For these applications, data indexing that leads more directly to the categories most often used could achieve quicker response times. But for dealing with large amounts of data and complex analysis, parallel processing can be the answer. If your query requires multiple operations and you want to improve response time, you probably need parallel processing. It's not an easy path to choose. It can be costly.
Applications can require recoding.
Moving data to a parallel architecture is work, although tools exist to help. You must train people for the environment. A key issue is figuring how to spread your data properly, which requires understanding the nature of the data and how it will be used. Given the increasing reliance of many organizations on data manipulation, a boom in parallel processing for data querying probably is on the way. Applications tend to grow, and data tends to grow a lot faster than anticipated.