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It shows a hierarchy of components contributing to an organizations intelligence.
•Data comes in the form of raw observations, e.g. temperature, scores, and bits of news.
•Information adds context to data; it is created by analyzing relationships and connections between the data.
•Knowledge is created by using the information for action. Knowledge answers the question "how".
•Wisdom is created through the use of knowledge and through reflection. It is the ultimate level of understanding.
How can we transform data into information?
Condensation: Data is summarized. Unnecessary depth is eliminated.
Calculate: Data is analyzed mathematically or statistically.
Contextualization: We know why the data was collected.
Correction: Errors removed.
Categorization: Unit of analysis is known.
How can we derive Knowledge from Information?
Comparison: How does this situation compare to others?
Consequence: What implications does the information have for decisions and actions?
Connection: How does this bit of information relate to others?
Conversation: What do other people think about this information?
Define Data Warehouse using its main characteristics.
A Data Warehouse is a subject-oriented, integrated time-variant, non-volatile collection of data in support of management's decision-making process.
Describe the two different approaches in designing a data warehouse.
top-down: user requirements
bottom-up: which data sources are available
Describe the ETL tasks.
Extract data from source systems
Clean up data and transform it
Consolidate (index the data, summarize)
Load data into the DW
Maintain the metadata
Refresh the DW with updated data
Why are the ETL tasks so important for Data Warehouses?
ETL tasks ensure that the data in the DW is.
Which transformation problems could arise during the ETL process? List and describe them.
•Multipart keys: record key structures with built-in meaning
•Multiple local standards
•Complexity of integrating Multiple files
•Element names: variation in naming conventions in fields from source to source.
•Element meanings: different interpretations of the name of an element.
•Inconsistent Input formats
•Inconsistent data values
•Referential integrity constraints
•Name and address
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