Use our essay title generator to get ideas and recommendations instantly
Data Mining
Determine the benefits of data mining to the businesses when employing:
Predictive analytics to understand the behaviour of customers
"The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011). There are basically seven steps involved in the predictive analysis, these are: spotting the business problem, exploring various data sources, extracting patterns from data, building a sample model by making use of the data and problem, Clarify data -- finding valuable factors -- generating new variables, constructing a predictive model by making use of sampling and validating and deploying the model.
Decisions can be made very quickly by the business if they make use of this method as, they will have a lot…
References
Two Crows Corporation (1999) Introduction to Data Mining and Knowledge Discovery, http://www.twocrows.com/intro-dm.pdf
Angoss (2012) Predictive Analytics in the Cloud Solutions, http://www.angoss.com/predictive-analytics-solutions/cloud-solutions
Oracle.com (2008) Oracle Data Mining Concepts, http://docs.oracle.com/cd/B28359_01/datamine.111/b28129/clustering.htm
Tiwari, S. (2011) A Web Usage Mining Framework for Business Intelligence, http://www.ijecct.org/v1n1/4.pdf
Data Mining in Health Care
Data mining has been used both intensively and extensively in many organizations.in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,.&Kerstingt, 2010).
Benefits of using data mining in health care
Care providers can make use of data analysis in identifying effective treatments and the best practices. This can be achieved through making comparison of causes, symptoms and adverse effects. Data mining can also be used in making analysis of the cause of action that will be effective for a specific group of patients. This can also be used in the identification of best clinical practices hence help in the development of guidelines and standards of care. To patients data mining is useful in that they can…
References
Kusserow R,(2010).Benefits of Data mining in HealthCare: The future has arrived. Retrieved 20 January from http://www.compliance.com/articles/benefits-of-data-mining-in-healthcare/
Kirby, M,.Flick, E.&Kerstingt, M.(2010).The Benefits of Digging Deeper: Using Data mining to improve Employee Health and reduce Employee costs. Retrieved 20 January from http://www.sibson.com/publications/perspectives/volume_18_issue_2/digging-deeper.html
Conjecture corporation.(2003). What Are the Different Types of Data Warehousing Tools? Retrieved 20 January from http://www.wisegeek.org/what-are-the-different-types-of-data-warehousing-tools.htm
Data Mining
Businesses can receive many benefits from data mining. Which benefits they receive, however, can also depend on the way in which their data mining is undertaken. Predictive analytics are used to understand customer behavior, and businesses use the behavior of the customer in the past to attempt to determine what the customer will do in the future (Cabena, et al., 1997). While it is not an exact science, many companies believe they can use it in order to decide which products will sell most often to which customers (Nisbet, Elder, & Miner, 2009). Association discovery is another type of data mining, and is more involved with the products that are sold and how they match up to specific types of customers, as opposed to specific customers by name or other determination (Nisbet, Elder, & Miner, 2009). In other words, predictive analytics look at what customer A will buy…
References
Cabena, P., Hadjnian, P., Stadler, R., Verhees, J., & Zanasi, A. (1997). Discovering data mining: From concept to implementation. New York, NY: Prentice Hall.
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis & data mining applications. New York, NY: Academic Press/Elsevier.
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 and yet highly informed knowledge-driven decisions. Data mining can therefore help businesses in answering various business questions that in the past have been considered too time consuming to analyze and solve. Companies can therefore collect as well as refine large quantities of data in order to gain a competitive advantage from the hidden predictive patterns contained within. In this paper we determine benefits of data mining to the businesses when employing:
Predictive analytics to understand the behavior of customers
Associations…
References
Ahmed, S., A. (2004) 'Applications of Data Mining in Retail Business', IEEE Computer society international Conference on Information Technology: Coding and Computing (ITCC'04).
Berry, M.J.A. And Linoff, G. (1997) Data mining techniques for marketing, sales and customer support, USA: John Wiley and Sons.
Chen, S., Y. And Liu, X. (2005) 'Data mining from 1994 to 2004: an application -- oriented review', International journal of business intelligence and data mining, vol.1, No.1, pp.4-21
Hui, S., C. And Jha, G. (2001) 'Application of data mining techniques for improving customer services', International Journal of computer applications in Technology, Vol.14, No.1-3, pp.64-77.
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 their business models are a case in point. The greater the level of economic uncertainty, perceived and actual risk in any given strategy or endeavor, the more the reliance on EDW, data mining and advanced forms of predictive modeling including analytics (Sen, amamurthy, Sinha, 2012). From this standpoint, the emerging areas of high growth in the global economy are attracting a high level of investment in EDW, data mining, predictive modeling and analytics. The latest figures illustrate how…
References
Applications to "visualize" the data stream. (1999). American Bankers Association.ABA Banking Journal, 91(3), 56-56.
Brachman, R.J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., & Simoudis, E. (1996). Mining business databases. Association for Computing Machinery.Communications of the ACM, 39(11), 42-48.
Jeffrey H. Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.
Fay, C.P., & Zahay, D. (2003). Understanding why marketing does not use the corporate data warehouse for CRM applications. Journal of Database Marketing & Customer Strategy Management, 10(4), 315-326.
The use of databases as the system of record is a common step across all data mining definitions and is critically important in creating a standardized set of query commands and data models for use. To the extent a system of record in a data mining application is stable and scalable is the extent to which a data mining application will be able to deliver the critical relationship data, predictive analytics and accurately reflect the associations most critical to companies (Kuhn, Ducasse, Girba, 2007). The uses of multidimensional database systems are essential for creating the system of record on which data mining applications are based on. Data warehouses are the system of record these data mining applications rely on for completing more extensive analysis of the data sets they have available. The third process is the development of user-based applications that make queries of the data sets possible, including role-based…
References
Berry (2004) - Survey of Text Mining Clustering, Classification, and Retrieval Berry, Michael W. (Ed.) 2004, XVII, 244 p. 57 illus., Hardcover ISBN: 0-387-95563-1
Buddhakulsomsiri, J., & Zakarian, a.. (2009). Sequential pattern mining algorithm for automotive warranty data. Computers & Industrial Engineering, 57(1), 137.
Cressionnie, L.. (2008). Ready for Takeoff. Quality Progress, 41(7), 59-61.
da Cunha, C., Agard, B., & Kusiak, a.. (2010). Selection of modules for mass customisation. International Journal of Production Research, 48(5), 1439.
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 on data collected from a variety of sources,
including corporate transactions, customer histories, and demographics,
even external sources such as credit bureaus and services organizations
that sell content (Westphal, C., Blaxton, T., 34). Companies
accomplishing best practices in data mining then use the many data and
prediction models to produce patterns in the information that can support
decision making and predict new business opportunities. What's unique
about data mining is the ability to…
Data Mining
The amount of knowledge available in today's world is massive. The information technology specialist who's responsible to his or her organization for maximizing the capacity for practical usage of this knowledge, it is becoming increasingly difficult to have a total grasp of the problem. The purpose of this essay is to discuss the importance of implementing data warehousing and mining systems inside an organization. In order to do this, it is necessary to contrast the positive benefits of data mining and contrast those ideas with the negative connotations associated with the similar processes.
Data mining, according to Thearling (2009) is "the automated extraction of hidden predictive information from large databases. " Additionally, data mining is a proactive and aggressive tactic that can serve the overall business strategy when properly aligned. Statistical analysis is inherent within any type of data mining technique and is expressed in these terms. The…
Bibliography
Betancourt, L. How Companies are using your Social Media Data, http://www.enterpriseirregulars.com/5706/the-top-10-trends-for-2010-in-analytics - business-intelligence-and-performance-management/
Greenfield, L. (2005) The Case for Data Warehousing http://www.dwinfocenter.org/casefor.html
Greenfield, L.Greenfield, L. (2005) The Case Against Data Warehousing http://www.dwinfocenter.org/against.html
Thearling, K. (2009) An Introduction to Data Mining http://www.thearling.com/dmintro/dmintro_2.htm
The tools used, in this case, for knowledge discovery and data mining where based on artificial neural networks (ANN) and consisted of four different models. All models represented supervised learning models with a known output. The four models of the ANN were dynamic network, prune network, the multilayer perceptron, and the radial basis function network.
The main challenge for its implementation was that data needed to be cleaned so the data mining process can easily retrieve these data and process them according to the required use. All abnormalities needed to be eliminated since the outcome would have been impacted by invalid or erroneous information.
