By Rob Mattison
A telecommunications-specific consultant to information warehousing and mining, this paintings bargains step by step instructions for designing and providing information warehousing and mining functions, utilizing a couple of case reviews and real-world examples.
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Additional info for Data Warehousing and Data Mining for Telecommunications (Artech House Computer Science Library)
The organizational structure is about the management of people, to manage them to levels of optimum effectiveness and efficiency. Therefore, our optimum data warehousing environment can only be developed when we understand the relationships between all three of these aspects of the business and we can figure out how to align them effectively. 4 Overall strategy for development (one piece at a time, fitting into the overall architecture) The job of figuring out the complete and perfect alignment between all of these different aspects of the business is clearly going to be a large, complicated, and 46 Data Warehousing and Data Mining for Telecommunications arduous task.
2 Alignment of the organizational structure and the core systems into knowledge pools. 3). The risk (and the observation) is that the fragmentation of processes creates knowledge “sinks” that will tend to hold on to, protect, insulate, or sometimes even hoard information that might be useful to other areas of the business, but which cannot be accessed for a wide variety of reasons: 1. It might be that the information in one knowledge silo is mechanically or technologically inaccessible for any number of reasons.
Every theory of data management before data warehousing was driven by several fundamental principles. For over 40 years the industry has been a slave to these principles. What is truly revolutionary about warehousing is the way that it discards or drastically alters the attention we give to these principles. 1 The “science” of data management the “old fashioned” way The core concepts that have for years dictated the rules of delivery for DBAs and system developers have been these: 1. The elimination of data redundancy and the minimization of disk storage space—Everybody who was anybody agreed that the primary goal of any data management exercise was to try to figure out how to minimize the amount of data that was being stored.
Data Warehousing and Data Mining for Telecommunications (Artech House Computer Science Library) by Rob Mattison