Information Technology is the technology to extract, generate and
distribute information. And information is very clearly defined as
meaningful data. Information is data that has been processed into a form
that is meaningful to the recipient. It should be very evident that
data management should be the core of any information technology
thinking. Datawarehousing and data mining are avenues in data management
to get better information.
Data warehouse stores information from various databases into a single location - cleaned and processed into the right formats for analysis. The process of transforming raw data into data warehouse involves steps such as extraction – getting data out of original database and transferring it to database infrastructure. Consolidation is process of combining data from several sources into one database. Cleansing is the process of correcting data.
Filtering is process of removing unnecessary information. Aggregation is process of combining data. Conversion is process of translating data into the model used by the warehouse.
A data mart is a specialised set of business information focusing on a particular aspect of the enterprise. Many companies choose to feed a data mart from a data warehouse because the information in the warehouse has already been consolidated and processed from the same raw data. An operational data store is a hybrid of an OLTP system and an analytical system. It contains information that's frequently updated on an ad hoc basis, often in response to changes in the OLTP system, as opposed to the scheduled updates of a data warehouse.
Warehousing brings together data. Mining sorts through the data collected and turns up interesting and useful connections. It all starts with a load of finely detailed historical data that needs to be sifted through for gems.
"Data warehousing is similar to any ordinary warehouse where one stores goods and takes out when required," says Dr Girish Kumar, DGM – IS, BPCL. Transactional data needs to be collated into a given area so that analysis can be conducted. BPCL, he says, has implemented SAP business warehouse by consolidating data at customer level. Analytical tools are used to generate management information system reports as per user requirements.
LIC plans to implement data warehousing and data mining, says T S Vijayan, chief – IT, BPR, LIC. Conceptually, in a technical sense, data warehousing comes first inasmuch as data needs to be collected at one location. After the data is collected, data mining extracts the required information. And from this MIS reports for management follow. However, in an organisation the management's need for information comes first and then the data mining is charted out and only after this data warehousing – collating data comes into the picture, says Vijayan. Datawarehousing has been implemented at LIC to get all historical transactions in one area. Another reason is to link up to other databases. Datamining is implemented to get more information on customer for up selling, cross selling and for other marketing purposes.
Data mining is mainly for analysis, says S B Patankar, director – information systems, Bombay Stock Exchange. This requires data to be in a particular format. Thus data warehousing is a prerequisite for data mining.
Data warehouse stores information from various databases into a single location - cleaned and processed into the right formats for analysis. The process of transforming raw data into data warehouse involves steps such as extraction – getting data out of original database and transferring it to database infrastructure. Consolidation is process of combining data from several sources into one database. Cleansing is the process of correcting data.
Filtering is process of removing unnecessary information. Aggregation is process of combining data. Conversion is process of translating data into the model used by the warehouse.
A data mart is a specialised set of business information focusing on a particular aspect of the enterprise. Many companies choose to feed a data mart from a data warehouse because the information in the warehouse has already been consolidated and processed from the same raw data. An operational data store is a hybrid of an OLTP system and an analytical system. It contains information that's frequently updated on an ad hoc basis, often in response to changes in the OLTP system, as opposed to the scheduled updates of a data warehouse.
Warehousing brings together data. Mining sorts through the data collected and turns up interesting and useful connections. It all starts with a load of finely detailed historical data that needs to be sifted through for gems.
"Data warehousing is similar to any ordinary warehouse where one stores goods and takes out when required," says Dr Girish Kumar, DGM – IS, BPCL. Transactional data needs to be collated into a given area so that analysis can be conducted. BPCL, he says, has implemented SAP business warehouse by consolidating data at customer level. Analytical tools are used to generate management information system reports as per user requirements.
LIC plans to implement data warehousing and data mining, says T S Vijayan, chief – IT, BPR, LIC. Conceptually, in a technical sense, data warehousing comes first inasmuch as data needs to be collected at one location. After the data is collected, data mining extracts the required information. And from this MIS reports for management follow. However, in an organisation the management's need for information comes first and then the data mining is charted out and only after this data warehousing – collating data comes into the picture, says Vijayan. Datawarehousing has been implemented at LIC to get all historical transactions in one area. Another reason is to link up to other databases. Datamining is implemented to get more information on customer for up selling, cross selling and for other marketing purposes.
Data mining is mainly for analysis, says S B Patankar, director – information systems, Bombay Stock Exchange. This requires data to be in a particular format. Thus data warehousing is a prerequisite for data mining.
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