Fuzzy Matching To The Rescue

Data collection methods in the real world are rarely a static process. Over time, the information collected by companies, researchers and data scientists can change in order to gain more insights or improve the quality of the information. Changing the data collection process presents a challenge for longitudinal data that requires aligning the new data with existing methods.

In the case of surveys, the introduction of new or modified questions and response choices is often problematic for aligning data collected over multiple periods. When changes are implemented, newly collected data must be compared against the existing data fields to match the correct fields of the survey and maintaining the data becomes challenging as the number of questions, responses, and respondents grow over time. 

Machine learning techniques are a valuable tool for tackling this challenging problem. In this session, learn how well fuzzy matching algorithms handles real world data.