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Case Study Of Database Security Case Study

Database Security Case Study Database Security: Case Study

The objective of this study is to answer specific questions following have read the case study which is the focus of this work in writing including naming the concepts which are illustrated in the case study and why a customer database is useful for companies related in the case study under review. This work will additionally answer as to what would occur if the companies did not keep their customer data in databases. Secondly this study will determine the effectiveness of the SAS statistical modeling software that is described in the case study and will answer as to how better data management and analytics improve each company's business performance and provide two examples of improvement realized through mining customer databases. This study will additionally describe some of the weak points of predictive analytics and answer as to what management, organization, and technology factors are responsible for those weaknesses. Finally, this study will answer as to the purpose of Target's predictive analytics team in terms of the benefits of predictive analytics and answer as to whether there are any ethical issues raised by mining the customer database.

I. Concepts Illustrated

Concepts illustrated in this case include the concept of 'data mining' or the process of reviewing customer demographics, purchase history and other customer-specific historical data and using that data to predict future customer behavior. Business analytics is another concept illustrated in this case such as that utilized by Monster.com which is reported to be used specifically for the purpose of scaling "back its broad-based brand advertising in favor of a more targeted multi-channel approach." (p.243) It is explained that Monster.com realizes the majority of its revenue from "employers who pay to post job listings and to search its resume database." (p. 243) In addition it is reported that employees are able to search Monster.com's job database and post their resumes and do so without paying a charge. In the past Monster.com had a campaign that began with an email however, historically...

However, due to the economic downturn and the unemployment rates rising Monster.com is reported to have been "inspired to look for a more cost-effective approach." (p.243)
II. Usefulness of Customer Database and Result if Customer Database Information Not Kept Available

The usefulness of the customer database in the study under reviewed enabled the companies to use effective predictive modeling to predict customer purchase behavior in the future. If the customer database has not been kept, this predictive modeling would not have been possible.

III. Effectiveness of the SAS Statistical Modeling Software

It is reported by SAS.com that SAS ensured effective response modeling through the new targeting mindset that is: (1) Predictive; (2) Interpretable; (3) Actionable; (4) Customized; and (5) Proactive. (2014, p. 1) SAS enables the construction of "a battery of predictive modeling." (SAS.com, 2014, p. 2)

IV. Improvement of Business Performance Using Data Management and Analytics -- Two Decisions that Improved Through Mining of Customer Databases

The business intelligence analytics tools used by Monster's business are useful in conducting examinations of "industry, company size, and location" and through this examination the company is enabled to "score the data and target a subset of around 1,000 human resources executive identified as top prospects who might merit special treatment" this is used in promoting Monster's Power Resume Search service. (p. 244) In addition, The Unica data is utilized by Monster in beginning selection of prospects through the website Linkedin and selection of prospects through other such social networks that are business-oriented networks. Monster tracks customer behavior through use of Unica so that its sales force can identify telemarketing that is prioritized using follow-up calling. The example stated is that the customer who has opened up more than two emails…

Sources used in this document:
Bibliography

Asking the Customer by Asking the Database: Case Study (nd) Chapter 6 -- Foundations of Business intelligence; Databases and Information Management.

Eckerson, WW (2007) Predictive Analytics: Extending the Value of Your Data Warehousing Investment. TDWI Best practices report. Retrieved from: http://www.sas.com/events/cm/174390/assets/102892_0107.pdf

Effective Response Modeling (2014) SAS.com. Retrieved from: http://www.sas.com/success/pdf/ing.pdf

Fule, P. (2004) Detecting Privacy and Ethical Sensitivity in Data Mining. Researchgate. Retrieved from: www.researchgate.net/...Ethical...Data_Mining.../3deec51f7...
Seltzer, W. (nd) The Promise and Pitfalls of Data Mining: Ethical Issues. ASA Section on Government Statistics. Retrieved from: http://www.amstat.org/committees/ethics/linksdir/Jsm2005Seltzer.pdf
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