Strategies for Handling Large Datasets and Improving User Experience in Workplace Incident Reporting System
In the meeting, the State Changers discussed complex issues relating to setting up dropdown lists, data cleaning on the backend, and dealing with potentially large amounts of data.
A participant asked what to do when a dropdown list, which was initially populated with around 3000 items, loaded slowly. The State Changers suggested replacing the dropdown list with a search box in which an employer can be typed into, with a call to the backend to search for it. This technique will work well with very large lists.
The discussion advanced towards capturing data about multiple subsidiary companies owned by a particular employer. The participant expressed concern about maintaining data cleanliness. Data cleanliness is vital, especially when the data is user-provided. The State Changers proposed that the initial data should be filled manually, ensuring privacy and cleanliness. They suggested that backend logic could be used to recognize and match the data, creating opportunities for automation in the future.
Privacy concerns were also raised, considering the sensitive nature of the data, such as employee complaints. The State Changers thought it would be insightful to hide employer data until there is enough data to ensure anonymity.
A potential issue of data duplication was discussed, with instances like wall-walmart versus Walmart. The participant was advised to manually deduplicate the data early in the process and to keep rules of data cleaning and validation to avoid capturing garbage data.
The possibility of a future issue regarding data protection and GDPR compliance was just briefly touched upon, indicating a potential challenge for future international expansion.
Overall, the conversation provides insight into issues surrounding dynamic data capturing, cleanliness, privacy, and manual deduplication. This meeting would be beneficial for individuals seeking to understand more in-depth aspects of dealing with user-provided data in a responsible and efficient manner.