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Individual assignment 3328773

  1. 1. INFOMGMT 393 | Data Mining & Decision Support Systems Individual Assignment | Semester One, 2008 Global Stationery Supplies Document Contents; Page No. Task One 1-6 Task Two 7-10 References 11 Jess Maher | 3328773 | jmah021
  2. 2. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .Task One Data Warehousing The implementation of a Data Warehouse into Global Stationery Supplies (GSS) is largely feasible dueto the nature of such being a ‘physical repository where relational data are specially organised to provideenterprise wide, cleansed data in an integrated, standardised format which is multidimensional’ (Turban,Aronson, Liang & Sharda, 2007). Due to GSS’s global nature, the information collected from a number ofphysically dispersed locations is required to be utilised in conjunction, causing difficulties due to varying timezones, currencies, languages, laws & taxes, holidays, policies and practises. A data warehouse would providethe organisation with a resource which would enable them to have an enterprise perspective of theorganisations data which could more richly utilise otherwise redundant data for decision making. GSS’s strong commitment to uphold their good reputation to provide value for money and speedy,reliable services enforces the importance of the role and responsibility held by the Procurement team withinthe organisation. In order for the Procurement team to effectively utilise these connections established, oftenheld directly with producing factories, and provide stock to required locations in a “just-in-time” basis, aneffective communication and interaction must occur between the Procurement, Logistics & Ordering teams.The nature of the “just-in-time” basis within which GSS operates its stationery procurement service means theneed for paperwork, ordering, logistics, procurement and delivery all need to be processed and forecastthrough an integrated information source. There is also a clear need for information transferred between thestores and warehouses with the payments and invoicing teams, the nature of which may see difficulties whendealing with different locations, recording procedures or time zones. According to Ariyachandra & Watson (2005) (as cited in Turban, et, al.) the key factors that potentiallyaffect architecture selection decision include’ information independence between units, upper managementsinformation needs and the nature of the end users tasks’(2007, pp?), with GSS, as described, requires anenterprise wide perspective of information collected requiring free flows of information. There are a numberof possible structures and designs of Data Warehouses which could potentially be utilised within and create apotential benefit for GSS, those which are potentially the most beneficial have been explored in more detailbelow.Data Mart Centric Data Marts contain lightly and highly summarised data and metadata which are generally easy toorganisationally and technically construct (Turban, et, al., 2007, Hsieh & Lin, 2003). They utilise anarchitecture which would enable GSS to constructively extract sources of information from within thebusiness, transform the data (into a collaborated, standardised format) and load into data warehouse whichacts as a respiratory for current and historical data of particular interest (Turban, et, al., 2007). They aresomewhat limited however in their application, they do not provide a business enterprise view, and as thedata warehouse itself cannot create more data, there is often a high cost of redundant data (Turban, et, al.,2007). This data warehouse architecture may require more maintenance than other alternatives and in turnthe cost of capturing and maintaining the data can be high (Hsieh, et, al., 2003). 1 Jess Maher | 3328773 .
