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Business Intelligence

Notes 1.2.3 Data-Mining Engines

The ETL utilities make data collection from numerous diverse systems practical. Then, the data
needs to be converted into useful information. Some key points to remember:

Data are easily facts, figures, and text that can be processed by a computer.

Example: A transaction at retail point-of-sale is data.

Information is processed data. For example, analysis of point-of-sale transactions yields
information of consumer buying behaviour.
Knowledge represents a pattern that connects information and usually presents a high
grade of predictability as to what is recounted or what will happen next.

Example: An example of knowledge is the prediction of promotional efforts on sales of
particular items based on buyers’ buying behaviour.
Useful data-mining engines were evolved to support complex analysis and ad hoc queries on a
data warehouse’s database. Data mining looks for patterns among hundreds of seemingly
unrelated fields in a large database, patterns that recognize earlier unknown trends. These
trends play a key role in strategic decision making because they disclose localities for process
enhancement.

Example: Data-mining engines are those from SPSS and Oracle which are the foundation
for OLAP (Online Analytical Processing) systems.

1.2.4 Reporting Tools

The knowledge created by a data-mining engine is not very useful unless it is presented easily
and clearly to those who need it. There are many formats for reporting information and
knowledge results. One of the common techniques for displaying information is the digital
dashboard (shown in Figure 1.4).
Figure 1.4: Digital dashboard

Source: http://www.powerhealthsolutions.com/images/PBR_DigitalDashboard_KPIs.png

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