The result of the business intelligence implementation was successful and the VHA could reach their goal of reducing the length of stay for each patient where necessary. Also a better productivity and a more efficient care program for their patients was a result of this implementation.…
Bibliography
Kraft, M.R., Desouza, K.C., & Androwich, I. (2003). Case Study of a Veterans' Administration Spinal Cord Injury Population. Retrieved April 24th, 2010, from Data Mining in Healthcare Information Systems: http://csdl2.computer.org/comp/proceedings/hicss/2003/1874/06/187460159a.pdf
Smalltree, H. (2006, July 20th). Business Analytics/Business Intelligence News. Retrieved April 23rd, 2010, from Business intelligence case study: Hospital BI helps healthcare: http://searchbusinessanalytics.techtarget.com/news/1507291/Business-intelligence-case-study-Hospital-BI-helps-healthcare
Similarly, the Air Force needed no only some intelligent reporting capailities, ut a way that Air Force personnel, government employees, and civilian IT contractors would work together in the evaluation of applications and reports in a more roust and real-time manner. "The intent was to provide the Keystone user community the aility to do more complex financial analysis and reporting on a "self-service" asis to reduce overall system maintenance and development costs' (Air Force).
The SAP Business Oject tools are useful for management and organizations that already have a decent set of data, ut need access to it in ways that are more meaningful to the organization. For example, in the new social-media paradigm there are literally hundreds of different demographic and psychographic modifiers that might assist firms in moving from an older marketing paradigm to a new, service to the client model, all y using data mining and warehousing…
bibliography, but to keep track of methods, research items, and/or even models within reference works. Small business owners can find ways to shave time and money from processes; and using a data mining concept, they can control their own costs and inventory management, as well as touch the customer more often in a way that is more conducive to long-term relationship building.
REFERENCES
Bardoliwalla, N. (December 1, 2009). The Top 10 Trends for 2010 in Analytics, Business Intelligence, and Performance Management. Enterprise Irregulars. Retrieved from: http://www.enterpriseirregulars.com/5706/the-top-10-trends-for-2010-in-analytics -business-intelligence-and-performance-management/' target='_blank' REL='NOFOLLOW'>
Data mining is very important for operational effectiveness but when / how to stop mining data before it becomes more trouble than it's worth?
Over the last several years, advancements in technology have meant that an increasing number of companies are using data mining to be able to understand the demographics of their customers. This is when they will look at large amounts of information to figure out specific buying habits and patterns. However, the rise in the large number of corporations that are using these practices has been facing increasing levels of scrutiny. This is because these techniques are being utilized as a way to: understand the overall demographics of customers and their shopping patterns. As a result, the use of this technique has been the focus as to how and when data mining should be conducted by organizations. To understand this, we will look at a number of…
Bibliography
Health Information Privacy. (2011). HHS. Retrieved from: http://www.hhs.gov/ocr/privacy/hipaa/understanding/consumers/index.html
Supreme Court Takes Up Case on Data Mining. (2011). PBS. Retrieved from: http://www.pbs.org/newshour/bb/law/jan-june11/scotus_04-26.html
Graettinger, T. (2010). Digging up Dollars. T Dan. Retrieved from: http://www.tdan.com/view-featured-columns/12370 '
Reidy, J. (2005). Hard Sell. Kansas City, KS: Andrews MccMeel
Computer Science
The overall theme or focus:
The media industry is an industry that is resistant to the validity of data mining and the kind of insight data mining in this field could yield.
There are two primary pieces of software with respect to film and television editing. They are Avid and Final Cut Pro, while there are more programs available. The latest version of Final Cut Pro, FCPX, takes more a data mining perspective and approach to nonlinear editing. There were significant changes with respect to aesthetics, interface, and organization. There was a bit of a well documented uproar by various editors and other industry professionals with respect to these changes. There is a great resistance to the use of FCPX because of the data mining approach relative to other upgrades. This is just one example of media's resistance to data mining.
The media industry is still a bit…
algoithms that can mine mounds of data that have been collected fom people and digital devices have led to the adoption of data mining by most businesses as a means of undestanding thei customes bette than befoe. Data mining takes place in etailing and sales, banking, education, manufactuing and poduction, health cae, insuance, boadcasting, maketing, custome sevices, and a numbe of othe aeas. The analytical infomation gatheed by data-mining applications has given some businesses a competitive advantage, an ability to make infomed decisions, and bette ways to pedict the behavio of customes.
Wite a fou to five (4-5) page pape in which you:
Detemine the benefits of data mining to the businesses when employing:
Pedictive analytics to undestand the behavio of customes Associations discovey in poducts sold to customes
The collected data can help the manage detemine which poducts best inteest the customes and which may inteest them in the…
references. Much of this may be private aside from which the individual may not
data mining?
The foundational elements of data mining are multidisciplinary in nature, encompassing analytics, computer science, database systems integration and management, statistics and artificial intelligence. Often these technologies are used to create a single system of record used for analysis and advanced queries by the enterprises who build them. Data mining is often included in business intelligence (BI) suites and the analytics layer of an enterprise-wide computing system, as each application needs to gain access to the metrics and key performance indicators (KPIs) (Peacock, 1998). The use of data mining has become more pervasive in marketing, sales and service as organizations strive to gain insights from the terabytes of data they have accumulated over years and in some cases decades of operation. Data mining can provide marketers with greater insights into the preferences, needs and wants of customers, in addition to potential new product or service ideas based on a…
References
Craft, S.H. (2001). An empirical investigation of international consumer market segmentation decisions. The George Washington University). ProQuest Dissertations and Theses,, 155-155
Ganeshasundaram, R., & Henley, N. (2006). The prevalence and usefulness of market research: An empirical investigation into 'background' versus 'decision' research. International Journal of Market Research, 48(5), 525-550.
Koh, H.C., & Chan Kin, L.G. (2002). Data mining and customer relationship marketing in the banking industry. Singapore Management Review, 24(2), 1-27.
Peacock, P.R. (1998). Data mining in marketing: Part 1. Marketing Management, 6(4), 8-18.
2001, p.160).
esults also indicated that patients between the ages of forty-six and sixty-four incurred significantly larger financial losses for the facility than the rest of the patients and Medicare and Medicaid nonexempt patients admitted via routine admission had the highest average loss for inpatient visits. Also, on Tuesdays, the average loss per patient was significantly greater for patients in the region. Only with data-mining could such an apparently insignificant finding as the predominance of Tuesday admittances come to light. The reason for this was not random: "Examination of Tuesday's admitting physicians" revealed that "several of the physicians were in the same medical specialty. This specialty cared for patients that typically required a high level of service intensity over a long period of time. The identification of a subset of patients with disproportionately high costs has prompted the institution to reevaluate its admission criteria to this unit" and also the…
References
Silver, Michael. Taiki Sakata; Hua-Ching Su; Charles Herman; Steven B. Dolins; & Michael J. O'Shea. (2001, Summer). "Case study: How to apply data mining techniques in a healthcare data warehouse." Journal of Healthcare Information Management. 15(2). Retrieved June 5, 2010 at http://www.himss.org/content/files/jhim/15-2/him15208.pdf
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…
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.
Introduction
There is exponential growth in the amount of data collections that contain person-specific information. The organizations that collect this data are entrusted to ensures that the data remains private and that no external entities have access to the data. However, there are instances that the data can be beneficial to researchers and analysts in their attempts to answer numerous questions. In many cases, organizations would like to share this data while protecting the privacy of the individuals. In an attempt to protect the privacy, it becomes hard for the organization to preserve the utility of the data, which would result in less accurate analytical outcomes (Sweeney, 2002). The data owner would like to have a way that they can transform datasets containing highly sensitive information into privacy-preserving records that they can easily share with other researchers or corporate partners. However, there have been numerous cases of organizations releasing datasets…
References
Fung, B. C., Wang, K., Fu, A. W.-C., & Philip, S. Y. (2010). Introduction to privacy-preserving data publishing: Concepts and techniques. Boca Raton, FL: CRC Press.
Kohlmayer, F., Prasser, F., & Kuhn, K. A. (2015). The cost of quality: Implementing generalization and suppression for anonymizing biomedical data with minimal information loss. Journal of biomedical informatics, 58, 37-48.
Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 557-570.
