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Uses, structures, and terminology |
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What kinds of information are collected by
typical businesses? |
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How could that information be used to identify
your best customers? |
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Data is collected in numerous systems |
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Each system has its own purpose |
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Each system has its own needs (usually speed) |
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Consequences are data redundancy, data
integrity, and data isolation |
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The ultimate consequence is bad decisions – poor
service and weak profits. |
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Data elements need to be grouped logically |
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Entity- relationship models show groups |
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Normalization rules help define groups |
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Example:
Fred Smith buys Prod#349 from Nabisco, Prod#1128 from Pepsi and
Prod#19 from Heinz. What data
structures would be necessary to total his purchase, update our inventory,
and update information about his buying habits? |
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4 parts: |
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Data Model – (selected structures) |
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Data Definition Language |
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Data Manipulation Language |
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Data Dictionary |
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Hierarchical – (Tree) |
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Network |
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Relational |
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Usually longitudinal views of selected data
elements. |
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Searched for trends and relationships |
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Selection of data elements frames most likely
questions |
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Advanced statistical functions used to tease out
relationships |
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Search for |
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Classification |
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Clusters |
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Associations |
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Sequences |
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Forecasts |
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Example – processes of Amazon.com |
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Notes