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MOVEIN BUSINESS INTELLIGENCE AND ANALYTICS REPORT me nt ap
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Business Plan on the Role of Business Intelligence and Analytics for MoveIn Pty Ltd

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TABLE OF CONTENTS Executive Summary ........................................................................................................................ 2 1 -­‐ Introduction .............................................................................................................................. 3 2 -­‐ Role of Business Intelligence ..................................................................................................... 3 2.1 -­‐ Business Intelligence -­‐ Overview ............................................................................................... 3 2.2 -­‐ Business Intelligence Tools ........................................................................................................ 4 2.2.1 -­‐ On-­‐line Analytical Processing .............................................................................................. 4 2.2.2 -­‐ Data Mining ........................................................................................................................ 5 2.2.3 – Dashboards ........................................................................................................................ 6 2.2.4 -­‐ Data Visualisation ............................................................................................................... 6 3 -­‐ Role of Business Analytics ......................................................................................................... 8 3.1 -­‐ Extracting Useful Information from Data .................................................................................. 8 3.2 -­‐ BA in Analytical level .................................................................................................................. 8 3.3 -­‐ BA on a Strategic level ............................................................................................................... 9 3.4 -­‐ Limitations of BA ...................................................................................................................... 10 4 -­‐ Issues with Business Intelligence Strategy ................................................................................ 10 4.1 -­‐ Ethics ........................................................................................................................................ 10 4.2 -­‐ Privacy ...................................................................................................................................... 11

5.1 -­‐ OLAP Recommendation -­‐ Oracle Hyperion Essbase ................................................................ 12 5.2 -­‐ Data Mining Recommendation -­‐ IBM Information Websphere Datastage .............................. 12

5.4 -­‐ Data Visualisation Recommendation -­‐ IBM Cognos ................................................................ 14

Overview of Application of INFS1602 Course Material to Report ................................................... 19

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List of References ............................................................................................................................. 16

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Appendices ................................................................................................................................... 16

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5.3 -­‐ Dashboard Recommendation -­‐ Yellowfin ................................................................................ 13

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5 -­‐ Final Recommendation ............................................................................................................ 12

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4.3 -­‐ Security .................................................................................................................................... 11

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Chapter: Executive Summary

Executive Summary This report contains the analysis for MoveIn Pty Ltd. that is targeted at expanding their business, whilst simultaneously improving their management reporting and decision support systems. An analysis of the Business Information (BI) tools -­‐ OLAP, Data Mining, Dashboards and Data Visualisation provided an insight into their applicability to the priorities of MoveIn. The varying extraction and analysis of data to understand the behavioural traits of the consumers and the market were identified as key priorities of MoveIn’s Business Intelligence plan. The Role of Business Analytics (BA) was identified in the various levels of business and we proposed that using BA will assist the research team to optimize MoveIn’s performance. Possible ethical, security and privacy issues that may rise when BI strategy is implemented were identified and suggested to be considered throughout MoveIn’s business. As a result of the analysis, the report reached the conclusion that choosing BI and BA software packages from IBM, Yellowfin and Oracle are the most suitable options to improve managerial decision-­‐making, develop real estate-­‐mortgage cross-­‐selling capabilities, detect fraudulent activity, and support the current research team’s capabilities.

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Chapter: Executive Summary

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1 -­‐ Introduction
MoveIn
Pty Ltd operates in an ever-­‐changing industry, and for a firm to obtain a competitive advantage in such an market, new and innovative tools must be considered in order for well-­‐informed managerial processes. Business Intelligence and Analytics are two fields which can be considered to support current existing ERP system, and as decision support systems, will assist MoveIn in accomplishing set goals for the firm. As such, we present this business report to you -­‐ our recommendation on how your firm can employ Business Intelligence tools to support decision-­‐making, facilitate effective research through Business Analytics, and the ethical and security issues associated with the use of these tools.