SAP Tools
In any data mining and evaluation operation, there are a number of tools that can be used. The most important part of the tool set is to be able to have access to current data, and to be able to utilize that data in a manner that is conducive to the business model. In fact, this is even truer now that mobile technology has advanced to the amount of memory it has, enterprise offerings are more robust, and more and more users are developing needs that require tremendous amounts of information. In fact, "increasing possibilities that come from deploying mobile solutions in the workplace are now counter balanced by an exponentially complex ecosystem of options that can make navigating the corporate " landscape challenging (Looking Beyond, 2011). Executives must have solutions that are cost-effective, yet powerful enough to make the time and investment worthwhile.
Certainly, within the telecommunications…
REFERENCES
CEZ -- Driving Synergies in Unique Transformation Projects. (2010). SAP Industries.
Retrieved from: http://www.sap.com/solutions/index.epx
Looking Beyond Mobile Device Management to Mobile application and Enterprise Mobility
Management. (August 2011). The Enterprise Mobility Foundation. Retrieved from:
Loyalty Programs
Popularity of loyalty programs is increasing, both in terms of the number of programs offered by merchants and in participation by customers. Yet, it seems that both groups remain ill informed of how the programs actually work and the true benefits and costs. This paper explores the reality of loyalty programs and concludes that they can be beneficial for all parties provided that they fully understand what the programs accomplish.
Do Loyalty Programs Work?
Research suggests that the demand-side success of loyalty programs is less than promised. It is difficult to change established patterns of repeat-purchase behavior and competition quickly develops counter responses that mitigate the impact of the program. However, it is possible to reap advantages from loyalty programs such as maintaining customer loyalty and brand share, improving accessibility and brand awareness, and offering incentives expected by customers.
Customers are apt to join the programs of the…
Uncles, Mark D., Dowling, Grahame R., and Hammond, Kathy, "Customer Loyalty and Customer Loyalty Programs." University of New South Wales School of Marketing Working Paper 98/6, University of New South Wales Web site. 27 Nov. 2004.
Beal, Barney. "Getting Loyalty Programs Right." CRM News 14 Jul 2004. TechTarget. 27 Nov. 2004. .
Stebbins. Kathleen., "Club Cards Actually May Cost You Money." Reno Gazette-Journal 4 Aug. 2003. RGJ.com. 27 Nov. 2004. .
Data Warehousing: A Strategic Weapon of an Organization.
Within Chapter One, an introduction to the study will be provided. Initially, the overall aims of the research proposal will be discussed. This will be followed by a presentation of the overall objectives of the study will be delineated. After this, the significance of the research will be discussed, including a justification and rationale for the investigation.
The aims of the study are to further establish the degree to which data warehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In a recent report in the Harvard Business eview (2003), it was suggested that companies faced with the harsh realities of the current economy want to have a better sense of how they are performing. With growing volumes of data available and increased efforts to transform that data into meaningful knowledge…
References
Agosta, L. (2003). Ask the Expert. Harvard Business Review, 81(6), 1.
Database: Business Source Premier.
Babcock, Charles (1995). Slice, dice & deliver. Computerworld, 29, 46, 129 -132.
Beitler, S.S., & Lean, R. (1997). Sears' EPIC Transformation: Converting from Mainframe Legacy Systems to Online Analytical Processing (OLAP). Journal of Data Warehousing (2:2), 5-16.
Data mining is one of the ways that a company can check into this consideration, but it is far from the only way. Companies that over-utilize it as a means to solve all of their problems are usually the companies that fail or struggle because they start making bad business choices.
These can be corrected but it takes time, and companies that do not learn what they did wrong will often continue data mining in an effort to discover where they went wrong. This can compound the problem and allow data miners to truly overwhelm the organization with facts, figures, calculations, and ideas for change. Some of these will certainly be good ideas, but they will get lost in the shuffle, and if a business implements one good idea and three bad ideas, that good idea will probably not be recognized because sales will continue to fall. Companies that are…
Measures
Targets
nitiatives
Profitable Growth
Return on nvested Capital
Return on Equity
Only accept strong NPV projects
15% ROC
20% ROE
Simplify the organization structure
Provide an open environment for idea generation and brainstorming
ndustry leading innovation
Update product upgrade cycle. Refresh or introduce a product at least once every two years.
Highest Quality products and services
Higher Gross Margin
nvest heavily in R and D with excess Free Cash Flow
Establish strong customer and brand loyalty
Adopt the net promoter score and customer satisfaction rating survey
4% Market Share growth per year
Rewards programs and partnerships with other service providers
Establish a well recognized brand
Strong brand recognition
Become the number 1 or number 2 rated brand in each product category
nvests heavily in marketing and advertising
Question 2
A major retailer such as Wal-Mart would be best served by using a transactional database. Wal-Mart unlike many other retailers…
I would use linear regression as it allows a practitioner to see clusters of date scattered around a particular area. Although many problems can persist with linear regression, I believe it provides the best means of explaining the overall relationship between loans and default risk. The practitioner must first eliminate non-stationary variables in addition to co dependence. Variable that depend on the proceeding variable can cause problems and errors in the overall regression analysis. However, solutions such as use of the adjusted R squared metric, the Dickey-Fuller test and others can help eliminate these concerns. Regressions, through the use of the R squared metric can help an analyst better determine what percentage of the loan defaults can be explained by variables such as income, debt, or other variables. Regressions are also flexible allowing for multiple variables to be used in an explanatory fashion.
The data mining items I would need to conduct a regression analysis are varied. For example, I would need variables relating to debt levels on and individual basis. I would also need income, education, and demographic information. For example, homes in the New York will be more expensive than homes in North Dakota. As a result, a loan will be much higher in New York. With the higher loan amount, the possibility of default and capital loss is also higher. With a higher default risk, the bank will demand higher collateral, more money down, etc. The bank must be sure that the collateral backing the loan is appropriately priced given the market conditions that are prevailing and will prevail in the future.
In 2008, regression analysis failed at financial institutions because they failed to see or account for "tail risk." These risks are those that are three standard deviations away from the mean. Their regressions didn't take these occurrences into account because they were very rare, or had never happened before. By omitting these risks from the regression analysis, the outputs were in error. In particularly, a wave of massive loan defaults occurred that nearly crippled the United States financial system. Due to these occurrences, regressions must take into all the variables, no matter how farfetched or rare they may be.
DBMS and Data Warehouses
(1) in this writing assignment, you will create a brochure advertising your services as a data repository.
Powered By Excellence
Data epository Service
Powered By Excellence is the only data repository service with globally-located data centers across each continent, each with specific security, reliability and fault redundancy systems in place.
Our staff includes world-class experts on the following platforms: IBM, Microsoft, Oracle, MySQL, Informix, Sybase, Teradata and SAS expertise in-house as part of our consulting services division.
Services Offered
Analytics Advisory Services
Big Data Consultancy - Map and Hadoop expertise for gaining insights from very large datasets)
Custom Software Development
Database Hosting
SaaS Application Support
Scalable File Storage
Private Cloud Hosting (Dedicated storage and unlimited virtual machines)
Customer Benefits
High performance with a world-class platform
24/7 Administrator Access
Unlimited Virtual Machine Use
Service Level Agreement (SLA) metrics available 24/7
Trusted Provider of Data epository Services:
ISO…
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.
SUFING & MINING THE WAVE OF BIG DATA
Management
What used to consider a simple annoyance or frustration has now become a respected field of inquiry. Consumer information has transformed into Big Data. As with many things that are vast, Big Data has the potential to intimidate. It is now the professional responsibility of people working in many fields to be aware of Big Data, and be able to use it to their respective organization's advantage. Navigating through and understanding what Big Data is a formidable challenge in of itself, yet not impossible. Effective management of the 21st century cannot fear or be overwhelmed by Big Data; managers must learn how to use Big Data like any other tool within their professional arsenal to maintain the status quo and even ahead or make establish new trends in business.
Big Data is in simple terms, mass quantities of data that are…
References:
Byrne, N. (2011) Mining Big Data for Meaning. Technology_IRELAND, 4(42), 28 -- 31.
Chong, R.F. (2012) Starting your education in big data. IBM developerWorks, BigDataUniversity, Available from: http://www.ibm.com/developerWorks . 2012 July 13.
Forsyth Communications. (2012) For Big Data Analytics There's No Such Thing as Too Big: The Compelling Economics and Technology of Big Data Computing. Available from: http://www.4sythcomm.com. 2012 July 11.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A.H. (2011) Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, McKinsey & Company: New York.
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 to provide customer meaningful information for business decision and enhance competitive market advantages. ecent growth of our company necessitates the needs to design and develop data warehouse that will accommodate large volume of customer data.
Objective of this project is to design and develop the data warehouse for our company.
