Integrated Customer View
Order Life Cycle
P & L Analysis
The History of Business Intelligence
The Early Days of Computing
The concept of the data warehouse (Figure 1) is a single system that is the repository of all of an organization’s data in a form that can be effectively analyzed so that meaningful reports can be prepared for management and other knowledge workers.
However, meeting this goal presents several very significant challenges:
- Data must be acquired from a variety of incompatible systems.
- The same item of information might reside in the databases of different systems in different forms. A particular data item might not only be represented in different formats, but the values of this data item might be different in different databases. Which value is the correct one?
- Data is continually changing. How often should the data warehouse be updated to reflect a reasonably current view?
- The amount of data is massive. How is it analyzed and presented simply so that it is useful?
- Extract, Transform, and Load (ETL) utilities for the moving of data from the various data sources to the common data warehouse.
- Data-mining engines for complex predetermined analyses and ad hoc queries of the enterprise data stored in the data warehouse.
- Reporting tools to provide management and knowledge workers with the results of the analysis in easy to absorb formats.
- the selection of data to load.
- the translation of encoded items (for instance, 1 for male, 2 for female to M, F).
- encoding and standardizing free-form values (New Jersey, N. Jersey, N. J. to NJ).
- deriving new calculated values (sale price = price - discount).
- merging data from multiple sources.
- summarizing (aggregating) certain rows and columns.
- splitting a column into multiple columns (for instance, a comma-separated list).
- resolving discrepancies between similar data items.
- validating the data.
- ensuring data consistency.
- The data in the data warehouse is stale. It could be weeks old. Therefore, it is useful for strategic functions but is not particularly adaptable to tactical uses.
- The source database typically must be quiesced during the extract process. Otherwise, the target database is in an inconsistent state following the load. With this result, the applications must be shut down, often for hours.
The ETL utilities make data collection from many diverse systems practical. However, the captured data needs to be converted into information and knowledge in order to be useful.
To expound on this concept,
- Data are simply facts, numbers, and text that can be processed by a computer. For instance, a transaction at a retail point-of-sale is data.
- Information embodies the understanding of a relationship of some sort between data. For example, analysis of point-of-sale transactions yield information on consumer buying behavior.
- Knowledge represents a pattern that connects information and generally provides a high level of predictability as to what is described or what will happen next. An example of knowledge is the prediction of promotional efforts on sales of particular items based on consumers’ buying behavior.
Today, many versions of digital dashboards are available from a variety of software vendors. Driven by information discovered by a data-mining engine, they give the business manager the information required to:
- immediately see key performance measures.
- identify and correct negative trends.
- measure efficiencies and inefficiencies.
- generate detailed reports showing new trends.
- increase revenues.
- decrease costs.
- make more informed decisions.
- align strategies and organizational goals.