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DBMS And Data Warehouses 1 In This Essay

DBMS and Data Warehouses (1) in this writing assignment, you will create a brochure advertising your services as a data repository.

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Your Data Is Critical to Your Growth!

Three reasons why your business needs to collect and analyze data:

1. Gain valuable customer intelligence & insight to design better sales & service campaigns, driving up Lifetime Customer Value.

2. Gain insights into managing suppliers and entire supply chains better.

3. Know which distributors and dealers are the highest and lowest performing, and why.

4. Understand how gross margins and profitability change and why across an entire product lifecycle.

Describe the features of a data mart. Differentiate between unsupervised and supervised data mining.
The functions of a data warehouse include integrating the diverse and often disparate legacy database and transactional systems of an enterprise into a single, unified system of record (Benander, Benander, Fadlalla, Gregory, 2000). Data warehouses are also require an intensive amount of integration platform support. Of the many integration technologies now being adopted to unify data warehouses, XML is increasingly being adopted as a standard (Van, 2002). Data warehouses are often designed to support multiple ontologies of data structures essential for the running of a business as well, which forces a logical file and data structure throughout the entire entity (Selma, Ilyes, Ladjel, Eric, Stephane, Michael, 2012). A second function of a data warehouse is to segment out the data that is used more frequently for queries from the actual data warehouse which is used for reporting and analysis. This second aspect of a data warehouse ensures higher levels of performance on more frequent queries.

A data mart is often a smaller subset of a data warehouse or other data platform, often designed to support the needs of a given business strategy or objective (Benander, Benander, Fadlalla, Gregory, 2000). The term data mart can apply equally to evaluational data, operational data, metadata, or spatial data. Data marts are designed to align with the strategic information requirements of a given business unit or functional area of an organization (Benander, Benander, Fadlalla, Gregory, 2000). Examples of this include specific data marts dedicated to accounting, finance, marketing, sales and service. A data mart is designed often as a one-dimensional model or star schema composed as a factor table or multi-dimensional table (Benander, Benander, Fadlalla, Gregory,…

Sources used in this document:
References

(Benander, Benander, Fadlalla, Gregory, 2000)

Benander, A., Benander, B., Fadlalla, A., & Gregory, J. (2000). Data warehouse administration and management. Information Systems Management, 17(1), 71-80.

Choudhary, A.K., Harding, J.A., & Tiwari, M.K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501-521.

He, Z., Lee, B.S., & Snapp, R. (2005). Self-tuning cost modeling of user-defined functions in an object-relational DBMS. ACM Transactions on Database Systems, 30(3), 812-812.
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