  3. 3. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .Distributed Data Warehouse Distributed data warehouses which are commonly incrementally constructed over time, provides nocommon metadata components across data marts which can potentially lead to complications whenattempting to integrate aspects of the organisation (Hsieh, et, al., 2003, Turban, et, al., 2007). The majority ofthe workload utilised when using such a data warehouse is placed on the individual work stations, meaningthe data warehouse itself is only viable for low volumes of data (Turban, et, al., 2007). These issues have leadto distributed data warehouse designs being widely perceived as unacceptable in the long run (Hsieh, et, al.,2007), and would not be a recommended option for integration into GSS for the same reasons.Hub & Spoke Data Mart The Data Warehousing Institute (TDWI) research findings report the most frequently implementedarchitecture among a number of large, powerful firms is the Hub-and-Spoke architecture of the datawarehouse design (Agosta, 2003). Such architectures enable easy customization of user interfaces and reports(Turban, et, al, 2007) which would be beneficial to GSS as it would allow the m to alter these interfaces andreports for all areas of their organisation on an international scale. The hub-and-spoke architecture wouldallow GSS to have a centrally planned area of operations while also allowing flexibility as ‘the number of timesthe data must be transformed is optimal for a majority of scenarios involving many to many nodes in anetwork of source and target data stores (Agosta, 2003). This structure utilises dependant data marts whichare subsets created directly from the data warehouse which ensures the advantage of consistent and qualitydata, however limits access to only one user at a time (Turban, et, al., 2007). This separated dependant natureof the data stored means a business enterprise view can be challenging to obtain and the costs for redundantdata, database administration and operations can be high (Turban, et, al., 2007).Enterprise Data Warehouse (EDW) The large scope for data allows the data warehouse to draw information from a variety of sources andconsolidate the information so it can be organised in a way which allows a business enterprise view of theorganisation (King, 2006, Turban, et, al., 2007). The EDW provides a number of tools to assist end users withno computer programming knowledge to effectively find, understand and evaluate data stored within thedata warehouse (King, 2006). Predefined reports provide instant access to relevant information which isretrieved and organised in a way which is understandable to the end user, however query and analysis toolscan be used to further discover patterns and draw conclusions from such (King, 2006). The EDW can be used to provide data for many types of additional types of decision support systems,which could allow the benefit of shared, consolidated information to be utilised by areas other than theanalysts and executives of GSS, for example the procurement, ordering and logistic teams, marketing andadvertising departments and financial functions of the organisation could make use of data through supportsuch as; CRM, SCM, BPM, PLM and KMS (Turban, et, al., 2007). The development of an EDW is not fast processand in this nature, it can be costly in set up (Turban, et, al., 2007), EDW’s are usually built a step at a time overseveral years, with the collection of data prioritised in order of importance (King, 2006). It is also essential tohave broad participation in development and strong executive support as without it, the project to developsuch a large system will lose momentum and development may become cost ineffective (Turban, et, al.,2007). 2 Jess Maher | 3328773 .
  4. 4. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies . When comparing the data mart and EDW approaches to data warehouses, Turban, Aronson, Liang &Sharda (2007) found the scope of the EDW is much larger (it can include several subject areas), there can be agreater number of simultaneous users, providing cross functional optimization and decision making. Howeverin comparison the data mart approach has a considerably shorter time, lower cost and difficulty indevelopment, yet requires more effort in the frequency of updates. The user type of the data mart approachare typically business area analysts and managers and the spotlight is on optimizing activities within thebusiness area, which is not really conducive to GSS’s structure and nature of operations. The EDW approach ismore commonly utilised by enterprise analysts and senior executives as it provides an enterprise wide view ofthe businesses data (p229) which is more conducive to the intention GSS holds. The implications of developing a data warehouse, regardless of the architecture chosen, will requiretime, resources and buy in from key members of the organisation and is therefore only a task which should beundertaken if a strong commitment is intended. The decision for design implementation of a data warehousewithin GSS needs to be researched more thoroughly in consideration of the intended purpose of the datawarehouse developed in order for any of the recommended suggestions to be considered of appropriatepotential. However, as the need to integrate and consolidate data from a variety of sources is critical to thebeneficial implementation of any data warehouse architecture within GSS, the issues posed by utilising a datamart centric or distributed data warehouse approaches are of great concern to GSS. Both approaches havedifficulty in providing an accurate enterprise view and the integration provided by the distributed datawarehouse is questionable due to a lack of common metadata (Hsieh, et, al., 2003,Turban, et, al., 2007),making these options the least favourable options for potential implementation within GSS. It is ideally recommended that an enterprise data warehouse be the preferred data warehousearchitecture to be developed within GSS. Such implementation would allow GSS to consolidate informationfrom a variety of sources providing an enterprise view of information and potentially assisting varied otherunits of the organisation by providing data for a range of decision support systems (Turban, et, al., 2007, King,2006). However, due to the time and effort required in the process of incrementally obtaining the prioritiseddata collections, the time and cost required for development of such an architecture can be considerably high(Turban, et, al., 2007, King, 2006), possibly making its utilisation within GSS not feasible. Alternatively, benefitcould also be potentially gained through the implementation of a hub-and-spoke data warehouse architecturewhich would potentially enable GSS to take an enterprise view through a network of linked source and targetdata stores (Agosta, 2003). This structure would enable consistent, quality data to be retrieved from a number of dependant datamarts (Turban, et, al., 2007) which would enable the independent regions of GSS to accurately record andreport their data to a central data store. There is however a requirement to consider the limitations of thisapproach by GSS before implementation in order to develop strategies to minimise the implications of these,the limited access to one user at a time may not potentially pose a great concern within GSS as the majority ofoperations analysis support data will be stored in regional data marts meaning access to the data warehousewould be limited to those executives based in the head office operations. With adequate consideration andplanning in order to minimise the costs of operation, data base administration and redundant data, theapplication of the hub-and-spoke data warehouse architecture within GSS would potentially provide decisionsupport in an enterprise view which could be of considerable benefit if utilised correctly. 3 Jess Maher | 3328773 .
