Weekly Assignments

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Please the see two attached assignments that I'm looking for assistance with. The Week 5 reading pdf file is attached for reference. These are two seperate assignemtns.

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International Management Review Vol. 11 No. 2 2015 Business Intelligence Technology, Applications, and Trends Muhammad Obeidat, Max North, Ronny Richardson, and Vebol Rattanak Management and Entrepreneurship Department Information Systems Department Coles College of Business Kennesaw State University, Kennesaw, GA 30144, USA Max@kennesaw.edu Sarah North Computer Science Department College of Computing and Software Engineering Kennesaw State University, Kennesaw, GA 30144, USA [Abstract] Enterprises are considering substantial investment in Business Intelligence (BI) theories and technologies to maintain their competitive advantages. BI allows massive diverse data collected from virus sources to be transformed into useful information, allowing more effective and efficient production. This paper briefly and broadly explores the business intelligence technology, applications and trends while provides a few stimulating and innovate theories and practices. The authors also explore several contemporary studies related to the future of BI and surrounding fields. [Keywords] Business Intelligence, Competitive Intelligence, Data Warehousing, Data Mining, Cloud Computing, Data Exploration and Visualization Introduction Data is growing at a rapid rate. Enterprises are turning to Business Intelligence (BI) theories and technologies in order to extract the maximum amount of information from this data in order to allow their employees to make better data-driven business decisions. BI transforms the raw, massive data collected by various sources into useful information. This information supports business operations, ultimately providing long-term stability for the firm (Rud, 2009). Additionally, as enterprises grow, there is an overwhelming need to analyze historical business data in order to predict future trends and improve business forecasting. A broader definition of BI is presented by Evelson (2008): “[BI] is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information.” Evalson builds on this, “[BI] allows business users to make informed business decisions with real-time data that can put a company ahead of its competitors.” In a recent article, Chaudhuri, Umeshwar, and Narasayya (2011) provided a broad overview of current BI technologies, and the manner in which they interact. The specific technologies addressed include extract transform load tools, complex event processing engines, relational database management systems, map-reduced paradigms, online analytic processing servers, reporting servers, enterprise search engines, data mining, and text analytic engines. The typical BI architecture is outlined as data moves through data sources, streaming engines, data warehouse servers, mid-tier servers, and front end applications. The article addresses the insights of reduced cost of data acquisition and storage as well as the resulting increased use by businesses acquiring large volumes of data to promote competitive advantages. Chaudhur et al. discuss new massively parallel data architectures and analytic tools, which are superior to traditional parallel SQL data warehouses and OLAP engines, and the need to shorten lag between data acquisition and decision making. The general goal of this paper is briefly and broadly to explore the BI technology, applications and trends while provides stimulating and innovate theories and practices. We explore several contemporary studies related to the future of BI and surrounding fields. 47 International Management Review Vol. 11 No. 2 2015 Competitive Intelligence It is important to understand that Competitive Intelligence (CI) is a term sometimes used as a synonym for business intelligence; however, CI is more accurately a sub-discipline of BI widely used for larger business clusters, focusing on textual reports prepared from public resources to help decision makers understand competitive environments. Consequently, Nemrava, Ralbovsky, Kliegr, Splichal, Svatek, and Vejlupek (2008) describe business clusters as geographic concentrations of interconnected businesses, suppliers, and any other companies in an associated field. The goal is to use semantic structures and business maps to enhance CI reports for easier retrieval of information and lucid presentation of complex information to support decision-makers’ strategies. Nemrava et al. conducted a study with a group of three hundred students who were trained to collect information for CI reports to address 3 fields: packaging, glass, and information industries. They designed core CI ontology and used Porter’s Five Forces as the underlying CI model. They also used two software tools, Ontopoly and Tovek Topic Matter (TTM), to better display and edit the ontology. Since this project was likely the first attempt to link CI reports with semantic technologies, specifically in large business clusters, the researchers suggest additional future work is needed. Diverse Business Intelligence Applications Business Intelligence applications are sporadically used in a majority of search-based applications within a variety of fields, such as Business, Security, Finance, Marketing, Law, Education, Visualization, Science, Engineering, Medicine, Bioinformatics, Health Informatics, Humanities, Retailing, and Telecommunications, just to list a few. While BI is widely used in Enterprises (private or public entities) for both standard business and e-business, BI applications are growing in many diverse fields. For instance, in the areas of Mobile Device Fraud Detection, Health Care Informatics, and even in Chronic Disease Management, studies are beginning to describe the advantages of BI applications. Mobile Device Fraud Detection Nguyen, Schiefer, and Tjoa (2005) reported on the use of real-time analytics to detect fraud of business process and operation. By providing real-time monitoring of processes, businesses were able to capitalize quickly on time-sensitive business opportunities. The sample of mobile phone fraud detection was used to gather events and was analyzed to detect usage patterns for normal or fraudulent behavior. Health Care Informatics Zheeng, Zhang, and Li (2014) addressed the lack of BI applications in Healthcare Informatics. They described BI and healthcare analytics as emerging technologies that can improve industry service quality, reduce cost, and manage risks. They note, however, that analytics healthcare data processing is mostly missing from current