Importance of Data Warehousing for our Organization
Comprehensive portfolios of our business include Business, Market, and Financial research, Data processing services and Domain based analytics. While the relational database…
Reference
Hillard, R. (2010). Information-Driven Business. UK. Wiley.
Microsoft (2012).Data Warehousing | Microsoft SQL Server 2012. Microsoft Corp.
Patil, P.S., Srikantha, R., Suryakant, B.P. (2011). Simplification in the Reporting and Analysis Optimization of the Data Warehousing System:, Foundation of Computer Science, 9 (6): 33 -- 37.
Rostek, K. (2010). Data Analytic Processing in Data Warehouses. Foundations of Management, 2(1), (2010), 99-116.
Data Mining Habits
There are several advantages to the type of customer data collection process that Hill (2012) indicated Target is employing on its customers. One is that it enables the company to due highly specific, targeted marketing efforts. Thus, the company can become more effective in marketing products and services to customers. Also, customers can derive benefit from this sort of analysis. They can receive coupons and promotional offers that are tailored to their individual shopping habits and needs. As such, they can purchase more at Target for less money, while sating their shopping and lifestyles needs in the process.
However, there are both legal and ethical ramifications of the sort of data mining and analytics that Target is utilizing on its customers. These predominantly pertain to privacy. In the use case Hill (2015) references, the store was able to glean that a young woman was pregnant before her…
References
Atahan, P., Sarkar, S. (2011) Accelerated learning of user profiles. Management Science. 57(2), 215-239.
Hill, K. (2012). How Target figured out a teen girl was pregnant before her father did. www.forbes.com Retrieved from http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ #328d768134c6
H Data
MY Organization (AAB BANK PLC ) a H Director. They requested review organization's approach collecting, storing H data produce a summary position statement
Collection and storage of H data
easons why an organization needs to collect H data
H data is collected for strategy purposes. The organization's management needs to know the qualifications of its current workforce to enable them make better strategies, and if need be they hire qualified employees who can deliver on the organizations goals and plans. This data will also ensure that the organization has the right people to handle the jobs assigned. The H department will need to capture this data as a requirement from the Government. In particular large companies are required to collect and store employee information that should be provided upon request by law. In case of liabilities, the data collected can be used in defense of the company against…
References
Agmon, N., & Ahituv, N. (1987). Assessing Data Reliability in an Information System. Journal of Management Information Systems, 4(2), 34-44.
Hochachka, W.M., Caruana, R., Fink, D., Munson, A., Riedewald, M., Sorokina, D., & Kelling, S. (2007). Data-Mining Discovery of Pattern and Process in Ecological Systems. The Journal of Wildlife Management, 71(7), 2427-2437.
Software
Qualitative data is characterized by the deep, rich aspects that enable researchers to enter the realm of the participants in a study. Qualitative research projects are characterized by considerable coordination challenges and tight deadlines. Business clients of market research providers and academic research colleagues anticipate that the value qualitative researchers bring to inquiry is the ability to analyze and interpret, providing insights or contributions to themes. But often these processes are given short shrift with regard to time allotments in the overall inquiry process.
Challenges of Qualitative Data Analysis (QDA)
Data analysis software is a strong tool for textual analysis, and the benefits fall primarily into three categories: (1) Efficient systematic analysis, (2) effective retrieval and identification of data, and (3) capacity. Data analysis software is a grounded in machine learning -- algorithms and mathematical approaches to textual analysis that are interpretation neutral. That is, either patterns exist in…
NVivo: NVivo supports the cognitive activities associated with qualitative research and the productivity capabilities for managing large amounts of data are strong. The students-only license is $189. The full NVivo 9 license is $650 for 1 computer installation with indefinite use.
Source: NVivo. http://www.qsrinternational.com/products_nvivo_pricing_pricelist.aspx
Atlas.ti: Use Atlas.ti collect, manage, analyze, and share both primary and secondary qualitative data. The learning curve is short, operations are intuitive, and it has embedded survey and transcription components. The cost is $99 for a student license and $1,800 for a regular single user license. Source: Atlas.ti. http://www.atlasti.co
Digital Forensics to Capture Data ources
Network Intrusion
Prioritizing Data ources
Account Auditing
Live ystem Data
Intrusion Detection ystem
Event Log Analysis
Malware Installation
Prioritizing data sources
Activity Monitoring
Integrity Checking
Data Mining
Insider File Deletion
Prioritizing data sources
Use of Uneraser program Recovers the Deleted Data
Network torage
A recent advance in information technology has brought about both benefits and threats to business organizations. While businesses have been able to achieve competitive market advantages through the internet technology, the hackers are also using the opportunities to penetrate the organizational network systems to steal sensitive data worth billions of dollars. A recent wave of cybercrimes leads to the growth of forensic investigation dealing with a collection of evidence to track cyber offenders. The study investigates different data sources that can assist in enhancing digital forensic investigation. The study identifies event log analysis, port scanning, account auditing, and intrusion detection system…
Stallings, W. (2011). Cryptography and Network Security Principles and Practice (Fifth Edition). Pearson Education, Inc. Prentice Hall.
Vigina, G. Johnson, E. Kruegel, C. (2003). Recent Advances in Intrusion Detection: 6th International 6th International Symposium, RAID 2003, Pittsburgh, PA, USA, September 8-10, 2003, Proceedings, Volume 6. Springer Science & Business Media.
Xu, M., Yang, X. Wu, B. et al. (2013).A metadata-based method for recovering files and file traces from YAFFS2. Digital Investigation. 10 (1); 62-72.
Miller Inc. is a company that wishes to develop a new and more efficicent data repository for all data collected, stored, and transferred. Their desire to create a data warehouse that operates quickly with less effort is the purpose of this project. Adaptation of database modeling along with designing their data warehouse will lead to higher consumer and employee satisfaction. The project goal is to create a database schema to work as well be designed alongside other components such as identifying metadata in order to let IT model the data warehouse, implement and test it.
to identify and gather database requirements, design the dimensional model, develop the system architecture, design the relational database and online transactional processing model, develop the data maintenance application, develop analysis applications to test and deploy the system through a series of steps intended to reduce error rate. The types of applications for use will be…
healthcare model that could enable physicians determine their patients' susceptibility to future disease on the bases of their medical records, and most importantly, their similarity to other patients. They acknowledge that despite numerous studies indicating a shift from the traditional disease-based to the more effective patient-centered approach of healthcare delivery, there still exists a knowledge gap, particularly because of the lack of a computational tool that can effectively discover patients' disease patterns without "falling prey to the noise" (Chawla & Davis, 2013, p. 660). The authors put forth the CAE model, which they posit addresses these concerns better than the existing models. They base their development on a number of findings from exiting literature.
The CAE model establishes risk factors by leveraging a patient's symptoms and traits with their interactions and biological disease information. To this end, its operation rides on the findings of a 2009 study by Schadt, which…
References
Anderson, J.D. (2006). Qualitative and Quantitative Research. Imperial COE. Retrieved 6 June 2014 from http://www.icoe.org/webfm_send/1936
Chawla, N.V. & Davis, D.A. (2013). Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework. JGIM, 28(3), 660-665.
component graded.
The amassing of data has become an integral process of life in the 21st century (Nunan and Di Domenico, 2013, p. 2). This fact is partially reflected by the fact that in contemporary times, people are generating much more data than they previously did. Every time someone goes shopping and makes a purchase with a credit card, receives a call or sends a text message, or visits a web site on a computer or downloads information to a mobile phone application, they are generating data. This data is stored and, through sophisticated processes of analytics that involve data mining and even predictive capabilities, is analyzed to determine aspects of consumer, individual, and collective behavior. The generation of these massive quantities of data in the myriad forms such data takes at the rapidity of real-time access is known as big data, which government representatives claim they are analyzing to…
References
Byman, D., Wittes, B. (2014). Reforming the NSA. Foreign Affairs. 93(3), 127-138.
This source considers a number of possibilities for reforming the NSA which are viable in the wake of Snowden's security breach. It examines other country's approaches to espionage as well. In provides an in-depth read into the considerations the NSA must make for securing the country
Hackett, K. (2013). Edward Snowden: the new brand of whistle blower. Quill. 101(5), 26-31. This source examines the ramifications of the actions of whistle blowers, and attempts to deconstruct the privacy issues associated with security concerns in the U.S. It details the actions of Snowden.
Nuna, D., Di Domenica, M. (2013). Market research and the ethics of big data. International Journal of Market Research. 55(4), 2-13. This source considers the practice of amassing and analyzing big data largely from a marketing research perspective. It details the wide scope of data that is regularly stored and scrutinized regarding the lives of citizens.