2 -­‐ Role of Business Intelligence
2.1
-­‐ Business Intelligence -­‐ Overview
The
incorporation of Business Intelligence as a tool in gathering and analysing information will provide relevant information to significantly improve the company’s decision-­‐making and managerial processes, on a strategic and operational level. With the emerging dominance of Web 2.0 in society, it is thus imperative that firms employ the use of Business Intelligence tools to obtain information from the various sources available, such as social media (Chen, Chiang & Storey, 2012). We will discuss the potential of using the tools of online analytical processing (OLAP), data mining, dashboards, and data visualisation to improve MoveIn’s managerial processes and achieve set goals.

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Chapter: 1 -­‐ Introduction

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2.2 -­‐ Business Intelligence Tools 2.2.1 -­‐ On-­‐line Analytical Processing

Figure 2.1 (Ju and Han, 2008)

OLAP, by comparing three variables, can act as a decision support system, assisting on determining the location of new MoveIn offices, with the incorporation of external XML data from the market assisting the identification of geographical “hot-­‐spots”; ideal places for MoveIn to set up new offices.

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Chapter: 2 -­‐ Role of Business Intelligence

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The potential of OLAP is not purely limited to internal data collected by a firm’s business support; XML data from external sources can be incorporated into the OLAP analysis for a more precise examination of collected data, allowing for greater categorisation and the creation of links between existing dimensions of the OLAP cube and the external XML data. (Pedersen, Pedersen & Riis 2011) XML data can be obtained from external statistical databases, such as the Australian Bureau of Statistics, on statistics such as geographical and population measures and this allows MoveIn to combine this with internal OLAP data in order to observe relationships in variables, such as the link between geographical location and house prices.

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Online Analytical Processing, or OLAP, is a BI tool MoveIn can employ to compare desired factors for a multi-­‐dimensional analysis of business data. The analysis of the data is presented as an OLAP cube, allowing users to analyse different dimensions of data to a greater degree than standard database queries. Figure 2.1 above is practical example by Ju and Han (2008), applying the OLAP approach to a retail drug industry by incorporating the three measures of time, location, and drugs in order to interpret the relationship between the three variables. MoveIn can benefit from this application through a comparison of desired traits they wish to observe; a suggested example would be the comparison of the three variables of cost of homes, location of homes, and MoveIn agencies. Such an analysis can allow important information and reports to be delivered to the relevant agencies, allowing autonomous decision making.

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2.2.2 -­‐ Data Mining

Data Mining is a BI tool which is a “process of nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases” (Katsara et. al 2001). The information gathered from this tool provides a detailed insight of previously unknown trends and influences of the given raw data. This approach is complementary to other data analysis techniques such as OLAP, as it is another way of finding meaning in data (Rygielski, Wang & Yen, 2002).

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Data-­‐mining is widely used in detecting fraudulent activities within the transactions of a company. Traditionally, each business is always susceptible to internal fraud or corruption, and data-­‐mining can be used as an analytical tool to identify the incoherencies of

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Each customer interaction is an essential opportunity to grow and develop their loyalty to MoveIn by increasing their satisfaction and revenue-­‐generation potential (Rygielski, Wang & Yen, 2002). By using the data-­‐mining model to predict the customers’ financial preferences, the real estate businesses can execute one-­‐to-­‐one campaigns for personalised services in offering a joint service with the mortgage businesses.

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Chapter: 2 -­‐ Role of Business Intelligence

Through the process of Data Mining, MoveIn can use the mass quantities of data compiled by the research team to develop correlations and relationships of the customers’ behaviours and trends (Dubey & Gupta, 2012). Like the in Figure 2.2, MoveIn can forecast and determine which locations are most suited towards their priorities in opening new franchises by analysing the behavioural variables of the consumer and market trends. This approach can also be used to understand the behavioural patterns of consumers using the NewHomes and MoveInMortgages websites.

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Figure 2.2 Data-­‐Clustering/Data Mining Map

inconsistent, duplicated or missing data. As it is not cost-­‐efficient to manually analyse all the data in search for fraud, data mining is mainly used to identify the riskiest anomalies (Phua et. al).

