Which technique can reduce save/update time related to lookup skew?

Using asynchronous processing to move unnecessary code can significantly reduce save or update time related to lookup skew. Lookup skew occurs when there is a performance bottleneck during the referencing of related records, especially when the number of records associated with a lookup field grows. By offloading tasks that do not need to run immediately—such as complex data processing, validation, or integration logic—to asynchronous processing, you free up resources for immediate operations, allowing for quicker transaction times.

Asynchronous processing allows these tasks to be queued and executed separately, creating a cleaner workflow that minimizes the time spent on lookups during critical save or update operations. As a result, when actual save or update transactions occur, they can be completed more quickly as they are not held up by other resource-intensive processes running in the same context.

In contrast, implementing more synchronous processes would likely exacerbate issues with lookup skew by adding to the load during critical operations. Scheduling updates to run together may help in managing tasks but doesn't inherently simplify the interactions with lookups. Using only picklist fields does not address the underlying issue of lookup skew directly; it simply changes the type of field being used without addressing performance impacts.