Data Mining
Predictive analytics help companies to understand the behavior of consumers. The way that predictive analytics works is that data from the past is used to help refine predictions about the future (CGI, 2013). Companies basically analyzed demand in terms of a wide range of variables in order to arrive at a better estimate for future outcomes than otherwise would have been found. It is basically the same principle as predicting that a colder, snowier winter will help Wal-Mart sell more snowblowers, but with hard data, sophisticated algorithms and reliable outputs -- such as x number of snow days will equal y number of snow blowers sold.
One of the interesting elements of predictive analytics is with associations, and this has been used fairly extensively in retail. Associations discovery is where correlations between things are noted that might not have been apparent. So that link between snow blowers and…
References
CGI. (2013). Predictive analytics. CGI Retrieved June 2, 2014 from http://www.cgi.com/sites/default/files/white-papers/Predictive-analytics-white-paper.pdf
Jain, A., Murty, M. & Flynn, P. (1999). Data clustering: A review. ACM Computing Systems. Vol. 31 (3) 264-323.
Nearing, B. (2013). Mining Internet for chunks of gold. Times Union. Retrieved June 2, 2014 from http://www.timesunion.com/business/article/Mining-Internet-for-chunks-of-gold-5056469.php
Olavsrud, T. (2014). CIOs should push big data projects but prioritize privacy. CIO Magazine. Retrieved June 2, 2014 from http://www.cio.com/article/753612/CIOs_Should_Push_Big_Data_Projects_but_Prioritize_Privacy
Summary
In the period between 2002 and 2012, Australia experienced a mining boom; a period in which the level of exports increased more than threefold and also the investment made in mining as a percentage of the nation’s GDP increasing from 2 percent to 8 percent. Imperatively, during the mining boom period, there was a significant increase in demand for minerals. This is because of the demand for minerals not only locally but also internationally. Therefore, this caused a rightward shift in the demand curve. This leads to the positioning of a new equilibrium price. The comparative theory best explains the exportation of minerals by Australia and the importation of other commodities from other nations. In this regard, Australia is considered to have a comparative advantage in the production of minerals because it can produce minerals at a relatively lower opportunity cost compared to China. Another aspect that was influenced…
Future of Data Storage in Computer Networks
There are a number of problems facing the future of information technology including the fact that networks are increasingly asked to expand in order to accommodate more and more data. Many experts believe that such increases will mean two things; one that the networks will become increasingly secure, and two because of the security, the data contained on the network will become more difficult to access. This study sought to determine the various processes that are currently being used to secure data on various networks, and to determine if that security will, or will not, ensure that data will become incrementally more difficult to obtain. To this end, this study used the most current literature available to determine if there is a problem with the data being stored in the current manner, or if there is a perception that the data will be…
References
Axellson, A-S. & Schroeder, R. (2009). Making it open and keeping it safe: E-enabled data-sharing in Sweden. Acta Sociologica, 52(3), 213-226.
Datt, S. (2011, Winter). The information explosion: Trends in technology review. The Journal of Government Financial Management, 60(4), 46-54.
Folk, M. & Barkstrom, B. (2003). Attributes of file formats for long-term preservation of scientific and engineering data in digital libraries. Paper presented at the Joint Conference
on Digital Libraries, Houston, TX, May 27-31.
ig data: What does it mean for your business?
Once data about consumers was relatively difficult to amass. Now, in the digital age businesses are assaulted with a plethora of sources of consumer data. "Data now stream from daily life: from phones and credit cards and televisions and computers; from the infrastructure of cities; from sensor-equipped buildings, trains, buses, planes, bridges, and factories. The data flow so fast that the total accumulation of the past two years -- a zettabyte -- dwarfs the prior record of human civilization" (Shaw 2014). The big data revolution has the power to be as revolutionary as the Internet in the ways that businesses conduct commerce and consumers view themselves. "ig data is distinct from the Internet, although the Web makes it much easier to collect and share data. ig data is about more than just communication: the idea is that we can learn from…
Bibliography
Corbin, K. 2014. CIOs must balance cloud security and customer service. CIO Magazine.
Available at: http://www.cio.com/article/2379776/government/cios-must-balance-cloud-security-and-customer-service.html [2 Nov 2014]
Cukier, K.N. & Schoenberger, V. 2013. The rise of Big Data. Foreign Affairs. Available at:
http://www.foreignaffairs.com/articles/139104/kenneth-neil-cukier-and-viktor-mayer-schoenberger/the-rise-of-big-data [2 Nov 2014]
This solution is then subjected to a process called solvent extraction (SX). The SX process concentrates and purifies the copper leach solution so the copper can be recovered at a high electrical current efficiency by the electrowinning cells. This is accomplished by adding a chemical reagent to the SX tanks, which selectively binds with and extracts the copper. This reagent is easily separated from the copper (stripped), as the operation looks to recover as much of the reagent as possible for re-use. The concentrated copper solution is dissolved in sulfuric acid and then sent to the electrolytic cells for recovery as copper plates (cathodes).
The Morenci operation has quite a large crusher, which can output at maximum approximately 63,000 tons of ore per day. This crusher pulverizes the ore and more conveyors send it to a nearby stump-leaching site called the Stargo leaching pad. This ore is agglomerated and made…
Works Cited
Groundwater Awareness League Homepage. (2007). "Phelps Corporation Environmental
Liabilities." Accessed via web: < http://www.g-a-l.info/ComplaintOne.htm > on August 9th, 2010.
Mine Engineer Homepage. (2010). "Copper Mining Information." Accessed via web: <
http://www.mine-engineer.com/mining/copperm.htm > on August 8th, 2010.
In this Facebook data breach essay, we discuss how Facebook allowed applications to mine user data. The essay will explain what data was breached, how it was breached, and how that data was used. Furthermore, the essay will also discuss the repercussions of the breach, including Facebook founder Mark Zuckerberg’s hearing in front of the United States Senate, issues involving Cambridge Analytical, and information that is being revealed about additional data breaches.
In addition to explaining the data breach, the essay will also discuss whether Facebook has a responsibility to users to keep data safe, and the steps that Facebook is taking to resolve data breaches in the future. This example essay should not only provide you with an overview of the Facebook data breach, but also provide you with a technical guide on how to write an academic essay. It will include the following parts of a standard academic…
relationships and distinctions between the information systems concepts of data warehousing and data mining, which combined with online analytical processing (OLAP) form the backbone of decision support capability in the database industry. Decision support applications impose different demands for OLAP database technology than the online transaction processing (OLTP) model that preceded it. Data mining with OLAP differs from OLTP queries in the use of multidimensional data models, different data query and analysis tools at both the user-facing front end and the database back end, and different mechanisms for data extraction and preparation before loading into a data warehouse can take place. The construction of data warehouses entails the operations of data cleaning and data integration, which are key pre-processing steps for enabling data mining. Furthermore, the concept of metadata (data about data) is essential to the functioning of a data warehouse, and must be managed appropriately for an effective and…
References
Berson, A., & Smith, S.J. (1997). Data Warehousing, Data Mining, and Olap (1st ed.). McGraw-Hill. Retrieved from http://dl.acm.org/citation.cfm?id=549950
Chaudhuri, S., & Dayal, Umeshwar. (1997). An overview of data warehousing and OLAP technology. ACM Digital Library, 26(1). doi:10.1145/248603.248616
Douq, Q. (2009, November 24). Comparison of Oracle to IBM DB2 UDB and NCR Teradata for Data Warehousing. X-Space. Retrieved November 14, 2011, from http://space.itpub.net/673608/viewspace-620367
Gartner Newsroom. (2008). Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond. Retrieved November 14, 2011, from http://www.gartner.com/it/page.jsp?id=856714
Conger, 2009).
ecommendations for Organizations
The many factors of data mining and their use for profiling customers and their needs also create opportunities for organizations to build greater levels of trust with their customers as well. And trust is the greatest asset any marketer can have today. The following are a series of recommendations for how organizations can address demographic influences that impact their marketing strategies in light of concerns surrounding the ethics of data mining.
First, it is imperative, across all demographic segments that marketers make a deliberate a very clear effort to explain their opt-in and opt-out policies and also provides evidence that they do what they claim to in this area. The greatest challenge for the consumer is controlling their personal information online and ensuring it is well managed to their preferences (Pratt. Conger, 2009). Marketers who give consumer control over their data in this way will…
References
Adams, N.M. (2010). Perspectives on data mining. International Journal of Market Research, 52(1), 11.