  5. 5. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies . Data Mining Global Stationery Suppliers (GSS) like any large, multinational organisation could potentially gainbenefit from accessing data which may otherwise be redundant due to its untapped location. The possibility tounderstand more fully the aspects of its business and customers and the ability to customise to the needs ofclients and markets differently, is obviously of great potential advantage to GSS. Data mining overcomes manyof the limitations of traditional forecasting which many organisations may utilise and allows them tounderstand and analyse complex data sets in a number of ways (Garver, 2002). Data mining could potentiallybe utilised within GSS to improve advertising and marketing campaigns, assist in outlining the most effectivecommunication processes and gaining better understanding of their potential customers more thoroughlythrough the information held about their current customers (Berry & Linoff, 2004). Hypothesis-driven data mining could be utilised within GSS by a user whom has a proposition of whichvalidation of truthfulness can be gained using data mining tools, alternatively, patterns, associations andrelationships among data can uncover facts that were previously unknown to GSS by utilising a discovery-driven data mining approach (Turban, et, al., 2007). An example of the potential benefit could be utilisedthrough hypothesis-driven data mining within the procurement and ordering teams, whom recognise thatthere are peaks to their business at certain times of the year, in different locations. By utilising a data miningtechnique, GSS could understand the time frame for such peaks more clearly and validate their existingunderstanding, which could potentially assist in their planning and accommodation of such periods. Discovery-driven data mining techniques could potentially benefit GSS in a number of ways and areas, the process andoperations within the procurement team, which are of considerable strategic importance within the business,is one area where benefit could be perceived through such methods. The current and historical data collectedfrom store and warehouse sale records, ordering teams, procurement processes, marketing efforts and clientinformation could be analysed to potentially discover patterns, associations and relationships which arecollected from dispersed and differing locations. The alternative data mining tools and techniques which couldpotentially be implemented within GSS are based on a varying array of methods and approaches to datamining, these include; statistical, mathematical or next generation and artificial intelligence or machinelearning approaches (Turban, et, al., 2007, Berson, Smith & Thearling, 1999). There are a number of statistical methods of data mining which could benefit GSS by being used inorder to discover patterns and build predictive models, these methods include; linear and non linearregression, point estimation, Bayes theorem, correlations and cluster analysis (Turban, et, al., 2007).Regression analysis is a commonly used technique that is used to forecast estimates/predict data based onpatterns observed within large data sets (Turban, et, al., 2007). There are a number of mathematical and nextgeneration data mining techniques which would potentially provide GSS benefit through their ability touncover new information in large databases while also providing the building of new predictive models(Turban, et, al., 2007, Berson, et, al., 1999), which include; decision trees, algorithms, classification andregression trees (CART) as well as neural networks which are also classified as a machine learned approach(Turban, et, al., 2007, Berson, et, al., 1999). Further investigation into the approaches considered to have themost potential benefit to GSS have been explored and expanded below.Cluster Analysis Cluster analysis is an exploratory data analysis tool for solving classification problems, it can assist inproviding measures of definition and rules for assigning classes for identification, targeting and diagnostic 4 Jess Maher | 3328773 .
