Data Warehouses Study Guide
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An information systems study guide from the University of Minnesota.
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Data Warehouses - Study Guide
A major factor limiting business intelligence initiatives is getting data into a form where it can be used (i.e., analyzed and turned into information). Legacy systems often limit data utilization because they were not designed to share data, aren’t compatible with newer technologies, and aren’t aligned with the firm’s current business needs.
Most transactional databases aren’t set up to be simultaneously accessed for reporting and analysis. In order to run analytics the data must first be ported to a data warehouse or data mart. Data warehouses and data marts are repositories for large amounts of transactional data awaiting analytics and reporting. Large data warehouses are complex, can cost millions, and take years to build.
How might information systems impact mergers and acquisitions? What are the key issues to consider?
Discuss the possible consequences of a company having multiple plants, each with a different information system using different part numbers and naming conventions for identical products.
Why does it take longer, and require more processing power, to analyze sales trends by region and product, as opposed to posting a sales transaction?
List the issues that need to be addressed in order to design, develop, deploy, and maintain data warehouses and data marts.
What is meant by “data relevance”? What is meant by “data governance”?
What is the difference between a data mart and a data warehouse?
Why are data marts and data warehouses necessary? Why can’t an organization simply query its transactional database?
How can something as simple as customer gender be difficult to for a large organization to establish in a data warehouse?
11.4 Data Rich, Information Poor and 11.5 Data Warehouses and Data Marts by University of Minnesota are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.