Sign Up Now
Get instant access to over 1 million+ study documents
OR
Already registered? click here to login
By creating your account, you agree to our terms of service, privacy policy and student honor code.
In addition to these two Director-level positions, the roles of the users of the databases and data mining applications also need to be taken into account. The sales, marketing, product management, product marketing, and services departments all need to have access to the databases and data mining applications. In addition, branch offices that access the company's applications over the shared T1 line will also need to have specific security roles assigned, especially if application and data are being accessed over the Web (Maheshwari, 1999). All of these roles must also be coordinated through the enterprise-wide security strategy (Yang, Li, Deng, Bao, 2010). Once this is accomplished, MMC will be able to more effectively attain its strategic plans with more secured systems.
Products for Ensuring Database and Data Mining Security
Given how distributed the company's offices are and the heavy reliance, they have on the use of their T1 lines and the Internet for VoIP, it is crucial for MMC to invest in networking routing and packet detection equipment in addition to firewalls and individualized system network security. For the databases and data mining software, suing biometrics to secure them at the administrator level is highly advisable (Amoruso, Brooks, Riley, 2005). In addition, the defining of IPSec protocols configuration options for the dedicated lines to the branches is advisable (Mattsson, 2009). For the 15 laptops in use, it is highly advisable that SSL-based VPN configurations also be included on them as well. The VoIP connections throughout the company also need to be benchmarked and audited for security to see how they can be improved. With audit and benchmark data the VoIP systems of a company can be significantly improved (Marsanu, 2006). In conclusion, MMC needs to start with an assessment of its most potentially threatening areas of the distributed network, analyzed and monitor network traffic and also secure the access points of the network using firewalls and role-based authentication. The use...
The integration of all the strategies mentioned in this paper needs to be accomplished so that the accounting, customer and financial reporting databases and data mining tools are secured to the role level. Once enterprise-wide security architecture is put into place MMC will also be able to grow more efficiently than before as well. The use of an enterprise-wide security architecture as the catalyst for future business growth has been proven by reducing costs of lost productivity due to lack of security (Bertino, Sandhu, 2005).
References:
Anthony J. Amoruso, Richard C. Brooks, & Richard a Riley Jr. (2005). Biometrics and Internal Control: An Emerging Opportunity. The Journal of Government Financial Management, 54(2), 40-44.
Elisa Bertino, & Ravi Sandhu. (2005). Database Security-Concepts, Approaches, and Challenges. IEEE Transactions on Dependable and Secure Computing, 2(1), 2-19.
Shuchih Ernest Chang, & Chienta Bruce Ho. (2006). Organizational factors to the effectiveness of implementing information security management. Industrial Management + Data Systems, 106(3), 345-361.
Harris, Duncan, & Sidwell, David. (1994). Distributed database security. Computers & Security, 13(7), 547.
Data Warehousing and Data Mining Executive Overview Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline
Data mining, a process that involves the extraction of predictive information which is hidden from very large databases (Vijayarani & Nithya,2011;Nirkhi,2010) is a very powerful and yet new technology having a great potential in helping companies to focus on the most important data in their data warehouses. The use of data mining techniques allows for the prediction of trends as well as behaviors thereby allowing various businesses to make proactive
Database Data Warehousing Design Data warehousing is a powerful tool that business organizations use to enhance competitive advantages. Data warehousing supports business decision by collecting, organizing and consolidating data for analysis and reporting using tools such as OLAP (online analytical processing ) and data mining. Typically, the relational database technology is generally being used to design a data warehousing and a relational database is a database having collection of tables, and
Database Data Warehouse Design Our company, Data Analytic Limited, specializes in collecting and analyzing data for various organizations. Over the years, we have assisted various companies to turn raw data into valuable information that assists the companies in making effective decision profitable in the short and long run. Our research and data analytics are geared towards giving extra edge to various companies. Our services include processing and analyzing terabytes of data
The ability to parse through the many records of transactions, customer contacts, and many other items stored electronically creates the foundation for data mining's definition. Data mining specifically is defined as the process of data selection, exploration and building models using vast data stores to uncover previously unknown patterns, insights, and observations that lead to strategies for effective differentiation and growth. Central to the development of data modeling is the creation of data and prediction models based
Data Warehouse and Business Intelligence In order to write a paper on the similarities and contrast between data warehouse and business intelligence, we need to first define each term before finding the similarities and contrasts between the two. Data Warehouses Data warehouse are used for storing data for archival, analysis, and security purposes. The warehouses themselves are made up of one or many computers (i.e. servers) that are connected together into one giant