Bose, I., & Chen, X. (2009). Hybrid models using unsupervised clustering for prediction of customer churn. Journal of Organizational Computing and Electronic Commerce, 19(2), 133.
Kaiser, C., & Bodendorf, F. (2012). Mining consumer dialog in online forums. Internet Research, 22(3), 275-297.
Kiron, D. (2012). Why detailed data is as important as big data. MIT Sloan Management Review, 53(4), 1-3.
Data storage for today's businesses [...] why a business has separate databases and a data warehouse, and why it does not have just one large database for all its data, both current and historical. Data warehouses help staff evaluate specific data more effectively, and bring together large amounts of data that might not be easily accessible in one massive database.
Storage of Data
The storage of data for today's businesses is a complicated issue, compounded by the need to access data quickly, while still ensuring the data is easily accessible to the staff that need it, and can be combined to utilize all the data from several single databases that might not be easily mined and evaluated from database to database. Keeping separate databases along with a data warehouse makes good business sense for a number of reasons.
One of the main reasons many businesses utilize data warehousing is to…
Bibliography
Chopoorian, John A., et al. "Mind Your Business by Mining Your Data." SAM Advanced Management Journal 66.2 (2001): 45.
Higgs, Edward, ed. History and Electronic Artefacts. Oxford, England: Clarendon Press, 1998.
O'Sullivan, Orla. "Data warehousing - without the warehouse." ABA Banking Journal 88.12 (1996): 42+.
Turning data into knowledge." Canadian Business June 1997: 2+.
Conclusion
When it comes to strategic information management, there are many options that a company has. Data mining, data warehousing, and the various OLAP options are only some of the tools that can be used, but they are three of the most important - if they are used correctly. Because there are several options with OLAP it is very versatile and most companies can find a way to make it work for them so that they can quickly get the answers that they are looking for and help to keep their company moving forward. The other two business intelligence tools, data mining and data warehousing, are also very important, but like any of the other tools there are contributions that they offer and drawbacks that they have. The key to using them all effectively is getting a good understanding of what they can bring to an organization and what they…
functions of an information system. List and describe three types of enterprise systems.
he four basic functions of an information system are gathering data, storing data, processing that data into information, and outputting the information (O'Leary & O'Leary, 2008). he system has to be able to collect data, or have the data placed into it, or it does not have anything with which it can work and with which it can provide output information after an analysis takes place. Storing data is a big part of what an information system does, because the data is important and must not be lost. A system that could not store data would not be valuable to a company for collection and retention of data (O'Leary & O'Leary, 2008). Once the data has been collected and stored, it can then be analyzed in order to draw conclusions from it based on the type of…
The four main points of IT strategic plans are the mission statement, the SWOT analysis, the list of actions to be prioritized, and the "road maps" that are used to examine and readjust the strategic plan in the future (Bradford & Duncan, 2000). The mission statement is a very important part of the plan, because it is the basic definition of what the company stands for and where it is headed in the future. Without it, IT cannot plan for continued structure and development, which can cause the company to stagnate (Bradford & Duncan, 2000). The SWOT analysis comes next, and addresses the strengths, weaknesses, opportunities, and threats that are being faced by the IT department of the company. These can include both internal and external issues, both of which have to be dealt with correctly in order to allow the company to continue to see success (Bradford & Duncan, 2000). Because IT is such an important part of companies today, what happens in that department affects nearly everything else that takes place within the company.
Prioritizing the actions needed is next on the list when it comes to IT strategic planning. There is no need to work on something just for the sake of working, when there are more important issues to be faced (Bradford & Duncan, 2000). Prioritizing everything means that the IT department will be focused on the most important issues first, so that the concerns that really need to be addressed do not languish. Finally, road maps are required so that the company can see where it intends to have its IT department at specific intervals in the future. These are usually at the one, two, and three-year marks, but they can be placed at other intervals, as well (Bradford & Duncan, 2000). There is no specific rule for when they need to be seen, and every company is different.
Bradford, R.W. & Duncan, J.P. (2000). Simplified strategic planning. NY: Chandler House.
Impediments to Integration of Business Analytics With Business Intelligence and Knowledge Management Data
Business Analytics
Impediments to Integration of Business Analytics
The consumer is out to emphasize that 'he is the king'. Accordingly, his demands for enhanced quality and timely deliveries of the products' order by them has revolutionized the management gurus to evolve new business strategies to enhance the intensity of coordination and communication between the seller and the consumer. Two key business strategies that have evolved from this exercise are business intelligence and knowledge management, which have contributed effectively to the progression of new and competent management strategies. Great benefits have also been achieved in for form of cost reduction, thereby increasing the sales and profitability of the organization.
Information routing is a critical link between the production department up to the client and the optimal course of production steps are directly related to business intelligence and knowledge…
Reference
Kathe, H. (2008). Research Paper Article: Business Intelligence and Knowledge Management, Retrieved from http://www.mightystudents.com/essay/Business.Intelligence.Knowledge.51740, 2008-11-21.
H.L. Lee and C. Billington, "Evolution of Supply Chain Management Models and Practice at Hewlett-Packard Company," Interfaces, 25, 5, 1995, 42-63.
Ron, K; Neal, J.R.;Evangelos, S. (2002). Emerging Trends in Business Analytics, Retrieved from ai.stanford.edu/~ronnyk/cacmEmergingTrendsInBI.pdf, Volume 45, Number 8, Aug 2002, and pages 45-48.
Ron, K. & Foster, P. (2001), Applications Of Data Mining To Electronic Commerce: Data Mining And Knowledge Discovery, 5(1/2), 2001, Retrieved from http://robotics.Stanford.EDU/users/ronnyk/ecommerce-dm .
high quality (error-Free) data entry.
Data entry is a vital part of a number of businesses. As such, it has to be of the highest quality and error-free. While there is generally no guarantee that data can be 100% error-free all the time, the more any data avoids errors the more likely the company will be to provide its customer with a good experience (Kos, Kosar, & Mernik, 2012). Additionally, companies that have data errors can end up in trouble because they are using the wrong details to make decisions about what they can and should be doing to move their business forward. If a company is not focused on entering data in a way that is free from errors (and omissions), that company can get a reputation as being sloppy and not well-prepared to handle its business and its customers (Kos, Kosar, & Mernik, 2012). Consumers who provide data…
Enterprise Resource Planning, or ERP, is designed to manage a business through every stage it reaches and moves through. It is generally presented as a suite of applications that are all integrated with one another, and that provide real-time information on the processes that are at the core of the business (Loh & Koh, 2004). Originally, ERP was designed to be something that was for back-office use only (Loh & Koh, 2004). It helped to keep a business running, but it was not for the customers to see. It also did not provide any kind of interactive tools that could be used with customers. However, over time that changed and customer relationship management (CRM) and other functions were integrated into ERP (Loh & Koh, 2004). That made a significant difference in how companies did business, especially online, because customers could reach these companies so much more easily than they were able to do in the past. That advanced business-related technology rapidly.
The main objectives of ERP are to help a company operate more smoothly and interact with its customers in the best way possible. These software suites store information, help a company plan and implement multiple considerations, allow for customer feedback, track the success of ideas and plans that the company has provided to the public, and make sure customers are getting what they asked for from a company (Loh & Koh, 2004). As such, ERP software needs to be upgraded from time to time, in order to ensure it is still providing the company and the customers with the best experience possible (Loh & Koh, 2004). If that is found not to be the case, it becomes time to make changes in order to continue advancing the company and moving it forward, as well as keeping customers happy with their interactions.
Loh, T.C., & Koh, S.C.L. (2004). Critical elements for a successful ERP implementation in SMEs. International Journal of Production Research, 42(17): 3433 -- 3455.
trouble with Philadelphia's water billing system is a technical problem or a people problem? Why?
From the case study it is at first difficult to separate the two and decide whether the issue rests mainly with the people involved or the system. The people who developed the system did not anticipate using it for such a broad application, so they are responsible for not preparing the system for such an application before they tried to use it. The system itself cannot handle the complex load required of a water billing system to the 500,000 customers that reside in Philadelphia. In the end, it seems more likely that this was "operator error" more than something that was wrong with the system itself. This will become apparent in the following discussion.
