to collect more concentrated replies.
• Recurring Surveys: Recurring surveys allow you to distribute the same survey regularly for a certain length of time. You may gain contextual and timely insights into your customers, workers, prospects, and other audience experiences. After that, you can put your best foot forward and take the necessary actions to boost engagement. Recurring surveys are referred to as “longitudinal studies” since they are conducted over a period of time.

What is BigQuery?

BigQuery is a fully managed corporate Data Warehouse with built-in technologies like Machine Learning, geospatial analysis, and business intelligence to help you manage and analyze your data. The serverless design of BigQuery allows you to utilize SQL queries to solve your organization’s most pressing problems while requiring no infrastructure administration. 

The scalable, distributed analytical engine in BigQuery allows you to query terabytes and hundreds of petabytes of data in minutes. BigQuery is an “externalized version” of Google’s home-brewed Dremel query service software, published as V2 in 2011.

BigQuery increases flexibility by separating the computational engine that analyses your data from your storage options. You may use BigQuery to store and analyze your data, or you can use it to review your data wherever it is stored.

BigQuery Working Process

Before you start working with BigQuery, you need to create a table structure. You can create 3 types of table structures — native tables, external tables, and views. After creating a structure, you can start listing your data tables and control the access of data:

• Loading Data (Ingestion): After creating tables, you have to insert your data, and there are many ways to upload your data into BigQuery. You can load data in batch, execute queries, stream individual information, and third-party tools.
• Storage and Preparation: This step involves preparing data stored in BigQuery for data analysis. Here, the raw data you obtained from the previous stage is processed into the required format.
• Analysis and Exporting Data: BigQuery data may be exported in various ways, with each file containing a maximum of 1GB. You can export the data or use a service like Dataflow to streamline the operation. After the data has been exported, it may be analyzed using services like BigQuery ML and Google Data Studio.

Features of BigQuery

BigQuery is a serverless Data Warehouse built for companies with large amounts of data. It offers a wide range of functions in a cost-effective pricing model, from acquiring comprehensive insights from data with built-in Machine Learning to analyzing petabytes of data using ANSI SQL. There are many features given below:

• Multi-Cloud Option: BigQuery’s USP is that it offers an innovative way of analyzing data from several clouds without incurring additional costs. In contrast to past methods, which usually had substantial egress fees for moving data from the source, BigQuery eliminates the need for moving the data. This implies BigQuery can do computations on data right at the storage location.
• Free Access: BigQuery’s Sandbox allows you to try out BigQuery and the Cloud Console without committing to anything.

How to Connect SurveyMonkey to BigQuery?

In this section, you will learn how to migrate SurveyMonkey to BigQuery using the two steps listed below:

• Step 1: Export Data from SurveyMonkey to Excel
• Step 2: Import Data to BigQuery

Step 1: Export Data from SurveyMonkey to Excel

• To export your survey results into Excel or CSV format, you must log in to your SurveyMonkey account. 
• Now, go to analyze the results tab.

• Click the “Save As” option at the top of the page to save your analyzed results. 
• Then, click on the “Export” option, where it will ask you to export different types of data: All Summary Data, Responses Data, and Individual Data.  
• You must select one option from All Summary Data, Responses Data, and Individual Data. Then, select a file format from different options like PDF, PPT, XLS, or CSV. After this, you can choose between landscape or portrait orientation. Eventually, you can rename the default file name to your desired file name. 

• Click on the “Export” option to download your results. The file downloaded is in zip format, so you have to unzip and export the file to the destination folder.

Step 2: Import Data to BigQuery

• You may create a table from scratch by visiting the Web console > Create Table > Create a Table From.
• You can specify your CSV file, which will be the source for the new table.

• You may choose from various sources, such as Cloud storage, using the “Source” selection.
• In that file format, you can select CSV or Excel.
• You may set the schema by uploading a sample JSON or letting the schema specification “auto-detect.”
• After this, you can click the create table option, which pulls your CSV or Excel file data.

Limitations of Connecting SurveyMonkey to BigQuery

There are several limitations while connecting SurveyMonkey to BigQuery. Usually, you would be required to load the files individually, which takes a lot of effort and is time-consuming.

Besides manual integration, you can also use APIs to fetch data from SurveyMonkey and write custom code to feed it into BigQuery.  However, this is tedious and would require in-depth expertise.

Conclusion

This blog gives you an overview of SurveyMonkey and BigQuery along with their features and limitations before delving into the steps for connecting SurveyMonkey to BigQuery. Though, the features of SurveyMonkey allow you to gather people’s perceptions in the form of surveys and integrate that data into BigQuery, companies significantly use this data to make valuable decisions. The only limitation of this approach is the manual data integration which is time-consuming and requires a lot of effort.

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You may share your experience migrating data from SurveyMonkey to BigQuery in the comments below.