healthcare information technology (HIT) programs. Their paper conducted an analysis of how BI technologies can be incorporated into an HIT program. A general framework and several strategies were presented; the authors conclude by stating they will expand their investigation onto a national level to improve the framework. It is their hope that more HIT programs will recognize the importance of healthcare BI. Chronic Disease Management Wickramasinghe, Alahakoon, Georgeff, Schattner, De Silva, Alahakoon, Adaji, Jones, and Piterman (2011) investigated BI use for chronic disease management. They identified chronic disease management as one area of healthcare in which health knowledge management can have a positive effect. Their research presented a new BI module that will analyze, visualize, and extract knowledge from the chronic disease management network (cdmNet). Their aim was to facilitate short- and long-term decision making and improve the ability to understand care models, policy models, and economic models which are part of chronic disease management. Their paper contained results which obtained by applying this model to the data. The module consisted of three sub-modules: pre-processing, dashboard, and data mining. Pre48 International Management Review Vol. 11 No. 2 2015 processing converts cdmNet data to a suitable form, the dashboard provides an interface, and data mining extracts patterns which can potentially provide solutions to questions concerning chronic disease management. Assorted Features of Business Intelligence Although a good number of features of BI theory and practice exist, we will discuss here the most prominent and well-researched. There are several research thrusts related to assorted aspects of BI worthy of exploration: Data Integration, Real-Time Analytics, Balanced Efficiency and Effectiveness, and Collaboration and Teamwork. Data Integration Dayal, Castellanos, Simitsis, and Wilkinson (2009) analyzed and described the requirements necessary for data integration flows in the “next generation” of operational BI systems, the limitations of current technologies, challenges, and a framework to address these challenges. Their goal was to facilitate the design and use of optimal flows to meet new and evolving business requirements. Their paper investigated the traditional BI architecture and compared it to next generation architecture. Their solution was a layered methodology for data integration flow life cycles. Metrics and tradeoffs were discussed, and the pros were shown to outweigh the cons. They concluded that with the more complex integration flow designs, it is important to create automated or semi-automated techniques to help practitioners deal with the complexity. Real-Time Analytics Nguyen, Schiefer, and Tjoa (2005) proposed an event-driven information technology infrastructure for operating BI applications to enable real-time analytics over business processes and operations. A “sense and response service architecture” called SARESA provided real-time monitoring of processes and allowed businesses to quickly capitalize on time-sensitive business opportunities. The real-time analysis requirements of a BI system, which are not a part of the traditional BI system, included data freshness, continuous data integration, analysis and active decision engines, high availability, and scalability. As mentioned earlier, the sample of mobile phone fraud detection was used to walk through the architecture’s approach. Call Detail Records (CDRs) are gathered as events and analyzed to detect usage patterns for normal or fraudulent behavior. This was a prototype of the SARESA system, and it will continue to be developed to support time-sensitive BI platforms. Balanced Efficiency and Effectiveness Finneran and Russell (2011) presented an article on Balanced Business Intelligence arguing that companies may be better served by concentrating on capability instead of maturity. The article was broken down in sections that would help with the balance, starting with Managed BI growth, Evaluating BI capacity, scope of delivery, information delivery capability curve, and levels of BI. Managed BI growth can be linked with BI capability, meaning that at any stage it is significant to operational, tactical or strategic perspective. For example, if a good is going to be made for one vendor, they may ask, “What is the most cost-effective way to manage our people and process to produce a product for our customer?” Next, they moved on to describing identifying and building capability, optimizing the architecture, and controlling the flow of information, focusing on areas defining organizational BI needs. For each category, authors identified needs to conceive and compose. The identification and building of capacity requires performance business-sustaining processes and generation of operational and managerial reporting capability. To optimize, businesses need to measure and manage through the creation of standard measures and tracking history to perform trend analysis for lines of business. Lastly, controlling the flow of information was segmented into govern and protect, meaning a continued framework for data governance to enable stewardship and improve corporate data confidence across the enterprise and the protection of 49 International Management Review Vol. 11 No. 2 2015 information delivered internally to the enterprise. The scope of delivery was described as the importance of getting information to the people that need it, when they need it. Using this helps BI to be effective by defining the audience and the manner for delivery as well as the method of access across the organization. Lastly, the levels of BI, which are described as stepping stones to success are described: Operational reporting, Tactical reporting, Strategic Reporting (History and Trending), Performance and Improvement, Highly Available and Highly Trusted, Highly Focused, and Highly Administered. To conclude the article they state, “The balance of both efficiency and effectiveness enables a well-rounded intelligence program in any organization.” Collaboration and Teamwork Berthold, Wortmann, Carenini, Campbell, Bisson, Strohmaier, and Zollep (2010) strived to create a system which would be highly scalable and flexible for gaining collaborative, ad hoc BI. The common shortcomings with organizations are the lack of business context information for analytical data, with too little emphasis on data from strong collaboration and a lack of integrating external or unstructured information in an effective and timely way. The BI platform proposed allows business users to shape their strategies in a collaborative manner, putting information acquisition