However, a recent Gartner report shows that the benefits are broader than what a single unified database can provide. Traditional application architectures separate transactional and analytical systems. Digital business and the need to respond to business moments means that using "after the fact" analysis is no longer applicable. Commercial moments are fleeting opportunities that must be exploited in real time. If one organization fails to recognize and/or respond quickly to business moments by making quick and informed decisions, some other organization will do so, resulting in missed opportunities (or new business threats). HTAP allows advanced analytics to be run in real-time on "dynamic" transactional data, providing a framework that enables users to respond more effectively to business moments. [10]

The main technical challenges of HTAP databases are how to efficiently handle operational (many small transactions with a large number of updates) and analytical workload (large and complex queries traversing a large number of rows) on the same database system and how to prevent the impact of analytical queries on the operational workload interference. This operational workload is also often referred to as operational analytical processing.

HTAP addresses analytics latency in several ways, including eliminating the need for multiple copies of the same data and the requirement to offload data from operational databases to data warehouses through ETL processes. [7][9]

Most applications of HTAP are powered by in-memory technologies that can handle high volume transactions and provide features such as forecasting and simulation.