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Start by accessing the SurveyMonkey website and logging into your account using your credentials. Ensure you have the necessary permissions to view and export survey data.
Once logged in, go to the "My Surveys" section. This area lists all surveys created under your account. Browse through the list and select the survey from which you want to export data.
After selecting the desired survey, click on the "Analyze Results" tab. This section displays all collected responses for the survey. It may include charts, graphs, and individual responses depending on your survey setup.
Before exporting, you might want to customize the data view to include only the questions and responses you need. Use filters and data display options provided by SurveyMonkey to tailor the dataset to your requirements.
Look for the "Export" button, typically found in the Analyze Results section. Click on it to open the export options. Choose "All Responses Data" to ensure you capture all survey responses. Select "CSV" as the export format to prepare for a local CSV file download.
After configuring the export settings, initiate the export process. Once completed, SurveyMonkey will generate a downloadable link or automatically start the download. Save the CSV file to your local machine in a designated folder for easy access.
Open the downloaded CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data to ensure completeness and accuracy. Make necessary adjustments, such as renaming columns or removing extraneous data, to prepare it for further analysis or reporting.
By following these steps, you can successfully transfer data from SurveyMonkey to a local CSV file without the need for third-party tools.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Survey Monkey uses the power of the web to enable individuals and companies to reach unprecedented numbers of respondents to gain insights into almost anything. An experience management company, Momentive Inc. (formerly SurveyMonkey Inc.) uses a cloud-based software to provide service solutions for businesses and individuals needing brand or market insights, information regarding consumers’ product experiences, employee and customer experiences—information of any kind for which surveys can provide useful information to improve products, events, experiences.
SurveyMonkey's API provides access to a wide range of data related to surveys and responses. The following are the categories of data that can be accessed through SurveyMonkey's API:
1. Survey data: This includes information about the survey itself, such as the survey title, description, and questions.
2. Response data: This includes information about the responses to the survey, such as the respondent's answers to each question.
3. User data: This includes information about the users who created the survey, such as their name, email address, and account type.
4. Team data: This includes information about the teams that the user belongs to, such as the team name and members.
5. Template data: This includes information about the survey templates available on SurveyMonkey, such as the template name and description.
6. Collector data: This includes information about the collectors used to distribute the survey, such as the collector type and status.
7. Analytic data: This includes information about the survey results, such as the response rate, completion time, and average score.
Overall, SurveyMonkey's API provides access to a comprehensive set of data related to surveys and responses, which can be used to gain insights and make data-driven decisions.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