back into the business user’s hands. It is accomplished with a flexible data model, scalable data store, a business configuration methodology, an information self-service environment, and an integrated collaboration environment (for instance, “Collaboration Rooms”). By using these methods, business users have the architecture for ad hoc and collaborative decision making. Furthermore, Lovell at el. (2014) stated that plenty of vendors promise to solve all business users' or technical teams' problems with their tool sets and methodologies. With the mounting pressure on BI teams (whether embedded in organizations or those of consultancies) to deliver on time and meet expectations, it is no wonder that the allure of agile BI has cast its net on unsuspecting teams desperate for success. It is possible to learn from an execution and delivery methodology crafted around the concept of the "team" rather than the "individual." This article looked at how teams can implement the agile mindset in building data output applications. It explained the concepts and how they relate to BI projects, rather than the typical data input applications managed through the software delivery life cycles commonly associated with the term “agile.” Data Storage and Technology As computer technology advances, larger volume of data are acquired and stored at much lower cost. Any classification of transaction in business, including e-business, RFID tags, Web sites, emails, blogs, and many more produces new data to be tracked. Authors briefly provide most important aspects of data storage and technology below, beginning with Data Type (Structured and Unstructured), Data Warehousing, Data Mining, and Data in Clouds. Data Type (Structured and Unstructured) In a broad context, there are two types of data—structured and unstructured—to be incorporated in BI phases. Park and Song (2011) introduced structured and unstructured data by stating that as the amount of data grows very fast inside and outside of an enterprise, it becomes important to seamlessly analyze both of categories to establish robust BI. Particularly as most valuable business information is encoded in the unstructured text documents, including Internet web pages, specialized Text OLAP solutions are needed to perform multi-dimensional analysis on text documents in the same way as on structured relational data. Since text mining and information retrieval are major technologies for handling text data, authors first review the representative works selected for demonstrating how they can be applied for Text OLAP. Then authors conduct a survey of the representative works selected for demonstrating how analysts can associate and consolidate both unstructured text documents and structured relation data for obtaining total BI. Finally, the authors present the architecture for a total BI platform incorporating structured and unstructured data. It is expected that the proposed architecture, which integrates information retrieval, text 50 International Management Review Vol. 11 No. 2 2015 mining, and information extraction technologies alongside relational OLAP technologies, would make an effective platform toward total BI. Data Warehousing One of the main sources of data provided for BI applications is collected from data warehouses. Data acquisition is becoming cheaper and easier, while the size of the data are getting larger, within range of tens to hundreds of terabytes. Farooq and Sarwar (2010) examine real-time data warehousing (RTDW) and highlight the advantages of using semi- structured multidimensional modeling (DMM), such as XML, in RTDW versus traditional DMM, such as relational. The two are compared on four characteristics, including heterogeneous data integration, types of measures supported, aggregate query processing, and incremental maintenance. The authors also provide explanations as to why semi-structured DMM is better than structured DMM. In their article, they used the RTDW framework as an example for a telecommunication company. Their experiment showed that if a delay is caused in incremental maintenance of DMM, there is no ETL technology that can help in real-time BI. They conclude that semistructured XML-DMM is more capable for incorporating real-time data updates from operation sources. Not only does it reduces query response time, but also increases real-time BI. In an article, Goeke and Faley (2007) wrote how data warehouse flexibility affects its use. In the beginning, background knowledge is given before the research is done. A data warehouse enables the collection and storage of vast amounts of data extracted and analyzed by end users. Now the research, which was done in a form of a survey including the original TAM items adapted to fit a data warehousing environment, was sent to managerial-level data warehouse users in a number of major Midwest U.S. Corporations. The survey also obtained other information, including the industry and size of the user’s company, position and department, the amount and type of system-related training the user had, what system support was most useful to the user, and the amount of experience the user had with the data warehouse. The research used various scales to get to the results. The results that they achieved were well in line with previous studies conducted. In conclusion, they made recommendations for increasing data warehouse usage by leveraging its flexibility. The extent to which the data warehouse is perceived to enhance job performance is the most important determinant of its usage. Flexibility is not a major determinant of usage, and users will not use a data warehouse because it is flexible. Lastly, system flexibility is embedded within the features of the data warehouse, meaning that sophisticated users are more likely to leverage system flexibility, because they are savvy enough to know where the flexibility exists in the data warehouse. Data Mining In simple terms, data mining provides extensive and complex analysis of historical and current data, allowing the building of predictive models. An article by Grossman, Hornick, and Meyer (2002) described Data Mining in great detail, starting with established and emerging standards that address various aspects of data mining, including Models, Attributes, Interfaces, Settings, Process, and Remote and Distributed Data. After a brief description of the aspects of data mining, authors move into the different standards of data mining and break them up into three major categories XML Standards, Standard API’s, and other standard efforts. In XML standard there was a group known as the Data Mining Group that deve ...
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School: Duke University