The first group of people who made an error were the city officials who did not conduct an adequate amount of…
NSA/FISA
Following the terrorist attacks of September 11, 2001 there has been a significant effort to protect America from any further terrorist attacks. The purpose of this discussion is to examine the U.S. National Security Agency's ability to identify and monitor the communications of terrorists and prevent terrorism from occuring. The research will also investigate how the implications of employing these techniques for foreign intelligence surveillance suggests that the Foreign Intelligence Surveillance Act ("FISA") is inadequate in addressing recent technological developments. These developments include the transition from circuit-based to packet-based communications; the globalization of communications infrastructure; and the development of automated monitoring techniques, including data mining and traffic analysis. The research will also focus on how FISA is challenged by technological developments.
The Monitoring of Communications
The National Security agency was created to "protect U.S. national security systems and to produce foreign signals intelligence information." The strategic plan of the…
Works Cited
Bill to Amend FISA. (2007) The United States Select Committee on Intelligence. Retrieved March 4, 2009 from; http://intelligence.senate.gov/071019/fisa.pdf
Feingold, R. (2008) Remarks of U.S. Senator Russ Feingold
Opposing H.R. 6304, FISA Amendments Act of 2008. Retrieved March 4, 2009 from;
http://feingold.senate.gov/statements/08/06/20080625f.htm
Portability
M
In the event that Myra decides to expand her business, portability becomes more important. However in this situation portability is not as important due to the single location and the access to cloud technology.
Security
L
There is not much security risk in this approach to the problem. Beautician scheduling is not regarded as a high risk activity.
Data Quality
L
Names and time are all that are really needed in this software. As long as that quality is fine, there are no problems with this area.
Authentication
L
Once again the lack of a need for high security denotes the lack of importance of this area. Authentication is not that important since the scheduling software is more like a common good to be used by all.
Encryption
L
Only basic encryption is needed in a software application such as this. There is no reasonable excuse for any…
In modern terminology, and for foreign policy, political science and international law, crimes against humanity are any atrocious act committed on a large scale. They can be prosecuted in most any Federal Court ystem, depending on where they occurred and which population was part of the criminal activity. The implication for international law is that crimes against humanity are subject to universal jurisdiction, which means that tates can exercise their own jurisdiction regardless of where the crime was committed, and that all tates also have the obligation and duty to assist each other in the defense of these sorts of activities. It is also important to note that no human, regardless of affiliation, is immune from prosecution, even heads of state, and on person can plead a defense as obeying orders.
Part 3 -- Is it legally justified to invade/occupy another country in the name of arresting/hitting terrorists? International cooperation…
Sources:
Dyson, W. (2012). Terrorism: An Investigator's Handbook. Waltham, MA: Anderson
Publishing.
McCormack, W. (2007). Understanding the Law of Terrorism. New York: Lexis Nexis.
Title of agency, not plagiarized
This makes it easier for investigators to identify connections by clicking on a particular item in the three-dimensional link.
The difficulties of this process of proving such a chain indicates the importance of creating steps that can help companies simplify the task of conducting a computer forensic investigation, should one ever be required. The article stresses that the most important step is to ensure that network logging devices are turned on, even though these devices use disk space and processor time. If they are turned off, investigations can become impossible. Closing any unneeded ports on the company firewall and patching systems regularly, are also helpful.
This article paints an overall benign portrait of law enforcement, zealously protecting user privacy and safety. It demonstrates how an apparently invisible crime can be rendered visible through the use of technology, and both the law and law enforcement's attempts to stay one step ahead…
Works Cited
Burke, Dan. "Transborder Intellectual Property Issues on the Electronic Frontier." Volume 5. Stanford Law & Policy Review
http://www.ifla.org/documents/infopol/copyright/dburk2.txt
Lang, David. "A Graphic Picture of Crime." ASIS. Sept 2002.
Lastly differentiating on the extent of experience our customers have had with Internet-based software is useful in defining how much extra time is necessary for software application training.
Discuss your efforts to create customer intimacy.
As our company relies intensively on long-term relationships with customers, taking a very active approach to creating customer intimacy is critical to our business. Our approach is to first concentrate on total accountability for our software by having our CEO visit each and every customer just after an installation to show a high degree of support and accountability. Next, we offer each customer the opportunity to join a customer advisory council specific to their industry and special interests. At present there are three customer advisory councils which give customers an opportunity to discuss their concerns, interested in new product ideas and see what is presently in development.
How will you customize your offering for particular…
Build an expectation model through the focus groups that will be used for Step 2 (below).
Using data from the focus groups, further hone the CVM approach to a dual qualitative and quantitative approach to very select clients (database will be purchased); send out survey with goal of at least 10% returns. Provide small incentive (cash, coupon or gift card approach to encourage participation).
Analyze data, use data to rehone the value relationship that these clients want, encouraging feedback for future strategic decisions (Farace, 2007).
Conclusions - Value means many things to many people at all income and demographic / psychographic levels. Certainly, no one wants to pay more than they have to for a particular project. However, certain items flow well into good value, CarnivorTrue, being ideal. If we use a CVM value table for the product, we find that Watts' ideas are valid and with research focus, appropriate…
REFERENCES
Luxury Brands: Marketing the Upscale During a Downturn. (November 12, 2008). Knowledge at Wharton Marketing. Retrieved from: http://knowledge.wharton.upenn.edu/article.cfm?articleid=2091
Caldwell, L. (a) (September 9, 2008). Client Value Management, Customer Value Management, and What is Value? Knowing and Making. Retrieved from: http://www.knowingandmaking.com/2008/09/client-value-management-customer-value.html
____. (b) (August 9, 2008). Business: Is CVM the new CRM? Eacademy. Retrieved from: http://www.ecademy.com/node.php?id=109905
Farace, V. (2007). Measuring and Managing Customer Value in the Marketplace. Satmansys.com. Retrieved from: http://www.satmansys.com/downloads/Measuring%20and%20Managing%20Value%20in%20the%20Marketplace%20v2.pdf
Accordingly, Browder notes that "the discipline of public administration has little sense of its historical circumstances and constantly re-issues 'new' calls for science and rigour. Instead, we must focus more research on critical, historically-based studies." (p. 1) Browder argues that the insertion of administrative evil into such discussions provides just such a basis for consideration.
Key Scholars:
The key scholars of importance in this discussion are Adams & Balfour, whose 1998 text Unmasking Administrative Evil is identified as the seminal work on the subject by Dubnick & Justice. Indeed, Adams & Balfour have continued to examine these issues, resolving as recently as 2007 that "the ethical framework within a technical rational system thus posits the primacy of an abstract, utility-maximizing individual, while binding leaders and professionals to organizations in ways that make them into reliable conduits for the dictates of legitimate authority, which is no less legitimate when it happens…
Boss I think someone stole our customers
Flayton Electronics Case Study
Brett Flayton, CEO of Flayton Electronics, is facing the most critical crisis of his career when it is discovered that 1,500 of 10,000 transactions have been compromised through an unprotected wireless link in the real-time inventory management system. Brett has to evaluate his obligation to let customers know of the massive leak of private data, define a communication strategy that would notify customers across all states of the potential security breach, and also evaluate the extent to which the Flayton Electronics' brand has been damaged in the security breach. In addition, steps that the company can take in the future to avert such a massive loss of customer data also needs to be defined and implemented.
Assessing the Obligations to Customers vs. Keeping It Quiet
Ethically, Brett Flayton has a responsibility to tell the customers immediately of the security…
References
Aldhizer, George R., I.,II, & Bowles, John R.,,Jr. (2011). Mitigating the growing threat to sensitive data: 21st century mobile devices. The CPA Journal, 81(5), 58-63.
Gatzlaff, K.M., & McCullough, K.A. (2010). The effect of data breaches on shareholder wealth. Risk Management and Insurance Review, 13(1), 61-83.
Gregory, A. (2008). Conserving customer value: Improving data security measures in business. Journal of Database Marketing & Customer Strategy Management, 15(4), 233-238.
Kelly, C. (2005). Data security: A new concern for PR practitioners. Public Relations Quarterly, 50(2), 25-26.
Moreover, EBSCO, U.S. National Library of Medicine National Institutes of Health, PubMed, and Sage Publication databases also contain thousands of research articles on the TEDs, DVT, Pulmonary Embolism, Anti-Embolism Stockings and the safe use of TEDs within the clinical units.
To identify the articles and research papers relevant to the study, the paper uses the keywords to search for data from the database and the keywords include:
Deep Vein Thrombosis (DVT )
TEDs (thromboembolic disease stockings)
PE (Pulmonary Embolism).
