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How Business Intelligence Tools Are Used with Data Ware Houses

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Introduction.

Business intelligence refers to software, hardware and practices applied in collecting data, processing it and analyzing it in order to assist managers in informed decision making. A Data Warehouse is composite storage area for data taken from several areas in the company.

Data warehousing is therefore a core module in business intelligence as it allows information to be stored in a common place, therefore enabling fast access and is often analyzed and used for making informed decisions by managers and other cooperate end users.

Best practices to be followed when designing a data warehouse for the use of business intelligence.

Sponsorship.

Make sure you have a high-level business sponsor not only during the building but also one that remains present even after the data warehouse has been built and is presented ready for use. The business sponsor ought to be communicating with other business partners. Data owners and handlers are obliged to establish full ownership pertaining the data there-in.

Room for growth.

Data is continually going to be added, therefore when designing it one should consider to leave enough room for incremental addition of data. To add on to that, you should also remember to retain the team of developers that assisted in building for this purpose.

Expertise.

Depending on the manner in which you want to design your required system, you should select experts experienced in that area to advice you on the best designing methods in addition to validating your resources and choice of technologies.

Minimized scope.

During the early steps in building, ensure you reduce the subject areas to be worked on so as to lower the investment in the initial build and assist in acquiring experience in a smaller area to be expanded later.

Loading the data warehouse.

Tools such as Utility Extract / Transformation / Load (ETL) software tool among other tools can be used to verify for example, the quality of data is maintained. The frequency in which to load data into the data warehouse is another element that should be considered.

Education and support.

One needs to avail the necessary information describing the new system including the techniques and modelling tools together with its structure and method of how to access information from the data warehouse. This is to be used by cooperate users of the system who may not be familiar with how it operates.

Future Modification.

One ought to ensure the modification process of the data warehouse is controlled and documented in detail.

Steps for implementing a data warehouse.

Carrying out a feasibility study.

The costs and benefits of implementing a data warehouse are defined as well as the elements that contribute to the successful implementation of the system. One puts into consideration the advantages and disadvantages of implementing the required system. The results of the feasibility study are presented to the managers in the company to decide on the sponsors. Tasks of each of the developer team members involved in the implementation are also defined.

Business line analysis.

This involves getting to familiarize with the organization for which the system is built, its operations and business requirements. The data warehouse scope is defined at this juncture.

Design of the data warehouse architecture.

The physical and logical foundation of the system to be built which includes the support architectures, data and technical structures necessary for implementation as well as the applications are designed.

Selection of the technological solution.

The best tools for use in implementing the data warehouse are chosen. The technical and business requirements for the implementation need to be put into consideration.

Project iterations planning.

User and technical requirements for the new system are redefined, the restrictions brought about by the present systems are also noted and documented.

Data Warehouse Modelling.

The database schema is achieved, metadata is defined and data source list is updated to include all the information necessary for the building and setting up of the new system.

Testing and implementation.

The developers test the system and later involve the users to use the system in an attempt to detect and eliminate any errors in the system.

Roll-out and deployment.

The system is the presented to the managers and other cooperate users for use.

Data warehouse maintenance techniques and strategies.

A data warehouse management system is used in the maintenance of the data warehouse.

Maintenance techniques and strategies include the following:

View Synchronization.

In this technique, any affected view definition is rewritten in the data warehouse whenever there is a schema change in thus making the current view definition undefined.

Incremental view maintenance.

This technique maintains the data warehouse extent every time a data update occurs within a source.

View Adaptation.

This technique enables the adaptation of the view extent incrementally after the view definition has been changed either directly by the data warehouse designer or indirectly by the view synchronization system.

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How Business Intelligence Tools Are Used With Data Ware Houses. (2019, Jun 27). GradesFixer. Retrieved October 26, 2020, from https://gradesfixer.com/free-essay-examples/how-business-intelligence-tools-are-used-with-data-ware-houses/
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How Business Intelligence Tools Are Used With Data Ware Houses. [online]. Available at: <https://gradesfixer.com/free-essay-examples/how-business-intelligence-tools-are-used-with-data-ware-houses/> [Accessed 26 Oct. 2020].
How Business Intelligence Tools Are Used With Data Ware Houses [Internet]. GradesFixer. 2019 Jun 27 [cited 2020 Oct 26]. Available from: https://gradesfixer.com/free-essay-examples/how-business-intelligence-tools-are-used-with-data-ware-houses/
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