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Answer Upon - Components of a Data Warehouse Architecture - Part 4, Kimball vs Inmon
The Misconceptions of the Value Of Disclosures in Franchising dependent datamart).Disclosure laws in franchising are suppose to help the consumer. They don’t. The FTC, which over sees franchising has in fact created a rule, which makes 5 lb. Disclosure documents for franchise buyers, which is so huge that no one ever reads it. I know when I personally meet a franchise buyer whose application form is approved and hand them a UFOC, Uniform Franchise Offering Circular with attachments K: Data marts are linked to each other, based on conformed dimensions. I: Data marts are not linked to each other. K: A data mart maintains all available historical data. I: A data mart maintains limited history, since history is kept in the Enterprise Datawarehouse. Phased development approach K: phased development of datamarts on selected business processes, which are linked on conformed dimensions, forming the
K: Direct development of data marts on the selected business processes. Exclusive use of denormalized dimensional models (star schemas). I: Development of the Enterprise Datawarehouse (EDW) based on a normalized database schema. The development of data marts, is based on data retrieved from the EDW. Data mart definition K: A data mart maintains data of the lowest level of detail (atomic data), which relate to a business process. Data marts are developed based on the popular dimensional modelling methodology. I: A data mart maintains aggregate data which relate to a Business Unit. They are built to monitor predefined KPIs (key performance indicators). K: A data mart is built by extracting data directly from operational systems. I: A data mart is built by extracting data from the Enterprise Datawarehouse (also called dependent datamart). K: Data marts are linked to each other, based on conformed dimensions. I: Data marts are not linked to each other. K: A data mart maintains all available historical data. I: A data mart maintains limited history, since history is kept in the Enterprise Datawarehouse. Phased development approach K: phased development of datamarts on selected business processes, which are linked on conformed dimensions, forming the K: Direct development of data marts on the selected business processes. Exclusive use of denormalized dimensional models (star schemas). I: Development of the Enterprise Datawarehouse (EDW) based on a normalized database schema. The development of data marts, is based on data retrieved from the EDW. Data mart definition K: A data mart maintains data of the lowest level of detail (atomic data), which relate to a business process. Data marts are developed based on the popular dimensional modelling methodology. I: A data mart maintains aggregate data which relate to a Business Unit. They are built to monitor predefined KPIs (key performance indicators). K: A data mart is built by extracting data directly from operational systems. I: A data mart is built by extracting data from the Enterprise Datawarehouse (also called dependent datamart). K: Data marts are linked to each other, based on conformed dimensions. I: Data marts are not linked to each other. K: A data mart maintains all available historical data. I: A data mart maintains limited history, since history is kept in the Enterprise Datawarehouse. Phased development approach K: phased development of datamarts on selected business processes, which are linked on conformed dimensions, forming the K: Direct development of data marts on the selected business processes. Exclusive use of denormalized dimensional models (star schemas). I: Development of the Enterprise Datawarehouse (EDW) based on a normalized database schema. The development of data marts, is based on data retrieved from the EDW. Data mart definition K: A data mart maintains data of the lowest level of detail (atomic data), which relate to a business process. Data marts are developed based on the popular dimensional modelling methodology. I: A data mart maintains aggregate data which relate to a Business Unit. They are built to monitor predefined KPIs (key performance indicators). K: A data mart is built by extracting data directly from operational systems. I: A data mart is built by extracting data from the Enterprise Datawarehouse (also called dependent datamart). K: Data marts are linked to each other, based on conformed dimensions. I: Data marts are not linked to each other. K: A data mart maintains all available historical data. I: A data mart maintains limited history, since history is kept in the Enterprise Datawarehouse. Phased development approach K: phased development of datamarts on selected business processes, which are linked on conformed dimensions, forming the I: A data mart maintains aggregate data which relate to a Business Unit. They are built to monitor predefined KPIs (key performance indicators). K: A data mart is built by extracting data directly from operational systems. I: A data mart is built by extracting data from the Enterprise Datawarehouse (also called dependent datamart). K: Data marts are linked to each other, based on conformed dimensions. I: Data marts are not linked to each other. K: A data mart maintains all available historical data. I: A data mart maintains limited history, since history is kept in the Enterprise Datawarehouse. Phased development approach K: phased development of datamarts on selected business processes, which are linked on conformed dimensions, forming the K: Data marts are linked to each other, based on conformed dimensions. I: Data marts are not linked to each other. K: A data mart maintains all available historical data. I: A data mart maintains limited history, since history is kept in the Enterprise Datawarehouse. Phased development approach K: phased development of datamarts on selected business processes, which are linked on conformed dimensions, forming the datawarehouse Bus architecture. I: design of the whole Enterprise Datawarehouse based on the Enterprise ‘data model’. Phased implementation of subject areas, according to priorities set. International experience records difficulties in the successful implementation of the Inmon approach. On the other hand, enterprises which have developed independent, incompatible and uncoupled data marts without central coordination, are facing the challenge to consolidate them, in order to yield combined data analysis value. Consolidation requires redesign of a major part of the existing infrastructures. The Kimball approach, which receives increasing attention, does not propose implementation of uncoupled data marts. Copyright 2006 – Kostis Panayotakis
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