It would be better if variables like experience or education containing complex data were already organized into sub-variables. For example, the experience variable could be broken down into company name, company ID, job description, start year, and end year. As part of the Bright Initiative, since we’re a nonprofit, we are limited in the number of data points we can download. This can occasionally be challenging, but we manage to work around it. In the past, I encountered a few other challenges as well. Initially, I had difficulty transferring the data from Bright Data to our cloud. One past issue involved date formats. There were many different date formats for the same data points. LinkedIn adjusts the date format based on the user’s browsing location. So, if you're viewing a profile from the US, the date might appear in a month-day-year format, whereas in other regions, it could show up as day-month-year. When using automatic web crawlers to download data, the data formats can vary depending on the server's IP address running the scraper, not your location. I received the employment start date variable in about fifty different formats, which I had to standardize. I wrote some code to convert all the time formats into a consistent format.