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Business intelligence systems. Data warehousing. An orderly and accessible repositery of known facts and related data used as a basis for making better.

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Presentation on theme: "Business intelligence systems. Data warehousing. An orderly and accessible repositery of known facts and related data used as a basis for making better."— Presentation transcript:

1 business intelligence systems

2 Data warehousing. An orderly and accessible repositery of known facts and related data used as a basis for making better management decisions. A subject oriented, integrated, time -- varient and nonvolatile collection of data in support of management decision-making process. Makes it possible to organise data by subject rather than by process. Non-volatile means the data is stable after the initial formatting and cleaning

3 Data warehousing Data is identified by a time period additional features is efficiency, allowing quick retrieval of a specific types of data Within and data warehouses, data is classified and organised around subjects meaningfully company. Data from internal and external sources is integrated in a common format

4 Data marts Data warehouses are intended as permanent storage facilities. Data marts can exist in a number of different formats – data marts created with a subset of data warehouse information, usually focusing on information needed by a specific set of users – freestanding data marts, making them a quick and less expensive means of implementing data warehouse idea – a prototype for a future full-scale data warehouse.

5 Data marts Data once stored in data warehouse, is usually not changed without a compelling reason. To apply a data mining, an intermediate storage form is used. Data mart provides an advantage. New variables can be created without fear of these transformation contaminating data warehouse. Information expected to be pertinent only to the specific data mining and analysis can be extracted, which reduces computer time required to process that

6 Online analytic processing (OLAP) OLAP systems are multidimensional databases. These systems allow firms to deliver access to data and report generation tools throughout the organisation in virtual time. These systems support both queries and report generation. These applications will focus more on analysing trends and other aspects of organisational operations. The purpose of OLAP is summarising data with report focus. All OLAP products have spreadsheet, computational capability

7 Data quality. Data warehouse projects can fail for a number of reasons, and one of the most common is users refusal to accept the validity of data obtained from a data warehouse Data integrity requires that meaningless, corrupt or redundant data not be entered into data warehouse. The process of developing unique variable values is call data standardisation. Means to guarantee better quality begins with ensuring that data extraction process operates correctly. Once the data is stored in data warehouse, controls can be applied to detect accuracy and completeness. Data quality is very important in ensuring the accuracy needed for successful system use

8 Data mining. An analysis of large quantities of data stored in computers. One of the most prominent applications of data mining is customer relationship management. Data mining requires identification of a problem, along with collection of data that can lead to better understanding and computer models to provide statistical or other means of analysis.

9 Data mining. Two general types of data mining – Hypothesis testing involves expressing a theory about a relationship between actions and outcome. Data mining can be applied to identify relationships based on large quantities of data. – knowledge discovery: a preconceived notion may not be present, but rather be seen by looking at the data – data mining has been called exploratory data analyses. Classical statistical approaches are modified in determining. Versatility and scalability are important parameters

10 Benefits of data mining Identification of most profitable customers. Estimation of a lifetime value of customers. – Retailing – customer relationship management – credit card management – insurance – telecommunications – telemarketing – human resource management


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