Data Warehousing and Data Mining
EXECUTIVE SUMMARY
This study takes an insight into the usage of data warehousing and data mining techniques to enhance the productivity of the business. The study of the processes is analysed so as to get the need of adaptation according to inherent demands of these industries in near future. The main topics we are discussing here are: a) Data warehousing b) Data Mining c) ETL d) Data Mart
An attempt has been made to analyse different ways of using these for the enhancement in the different field.
Data warehousing and current trends.
Data Warehouse
A data warehouse is a relational database that is used for reporting and data analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In other words, the data in a data warehouse is made up of snapshots of a business’s multiple operational data-bases. It comprises of software and hardware optimized for executive information systems (EIS) and decision support systems and is combined to run on-line analytical processing (OLAP), rather than the on-line transaction processing which represents the operations world.
The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales etc.), ERP, CRM etc. as shown in the figure below. The data may go through an operational data store for additional operations before they are used in the DW
Data mining is intended for users who are statistically inclined. These analysts look for patterns hidden in data, which they are able to extract using statistical models. Data miners engage in question formulation based primarily on the "law of large numbers" to identify potentially useful relationships between data elements, which can be profitable to companies.
Data warehouse users,tend to be data experts
This study takes an insight into the usage of data warehousing and data mining techniques to enhance the productivity of the business. The study of the processes is analysed so as to get the need of adaptation according to inherent demands of these industries in near future. The main topics we are discussing here are: a) Data warehousing b) Data Mining c) ETL d) Data Mart
An attempt has been made to analyse different ways of using these for the enhancement in the different field.
Data warehousing and current trends.
Data Warehouse
A data warehouse is a relational database that is used for reporting and data analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In other words, the data in a data warehouse is made up of snapshots of a business’s multiple operational data-bases. It comprises of software and hardware optimized for executive information systems (EIS) and decision support systems and is combined to run on-line analytical processing (OLAP), rather than the on-line transaction processing which represents the operations world.
The data stored in the warehouse is uploaded from the operational systems (such as marketing, sales etc.), ERP, CRM etc. as shown in the figure below. The data may go through an operational data store for additional operations before they are used in the DW
Data mining is intended for users who are statistically inclined. These analysts look for patterns hidden in data, which they are able to extract using statistical models. Data miners engage in question formulation based primarily on the "law of large numbers" to identify potentially useful relationships between data elements, which can be profitable to companies.
Data warehouse users,tend to be data experts