Building an Effective Data Warehouse

The access component of a DW project is referred as the front end. It consists of access tools and techniques that provide a business user with direct, interactive, or batch access of data, while hiding the technical complexity of data retrieval. The interface provides an intuitive, business-like presentation of information, friendly enough for a non-technical person. A variety of tools can be typically used, such as data analytical tools, data mining, machine learning, etc.

In order to facilitate complex analysis and visualization, the data in warehouse is typically modelled multidimensionally. The best known knowledge discovery techniques are Online Analytical Processing (OLAP) and data mining (DM) techniques.

OLAP provides users with the means to explore and analyse large amounts of data, involving complex computations, their relationships, and visually present results in different perspectives. Typical OLAP operations include rollup (increasing the level of aggregation) and drill-down (decreasing the level of aggregation or increasing detail) along one or more dimension hierarchies, slice and dice (selection and projection), and pivot (re-orienting the multidimensional view of data).

Data mining is the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable, and predictive models from large-scale data 16. The relationships and summaries derived through a data mining exercise are often referred to as models or patterns. Examples include linear equations, rules, clusters, graphs, tree structures, and recurrent patterns in time series.