By Roger Kuykendall
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In the past, analysts who needed quick response time placed data extracts in local, single-user applications that were fully dedicated to a single user/analyst. Today’s challenge is for systems to provide blazing response time to access and computation requests while working with large data sets in a multiuser environment distributed across a network. Some tools provide for this by precomputing all aggregates. This can, 23 24 Chapter 1 however, lead to database explosion (as described in Chapter 10).
Coverage Timeliness Accuracy Understandability 19 20 Chapter 1 The Goal-Challenge Matrix Most OLAP challenges affect multiple goals. For example, in addition to speed, which is obvious, large amounts of data can affect accuracy and understandability as well as coverage or existence. On the input side, the presence of lots of source data means there will be an increased likelihood that some data is anomalous. And calculating with large amounts of data has its own complications that may introduce errors.
This is especially important in today’s world. In earlier times, analysis tended to be performed by small groups of individuals within the organization who devoted most of their time to analysis. These people could afford the steep learning curves associated with DSS-style analytical systems. Today, however, analysis is increasingly performed by a wider scope of people, each of whom devotes only a small percentage of her or his time to analysis. They need an analytical environment that enables them to maximally leverage what they already know, so they can be up and running quickly.