2.2.3 – Dashboards

Figure 2.3 -­‐ Example dashboard for software projects (Selby, 2005)

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Dashboards are a graphical representation of a firm’s internal performance at a specific point in time, displaying the firm’s key performance indicators (KPIs) in a simplistic, customisable layout. As one of the most user-­‐friendly BI tools available to firms, dashboards can be customised for each firm’s individual usage, and is capable of drawing and displaying real-­‐time data to the user. Hoang, Nguyen and Tjoa (2012) note the capabilities of interactive analytical data processing in data exploration, monitoring, and other tasks, indicating that the “personalization of user interactions and context representations is most important where formulating queries should be easily, effectively and should not require programming skills.”

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2.2.4 -­‐ Data Visualisation

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Dashboards are a simple BI tool anyone can use; MoveIn, by using this tool, will be able to identify gauges trends within internal, quantitative data, such as auction clearance rates and property prices, at a glance, and be able to identify previously unseen patterns within the data. These patterns will allow MoveIn to make justified decisions on the locations of new MoveIn branches in order to maximise competitive advantage, and thus, profitability.

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Chapter: 2 -­‐ Role of Business Intelligence

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Data Visualisation is the use of images to depict information, a tool to support analytic reasoning. This visual analysis not only represents the data graphically but also interacts with the representations which “filters out what’s not relevant, drill into lower levels of detail, and highlight subsets of data across multiple graphs simultaneously” (Few, 2007). We recommend that MoveIn visually analyses the saturation of real-­‐estate agencies within their marketing regions to determine which locations should be utilised for their new franchises. MoveIn can also improve the functionality of their NewHome and MoveInMortgages websites, as they can monitor how customers navigate through the pages. Also, by gathering information of customers’ past transactions, we can provide offers and services that are personalised to their needs. This will improve the capabilities of cross-­‐ selling between the real-­‐estate and mortgage businesses.

Figure 2.4 -­‐ String and Bead Model (Chang, R et. al 2010)

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Chapter: 2 -­‐ Role of Business Intelligence

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Detecting fraudulent activity is possible through data visualisation. Figure 2.4 is an example of a Strings and Beads Visualisation, giving an overview of all transactions but also providing specific detail when you click on each transaction. The strings represents the clusters for the year while the beads represent the transactions of the day. Through this process, we are able to visually see any abnormal temporal patterns which would initiate a further investigation to determine when the activity was fraudulent.

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3 -­‐ Role of Business Analytics

Business Analytics (BA) embraces technologies and is a process of gathering, analyzing and transforming historical data. It uncovers the insights from data to predict future events and business requirements in companion with analytical processes (Watson and Wixom 2007). The Integration of BI and BA processes will ultimately lead MoveIn’s research team to capture useful data that optimises business process. Figure 3.1 demonstrates how BI and BA are integrated (Hardroon 2011). By extracting and interpreting useful information about trends of economies and market, BA will play a key role in achieving effectiveness.

3.2 -­‐ BA in Analytical level

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Chapter: 3 -­‐ Role of Business Analytics

BA allows organisations to gather information from data of the real estate market’s key determinants such as demographics, real interest rates, economic cycles and government policies (Egebo, Richardson and Lienert 1990). Since key determinants play a significant role in how real estate is priced and what types of properties are in demand (Nguyen 2011), MoveIn’s research team will have a deeper understanding of market activities, and further to refine target markets, reduce customer attrition and perform a holistic evaluation of potential investments (Bruce 2013).

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3.1 -­‐ Extracting Useful Information from Data

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BA bridges the gap between the technical environment and company itself. Figure 3.2 shows how analysts connect business and its data warehouse. BA allows organisations to interpret the information to generate knowledge by employing analytical methods such as data mining, statistical test and explorative analytics (Laursen & Thorlund 2010). This essential knowledge will guide the research team to not only disclose areas for improvement but also to discover customers’ purchasing tendency (cross selling) and how they develop over time (up selling). From this knowledge MoveIn will be able to apply analytical customer relationship management (CRM) and design solutions that are tailored to the customers’ and vendors’ needs (Kmakura et al 2005).

3.3 -­‐ BA on a Strategic level

Predictive analytics will enable company to predict future trends and probabilities. Therefore BA will allow company to develop suitable strategies that can handle possible future issues and create long run competitive advantages (Rouse 2009). During strategic development, the information and knowledge is regarded as a resource. Ongoing measurement and analyzing of performance and target achievements will build feedback system that provide information such that highlight its strengths and weaknesses that need improvement. Therefore by deploying the full capabilities of BA, MoveIn will be able to optimise their business (Davenport & Harris 2007). Figure 3.3 displays how the processes are carried out.