TEDs Anti-Embolism Stockings
Prevention of DVT,
The goal of using the keywords is to search the articles and research papers relevant to the study. When the author submits the keywords to the database, numerous articles come out from the database and the study only selects the articles that relevant to complete this study. Using the relevant search strategies, the author has been able to source for the quality research papers to…
References
Agu, O., Hamilton, G., and Baker, D. (1999). Graduated compression stockings for the prevention of venous thromboembolism. British Journal of Surgery, 86- 992-1004.
Covidien. (2012).T.E.D. ™ Anti-Embolism Stockings. Fastus Library. Covidien Company.USA.
National Health and Medical Research Council (2009). Guideline of Clinical practice for the prevention of venous thromboembolism (deep vein thrombosis and pulmonary embolism) in patients admitted to Australian hospitals. Commonwealth of Australia 2009.
Miller, J.A. (2011). Use and wear of anti-embolism stockings: a clinical audit of surgical patients. International Wound Journal. 8 (1):74-83.
Proposed Ecosystem Hub for Starbucks
Overview
Each day, technologists working at Starbucks, in collaboration with other stakeholders, work on innovations that could only be described as groundbreaking. This is the team responsible for the unique Starbucks experience. However, without this level of dedication and inventiveness, Starbucks technology-centeredness would not have been possible. Thanks to these efforts, Starbucks now boasts of superior customer connection with the enterprise and continues to further promote customer experience. In line with Starbuck’s digital business transformation strategy, there is need for the development of a robust and effective Ecosystem Hub solution comprising of the software tools listed below:
a) Asana (Collaboration Portal)
b) Dropbox (Enterprise Content Management)
c) Board (Platform combining business intelligence tools with predictive analytics, simulation, as well as corporate performance management capabilities)
d) Mailchimp (Digital Content Marketing Management)
In an attempt to underline its commitment to digital business transformation, the company continues to…
References
Adams, R. (2019). Data Analytics for Businesses 2019. Mason, OH: Cengage Learning.
Ansoff, H.I. (2016). Strategic Management. New York, NY: Springer.
Hitt, M.A., Ireland, R.D. & Hoskisson, R.E. (2014). Strategic Management: Concepts, Competitiveness and Globalization (11th ed.). Stamford, CT: Cengage Learning.
Marr, B. (2019). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Hoboken, NJ: John Wiley & Sons.
Rahman, W. (2020). Starbucks Isn’t a Coffee Business — It’s a Data Tech Company. Retrieved from https://marker.medium.com/starbucks-isnt-a-coffee-company-its-a-data-technology-business-ddd9b397d83e
Starbucks (2020). Explore the Possibilities, Drive Innovation. Retrieved from https://www.starbucks.com/careers/technology
Thau, B. (2014). How Big Data Helps Chains Like Starbucks Pick Store Locations -- An (Unsung) Key To Retail Success. Retrieved from https://www.forbes.com/sites/barbarathau/2014/04/24/how-big-data-helps-retailers-like-starbucks-pick-store-locations-an-unsung-key-to-retail-success/#432e7edd16db
Whitten, S. (2017). Starbucks Rewards Members Can Now Earn Even More Stars at the Grocery Store. Retrieved from https://www.cnbc.com/2017/05/04/starbucks-reward-members-can-now-earn-stars-at-the-grocery-store.html
Predictive policing is a trend that uses technology to predict hot crime spots and send police to the area before a crime is committed. By using data mining and crime mapping, police are deployed to areas based on statistical probability and geospatial predictions. This technology is based on the same technology used by businesses to predict sales trends and customer behavior patterns. Now, police departments can use the same technology to predict crime patterns and work to reduce crime in their area.
Predictive policing is putting officers where crimes are more likely to occur. "…it generates projections about which areas and windows of time are at highest risk for future crimes by analyzing and detecting patterns in years of past crime data." (Goode) The data mining generates projections using past crime data to analyze which areas and the time of the day, week, or month, etc. that crime is likely…
Works Cited
CrimeSolutions.gov. (n.d.). Compstate (Fort Worth, Texas). Retrieved from CrimeSolutions.gov: http://www.crimesolutions.gov/ProgramDetales.aspx?ID-87
Goode, E. (n.d.). Sending the Police Before There's a Crime. Retrieved from The New York Times: http:www.nytimes.com/2011/08/16/us/16police.html
Pearsall, B. (2010, Jun). Predictive Policing: The Future of Law Enforcement? Retrieved from National Institute of Justice: http://www.nij/journals/266/predictive.htm
Shurkin, J.N. (2011, Sept 13). Santa Cruz cops experiment with predictive policing. Retrieved from TPM Idea Lab: http:idealab.talkingpointsmemo.com/2011/09/santa-cruz-cops-experiment-with-predictive-policing.php
Direct Selling
A lot of companies are resorting to marketing one-to-one or concentrating on a narrow niche. For this reason, the respective corporations find it preferable to make use of direct communication with their clients who are typically a small targeted group that are considered after much thought. Direct communication helps the companies in obtaining a quick feedback from the clients that quickens the pace of their decision making. Over the years, the paradigm of direct communication has come a long way because of the dramatic evolutions in technologies and because of the introduction of new marketing media, particularly the usage of Internet.
Direct mailing and electronic catalogs, facilitated by Internet technologies have allowed for the implementation of models related to direct marketing (Jonker, Piersma, & Potharst, 2006; Liao & Chen, 2004; 2011). esearchers in the past have made use of direct communication as a model of complete business or…
References
Bolton, Ruth N., P.K. Kannan, and Matthew D. Bramlett (2000), Implications of Loyalty Program Membership and Service Experiences for Customer Retention and Value, Journal of the Academy of Marketing Science, 28 (Winter), 95 -- 108.
Bowman, Douglas and Das Narayandas (2001), Managing Customer-Initiated Contacts with Manufacturers: The Impact on Share of Category Requirements and Word-of-Mouth Behavior, Journal of Marketing Research, 38 (August), 281 -- 97.
Coughlan, A.T., & Grayson, K. (1998). Network marketing organizations: Compensation plans, retail network growth, and profitability. International Journal of Research in Marketing, 15(5), 401 -- 426.
De Wulf, Kristof, Gaby Odekerken-Schroder, and Dawn Iacobucci (2001), Investments in Consumer Relationships: A CrossCountry and Cross-Industry Exploration, Journal of Marketing, 65 (October), 33 -- 50.
76). As automation increasingly assumes the more mundane and routine aspects of work of all types, Drucker was visionary in his assessment of how decisions would be made in the years to come. "In the future," said Drucker, "it was possible that all employment would be managerial in nature, and we would then have progressed from a society of labor to a society of management" (Witzel, p. 76). The first tasks of the manager, then, are to coordinate an organization's resources and provide a viable framework in which they can be used to produce goods and services effectively and efficiently. The second set of tasks concern guidance and control. In Drucker's view, this role is almost entirely proactive: "Economic forces set limits to what a manager can do. They create opportunities for management's action. But they do not by themselves dictate what a business is or what it does" (Drucker,…
Enterprise vs. Departmental CM
Comparing Departmental and Enterprise Information Systems
Customer elationship Management (CM) Teams in Cincom Systems
It is paradoxical that the majority of enterprise software companies today have highly fragmented Information Systems (IS) departments with one entire series of departments dedicated to enterprise computing and a second, to specific departments or divisions. As enterprise software systems, specifically in the areas of enterprise CM are organized to ensure a very high level of data fidelity across departments, there is a conflicting set of priorities for ensuring real-time response to prospective customer requests (Power, 2009). Not only are the differences in enterprise-wide information systems significant in terms of the real-time vs. batch-oriented nature of their information needs, they also vary significantly in terms of the analytics used to evaluate their performance (Power, 2009). At Cincom Systems, these conflicts are accentuated by the speed of new product introductions in their five…
References
Coelho, P.S., & Henseler, J. (2012). Creating customer loyalty through service customization. European Journal of Marketing, 46(3), 331-356.
Molan, K., Park, J.E., Dubinsky, A.J., & Chaiy, S. (2012). Frequency of CRM implementation activities: A customer-centric view. The Journal of Services Marketing, 26(2), 83-93.
Ranjan, J., & Bhatnagar, V. (2011). Role of knowledge management and analytical CRM in business: Data mining-based framework. The Learning Organization, 18(2), 131-148.
Saini, A.K., Khatri, P., & Thareja, K. (2012). Customer relationship management in service organisations: Prospects, practices and areas of research. International Journal of Management Practice, 5(1), 1.