What’s new with Google Cloud
March 30, 2021
What’s new with Google Cloud
March 31, 2021
Organizations are realizing that experience management and analysis are important aspects of understanding needs and providing the best level of service to customers, employees, and vendors. Surveys are a powerful vehicle within the experience management space for data collection within organizations of all shapes and sizes. According to Verified Market Research, Experience Management, which includes tools like surveys, is a USD $17.5B market that is expected to grow 16.8% annually (CAGR) from 2020 to 2027 (Source).
Tools like Google Forms, Survey Monkey, Qualtrics, and TypeForm allow companies to get answers fast from groups and organizations with whom they interact. The growth in technology options and the ease and effectiveness of survey platforms means that many companies create a lot of surveys. Oftentimes, these surveys are used once to solve a specific problem, the results are analyzed and shared, and then the survey and resultant data are forgotten. A natural opportunity exists for companies to instead capture and store those results in a place where they can be used for survey-over-survey analysis and comparison against other first and third party data to better understand cause and potential options for improvement.
So, what barriers exist to creating this experience management data warehouse? Surveys by nature are flexible vehicles, and many survey systems provide data in a question-answer, column-row format, or as delimited results. This data shape, while good for human consumption, is not great for wide-scale analytics, and the process for getting it to a better format can be daunting. Over the course of this blog, we’ll demonstrate how Google Cloud and Trifacta have partnered to create a design pattern to easily shape, display, and use this data.
Focusing on scale and flexibility
Survey datasets often require pivoting or parsing of data so that questions can be compared, analyzed, and augmented in a more structured format. This survey analytics pattern walks through the process for transforming the data, putting it into a data warehouse, and using that warehouse to analyze and share findings. This pattern can be extended to a variety of survey types and different iterations of the surveys, providing you with a platform that can be used to do deeper and more consistent analysis.
To illustrate this pattern, we’ll leverage Google Forms as our capture mechanism (Note: although we’re using Google Forms for this example, the concepts are transferable to other survey engines that have similar export schemas). Google Forms allows users to structure questions in a variety of ways, from multiple checkboxes to a ranked list of items to a single, freeform answer, each producing slightly a different output. Based on the question category, the survey analytics pattern provides a specific approach to structure the data and load it in BigQuery tables.
For example, with multiple choice questions, the results may appear as a list of values with semicolon separator (e.g. “Resp 1; Resp 4; Resp 6”). Using Google Cloud Dataprep by Trifacta, a data preparation service found on the Google Platform, we can take those values and parse the extract into a cleaner format for analysis where each response is a new row.