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Chapter: 3 -­‐ Role of Business Analytics

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3.4 -­‐ Limitations of BA Even though BA enables the organisation to improve business processes and performance, there are some downsides associated with BA (Kohavi et al 2002). Computerworld report shows that the challenges are associated with integration of data from multiple source systems and other enterprise applications. To combat this difficulty, organisations should choose quality BI and information systems that meets the requirements of organisations. As BA predictions are based on historical data a more proactive shift may also reduce the limitation of BA (Computerworld & SAS 2009).

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4.1 -­‐ Ethics

Ethics give us the foundation for better decision making within an organisation (Mingers & Walsham 2010). It is critical that MoveIn consider and evaluate how it will address ethical problems as part of their BI Strategy. MoveIn must ensure that all ethical policies are well thought out, updated and implemented prior to the ambitious plans to further expand.

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4 -­‐ Issues with Business Intelligence Strategy

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Chapter: 4 -­‐ Issues with Business Intelligence Strategy

4.2 -­‐ Privacy Privacy is one of the most important issues raised in information systems (Zwass 2012). With the planned expansion MoveIn will receive high volume data traffic from updating and integrating its current BI. How much should a client, employee, franchisee to reveal to others, and under what conditions and with what safeguards? (Mason 1986) Given the nature of the industry, highly sensitive information would need to be exchanged in the BI. Collection, storage, and dissemination of individual records are an essential tool that MoveIn will incorporate, and thus a balance must be struck with consideration for privacy. BI usage must complies with the Privacy Act 1988 (Commonweath). An opt-­‐in approach in soliciting information, whereby the individual expressly needs to consent to the information provided, can be an effective way of ensuring privacy (Mason 2010).

4.3 -­‐ Security A goal of information security is essentially to protect MoveIn BI. Security problems exist in all aspect of the BI. A perfect BI is fictional and every BI has flaws (Paulson & Coulson 2011). Threats exist within all three facets of a BI those being presentation layer, network layer, and business process (She & Thuraisingham 2007). The major security challenge will be merging the many services and divisions MoveIn operates under one BI system successfully and integrating them. This requires vast amounts of data to be transferred increasing security attacks from external and internal sources. The issue that MoveIn must consider is weighing up the security measures versus accurate business decision tools. Chapter: 4 -­‐ Issues with Business Intelligence Strategy

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59% of security breaches surveyed in 2004 were caused internally (Gordon, Loeb, Lucyshyn & Richardson, 2004). This is a result of users not paying attention to important security procedures (McKendrick, 2011). Therefore MoveIn must ensure a culture of security awareness and is achieved by ongoing, updated training and education for clients and employees.

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5 -­‐ Final Recommendation We present to MoveIn our final discussion and recommendation of Business Intelligence and Analytics software packages -­‐ the packages best suited to improve managerial decision-­‐ making, develop real estate-­‐mortgage cross-­‐selling capabilities, detect fraudulent activity, and supports the current research team’s capabilities. We will evaluate the Business Intelligence and Analytics options offered and present to you our recommendation of these products for your business’s projects.

5.1 -­‐ OLAP Recommendation -­‐ Oracle Hyperion Essbase
Our
first recommendation are the Business Intelligence solutions offered by Oracle, specifically Oracle’s OLAP tool provided through Oracle Hyperion’s specialised Essbase program. An alternative OLAP product is Oracle Database 11g, and basic users may benefit from simple access to a multi-­‐dimensional analysis. However, MoveIn operates in the real estate market involving multiple variables that can change quite drastically in a short period of time, and thus requires full multi-­‐dimensional access to OLAP. Oracle Database does not support the incorporation of external XML data for greater categorisation; this is one critical weakness of the software, as MoveIn can benefit from the incorporation of geographical and population data into their internally contained data (Pedersen, Pedersen & Riis, 2011), as we suggested in 2.21 of this report. In contrast, Essbase is directed at users in a “line a business typically with a large degree of uncertainty”, whom “needs to understand a dynamic and changing environment” (Schrader, 2008), and allows greater analysis and reporting of information. (Schrader, 2008) Hyperion’s Essbase software is, however, significantly more expensive than Database 11g’s in-­‐built option -­‐ Oracle’s online store sells the Essbase program at a minimum cost of $2900.00 per person, as opposed to $460.00 for the Oracle OLAP solution. However, we believe the higher price is justified by the greater features of the Essbase version, and for a firm the size of MoveIn, a program with a stronger focus on end-­‐user forecasting is essential for greater decision-­‐making.