Enterprise Threats

Enterprise Threats
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Enterprise Threats

Any individual or group of people who own an enterprise expect that the business offers
numerous rewards. However, although this is the expectation of many people, there are still
threats that if they befall a business, then it becomes very difficult to realise such rewards.
Since most of these threats are in most cases inevitable, it’s very much encouraging for the
enterprise owners to have a risk management plan for their enterprises so they can carry out
successful risk mitigation and recovery processes and ensure that their enterprises do not
always end up in losses.
Threats that an enterprise can face can be grouped in to four major categories which are
strategic, compliance, financial and operation threats. An example of a strategic threat is a
new competitor coming on to the market while compliance threats include responding to new
business regulations.
On the other hand, an example of a financial threat includes increased charges on interest
rates while operational threats include theft or breakdown of enterprise equipment. Threats
that are either caused by natural disasters or just accidents can lead to legal liabilities, loss of
revenue and big headaches to the enterprise. Some of the most common enterprise threats
include the following.

Enterprise Threats


Property losses.

In many enterprises the commercial property in one of the largest fixed assets that the
enterprise owns. Loss of property in an organization can be due to either theft from within or
outside the enterprise.
When such losses occur, it’s in most cases costly and time consuming to recover them which
means that the enterprise may most likely operate at a loss. An example of a property loss is
loss of an assembling machine which will result to halting all the assembling processes.
To ensure that such losses do not take place, there is need for proper coverage of the
enterprise’s premises and ensuring that a regular physical inventory in done. Check points
within the enterprise should also be established to ensure that there is no theft from within the
enterprise which is occasionally linked to the workers. Auditing the business processes and
physical locations can also be used to counter this type of threat.
When property losses occur, it leads to other threats such as business interruption since the
business will not be able to run as expected.

Business interruption

Business interruption is caused by natural calamities such as fire and floods which may force
an enterprise to relocate or even never to reopen again. This is because the cost of repairing
the original premises may be so high that the enterprise cannot afford. Research has shown
that more that forty per cent of enterprises do no reopen after a disaster such as fire or flood

Enterprise Threats

A real world example is fire consuming the entire enterprise property. Once such business
interruptions threats occur, they also lead to other threats such as loss of business property as
in most cases fire consumes the fixed assets of an organization and employee’s injuries
especially when the disaster occurred during the working hours.
To ensure that such disasters do no interrupt the normal functioning of the enterprise, proper
disaster recovery plan for the enterprise should be formulated. Also, creating a data recovery
strategy and backups and a procedure to alert employees of any calamity can be used to
counter this type of...

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