There are three categories of data latency when it comes to BI:
Real-time data is precisely what it sounds like - information that is placed in the database as it occurs, with little or no latency. Monitoring stock price information through a real-time feed from Wall Street is an example of real-time data. Real-time data is updated as it enters the database, and that information is available immediately to anyone, or any application, requiring it for processing.
While zero latency real-time is clearly the goal, achieving it represents a huge challenge. In order to achieve something near zero latency, BI implementation requires constant returns to the database, application, or other resource to retrieve new and/or updated information. In the context of real-time updates, database performance must also be considered; simultaneous to one process updating the database as quickly as possible, another process must be extracting the updated information.
Near-time data refers to information that is updated at set intervals rather than instantaneously. Stock quotes posted on the Web are a good example of near-time data. They are typically delayed twenty minutes or more, since the Web sites distributing the quotes are generally unable to process real-time data. Near-time data can be thought of as "good-enough" latency data. In other words, data only as timely as needed.
Although near-time data is not updated constantly, providing it still presents many of the same challenges as real-time data, including overcoming performance and management issues.
Some-time data is typically updated only once. Customer addresses or account numbers are examples of no-time information. Within the context of a BI computing architecture, the intervals of data copy, or data movement, do not require the kind of aggressiveness needed to accomplish real-time or near-time data exchange.