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Our second recommendation is the the IBM Information Websphere Datastage, the core component of the Websphere Data Integration Suite which enables businesses to tightly integrate enterprise information.

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5.2 -­‐ Data Mining Recommendation -­‐ IBM Information Websphere Datastage

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Chapter: 5 -­‐ Final Recommendation

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IBM Datastage extracts data from various source systems and transforming it to valuable information. A distinct competitive benefit of Datastage is its “unique, high-­‐performance parallel processing engine built upon a single integrated repository for maximum integration and collaboration between team members” (IBM Software Group, 2011). This will allow MoveIn to connect all the franchises to one main repository whilst also improving the capabilities of cross-­‐selling between the mortgage and real-­‐estate businesses. Its flexible, yet powerful platform has numerous productivity-­‐enhancing features which substantially lowers the learning curve for new users. This is particularly beneficial for MoveIn to lower administration costs in teaching the system to new franchisees. However, IBM’s biggest weakness is in its costs, priced at $3740 which is substantially more expensive than Oracle’s Hyperion Essbase. It is also limited as its only available to Windows users.

5.3 -­‐ Dashboard Recommendation -­‐ Yellowfin
Thirdly,
we recommend Yellowfin as the ideal vendor for MoveIn’s dashboard processing. Yellowfin, unlike much larger companies, such as Oracle, specialises in dashboarding and reporting, allowing the firm to focus on providing high quality dashboards to consumers and end-­‐users. Yellowfin, an up-­‐and-­‐coming Australian business vendor, supports mobile dashboard usage, granting a greater degree of accessibility for employees, allowing employees to check key performance indicators wherever they are.

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Figure 3.1 -­‐ BARC (2012)

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Chapter: 5 -­‐ Final Recommendation

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Figure 3.2 -­‐ BARC (2012)

5.4 -­‐ Data Visualisation Recommendation -­‐ IBM Cognos
The
fourth recommended solution for MoveIn is IBM’s ‘Cognos’, particularly the user-­‐ interface, ‘Insight’. Insight delivers a personal approach to analytics whilst also creating a “unified workspace for business intelligence and analytics that the entire organization can use to answer key business questions and outperform the competition” (IBM Software Group, 2010).

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As dashboards are highly customisable in regards to content displayed, we believe Yellowfin, as top ranked dashboard vendor in customer satisfaction in the BI Survey 12, is perfectly suited for MoveIn’s purposes. Other vendors, such as Oracle, are unable to match the productivity offered through Yellowfin, as demonstrated in the above graphs. IIf MoveIn chooses to invest in a dashboard as part of their BI solution, we highly recommend Yellowfin; however, quality does not come cheap, at $600 per user per annum, but we believe this price can be justified through Yellowfin’s “modern look and feel” and innovation. Oracle, in comparison, offers dashboard services at $400 per user per annum; although cheaper, we believe the step-­‐up in price is justified for the services provided by Yellowfin.

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Cognos Insight can be utilised to visualise the analytic results of the raw data, where users can navigate through large sets of data with ease. These clusters can be filtered according to association and relationships, where users can identify trends and outliers through the illustrations. A key benefit of Cognos Insight is the What-­‐If Scenario modelling, which empowers users to analyse and optimise plans based on different assumptions (IBM Software Group, 2010). MoveIn can use this function for all of its priorities to analyse the

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Chapter: 5 -­‐ Final Recommendation

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behaviour of the markets and its customers, whilst also discovering unnoticed trends that is difficult to see without a visualised tool.

