How does one review and analyze the underlying data set for an autosuggest in order to know what mitigating choices to take with the autosuggest UX? I am concerned there may be problems in a data set eroding the user experience.
In this case, it's a tagging solution using a list of company names. Users will be able to create a new tag if there is no match, so if the autosuggest is not effective we may have both an abundance of redundant tags where the user assumed the company was not already in the system, and untagged entries where the user became frustrated and chose not to tag.
For years there has been a informal set of data in the voice of the users - "Disney" vs. "Walt Disney Company, The" but we're using data from another source - we'll use "starts with" on each word of the company name to mitigate this specific issue, but are there any tips on reviewing the data to determine other choices we can make before going live?
I've read with great interest many of the Q&As around Autocomplete and Autosuggest, and I'm grateful for the UX tips and perspectives I have already found here.