Scoring Model Criteria Cost Security Functionality Speed Ability to detect fraud Oracle Yellowfin IBM (Datastage) IBM (Cognos) 4 7 9 8 7 5 7 9 9 N/A 9 7 53.66 6 7 8 8 8 7 8 52 7 7 8 9 8 7 8 54

Implementation Ease of Use 8 Ad Hoc Query and Enquiry Weighted Total /70 9 53

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Chapter: 5 -­‐ Final Recommendation

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Appendices List of References BARC, 2012, Yellowfin in THE BI SURVEY, accessed 16 November 2012, Bruce, S., 2013, Gathering Customer Demographics Using Business Analytics, accessed 31 May 2013, Chang, R., Lee, A., Ghoniem, M., Kosara, G., Ribarsky, W., Yang, J., Suma, E., Ziemkiewicz, C., Kern, D., Sudjianto, A., 2008, ‘Scalable and Interactive Visual Analysis of Financial Wire Transactions for Fraud Detection’. Computer Science. Chen, H., Chiang, R.H.L., Storey, V.C., 2012, ‘Business Intelligence and Analytics: From Big Data to Big Impact’, MIS Quarterly, Vol. 36, No. 4, pp. 1165-­‐1188. Computerworld & SAS., 2009, ‘Defining Business Analytics and Its Impact On Organizational Decision-­‐Making’ Coulson, T., Paulsen, C., 2011, Beyond Awareness: Using Business Intelligence to Create a Culture of Information Security, Communications of the IIMA, Vol.11 No. 3, pp. 36-­‐54 Davenport, T. H., & Harris, J. G., 2007, Competing on analytics: the new science of winning. Harvard Business School Press. Dubey, G., Gupta, A., 2012. ‘Identifying Buying Preferences of Customers in Real Estate Industry Using Data Mining Techniques’, International Journal of Technology. Vol. 2, No. 1, pp. 1-­‐5. Egebo, T., Richardson, P., & Lienert, I.,1990, ‘A model of housing investment for the Major OECD economies’, OECD Economic Studies, 14, 151-­‐88. Few, S., 2007, ‘Data Visualisation, Past, Present and Future’,Innovation Center for Performance Management pp. 1-­‐12. Gessner, G. & Scott, R., 2001, ‘Using Business Intelligence Tools to Help Manage Costs and Effectiveness of Business-­‐to-­‐Business Inside-­‐Sales Programs’, Information Systems Management. Vol. 26, pp. 199-­‐208.

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Gordon, L. A., Loeb, M. P., Lucyshyn, W., & Richardson, R., 2004. 2004 CSI/FBI computer crime and security survey. Retrieved May 28, 2013,

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Chapter: Appendices

Hardroon, D.R., 2011, 'Business Analytics; Unleashing Latent Potential', Synthesis Journal, Section 2, pp. 21-­‐28. Hoang, D.T.A., Nguyen, T.B., Tjoa, A.M., 2012, ‘Dashboard by-­‐Example: A Hypergraph-­‐based approach to On-­‐demand Data warehousing systems’, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1853-­‐1858. IBM Cognos Business Intelligence Handbook, 2010 IBM InfoSphere Datastage Data Flow and Job Design Handbook, 2011 Jin, H., Shum, W., Leung, K., Wong, M., 2004, ‘Expanding Self-­‐Organizing Map for data visualization and cluster analysis’, Information Sciences. Vol. 163, pp. 157-­‐163. Ju, C., Han, M., 2008, ‘Effectiveness of OLAP-­‐based Sales Analysis in Retail Enterprises’, 2008 ISECS International Colloquium on Computing, Communication, Control, and Management, Vol. 3, pp. 240-­‐244. Kamakura, W., Mela, C. F., Ansari, A., Bodapati, A., Fader, P., Iyengar, R., & Wilcox, R. ,2005. ‘Choice models and customer relationship management.’, Marketing Letters, Vol. 16, No. 3-­‐4, pp. 279-­‐291. Katsaras, N., Kinsey, J., Senauer, B., Wolfson, P., 2001, ‘Data Mining a Segmentation Analysis of U.S. Grocery Shoppers’, Working Paper 01-­‐01. The Retail Food Industry Sector Kohavi, R., Rothleder, N., and Simoudis, E., 2002, ‘Emerging Trends in Business Analytics’, Communications of the ACM. Vol. 45, No. 8, pp 45-­‐48 Laursen, G. H., & Thorlund, J. 2010, ‘Business analytics for managers: taking business intelligence beyond reporting’, Wiley. Vol. 40. Mason, R. O., 1986, ‘Four ethical issues of the information age’, Mis Quarterly,Vol. 10, No. 1, pp. 5-­‐12. McKendrick, J., 2011, ‘Culture of complacency-­‐-­‐and misunderstanding hampers-­‐-­‐information security’, Database Trends & Applications, Vol. 25, No. 1. Mingers, J., & Walsham, G., 2010, ‘Toward ethical information BIs: the contribution of discourse ethics’, MIS Quarterly, Vol. 34, No. 4, pp. 833-­‐854. Nguyen, J., 2011, 4 Key Factors That Drive The Real Estate Market, accessed 31 May 2013.

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Pedersen, D., Pedersen, T.B., Riis, K., 2011, ‘On-­‐demand multidimensional data integration: toward a semantic foundation for cloud intelligence’, The Journal of Supercomputing, pp. 1-­‐41.

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Chapter: Appendices

Phua, C., Lee, V., Smith, K., Gayler R., ‘A Comprehensive Survey of Data Mining-­‐based Fraud Detection Research’. School of Business Systems, Faculty of Information Technology. Rouse, M. (2009) Predictive analytics, accessed 31 May 2013. Rygielski, C, Wang, J, Yen, D. 2002, ‘Data mining techniques for customer relationship management’, Technology in Society. Vol. 24, pp. 483-­‐502. She, W., & Thuraisingham, B., 2007, ‘Security for enterprise resource planning BIs’, Information BIs Security, Vol. 16, No. 3, pp. 152-­‐163. Schrader, M., 2008, Understanding an OLAP Solution from Oracle, Oracle, accessed April 2008. Watson, H.J., and Wixom, B.H., 2007, ‘The current state of business intelligence’, Computer Society Press Vol. 40, No. 9, pp 96-­‐99. Wu, M. 2010, ‘The Search for Sustainable Competitive Advantage: A Stakeholder Management Perspective’, Philosophy and Management, Vol. 1, pp. 1-­‐20. Zwass,V., 2012, ‘Ethical issues in information BIs’, Understanding Information Retrieval BIs: Management, Types, and Standards, 77.

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Chapter: Appendices

Overview of Application of INFS1602 Course Material to Report
The
course material on Business Intelligence and Analytics provided us with a useful starting point for the discussion of the various tools and solutions available for use by various companies around the world -­‐ with a basic introduction to OLAP, data mining, dashboards, and data visualisation, we were guided through the initial steps of this report and was provided with a clear, easy to understand foundation upon which we could start our academic research and structure our report over. Our academic research on these Business Intelligence tools focused strongly on the proven benefits of these tools, and as these vary, our research was fruitful, due to our engagement with the course material providing us a strong understanding of the properties of these tools. Along with the rise of technology come the contentious boundaries of ethical and social issues. Within Information Systems, the course material outlined the various ways individuals can commit white-­‐collar crimes. As fraudulent activity would appear as anomalies within the cluster of data, it is evident that BI tools can be utilised to capture such criminal activity within the Information Systems. Other issues included determining the balance between data needed for the BI tools, and data that shouldn’t be revealed due to respect one’s right to privacy. The course material related to Ethical and Social Issues in Information Systems helped us understand the rising social and ethical issues faced by businesses as they engage with modern society through new technologies. In finding our recommendations, we were able to apply our theoretical knowledge gained from the course material and apply it to the case-­‐study. Our foundational knowledge was furthered through extensive research of Business Intelligence vendors -­‐ outlining the costs and benefits of adopting certain information systems. However, in order to evaluate which solutions best fit the priorities of MoveIn, we had to have a comprehensive understanding of the functionalities of each BI tool and how they are utilised for businesses to gain that competitive advantage. Thus the course material directed us towards our final recommendation for MoveIn, taking into account all the advantages and disadvantages of the technologies.

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Chapter: Appendices

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