  6. 6. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .purposes (Turban, et, al., 2007). Cluster analysis can be approached in either a hierarchical or partitionalmanner, hierarchical algorithms use previously established clusters to discover successive ones, whilepartitional algorithms determines all the clusters at once (Wikipedia, 2008). GSS could utilise cluster analysis ina number of ways, for example, in order to segment their client market and determine target markets whichwould enable them to better utilise advertising resources, alternatively, the procurement team would definewhich locations, featuring which clients, purchase which products from which suppliers, potentially assistingthe planning of the logistics and procurement teams. There are a number of software options which integratetools which allow cluster analysis, such as Microsoft Visual Studio, would allow GSS to complete a range ofanalysis which could potentially provide benefit to many areas of the organisation with decision making. Therehave also been a number of specialised software packages which have been specifically developed for thepurposes of cluster analysis, including; Clustan Graphics and SPSS (Turban, et, al., 2007), in addition to a rangeof open source code freely available on the internet.Decision Trees Decision tree analysis identifies predictor variables, searching all variables until all relevant ones areselected, if they are not selected, they are not important to the prediction (Garver, 2002). By utilisingclassification and clustering methods, decision trees can be developed to assist GSS with decision making bytaking complex problems and breaking them down into increasingly discrete subsets (Turban, et, al., 2007).Decision trees can be used in a wide variety of business problems for both exploration and prediction using avariety of algorithms based on the tree created (Berson, et, al., 1999). There are a number of decision treealgorithms which are commonly implemented in many organisations, including; ID3 and C4.5 (Berson, et, al.,1999, Turban, et, al., 2007). These algorithms work on the basis that predictions are picked and splitting valuesbased on information provided by that slipt or splits (Berson, et, al., 1999). Decision Tree Analysis uses categorical and continuos data and has the ability to accommodate formissing data (Thomas, 2004), which would provide GSS with an accurate, historically based representation ofpatterns, associations and subsets of data collected. Decision trees have the ability to discover unexpectedrelationships and more clearly identify the differences between subgroups (Thomas, 2004). An example of thepotential insight which can be provided is described by Garner (2002) in the use of decision trees within thepizza delivery industry to assist customer satisfaction and loyalty data (p62). In this case, the use of decisiontrees provides a greater insight into the different segments of loyalty analysed, providing perceptions of bothimportance and performance (Garver, 2002). The use of decision tree analysis in a simular manner within GSScould potentially provide benefit in a number of areas, for example, the analysis of customer preferences instores, the merchandise order patterns in differing countries or the periods of elevated purchasing. There are a number of software packages that provide decision tree analysis as one of their tools oroptions, including; Oracle, Microsoft Visual Studio and Clementine (Turban, et, al., 2007). The interfaceprovided by Microsoft Visual Studio would possibly be the easiest for implementation as members of GSS areall currently users of other Microsoft software, such as Office, and the familiar interface would help in theimplementation of this system in a way that will encourage its use. Microsoft Visual Studio also allows for arange of other analysis options such as; cluster analysis, association rule analysis, linear and logistic regression,Naïve Bayes and even neural networks, all within the same project.Neural Computing Neural computing uses masses of historical data to identify and analysis changes in patterns, situationsand tactics (Williams, 1994), enabled by utilising a method which emulates how the brain works (Turban, et, 5 Jess Maher | 3328773 .
  7. 7. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .al., 2007) in a manner which is mathematically driven (Garver, 2002). Such networks require a step by stepprocess in order to create the knowledge stored in weight associations between two neurons (Turban, et, al.,2007). This provides the neural network of the advantage of being able to perform tasks that other linearanalysis options can not while also having the ability to be implemented into a wide variety of applications(Artificial Neural Networks, 2008). A neural network has the advantage of being able to perform tasks thatother linear analysis options can not while also having the ability to be implemented into a wide variety ofapplications (Artificial Neural Networks, 2008). The neural network is actually a form of artificial intelligence,which can either be based on a mainframe (such as a data mart) or on a network of personal computers(Williams, 1994) which would provide GSS with more flexibility and chose when implementing and using such adata mining technique. Neural networks have the ability to learn from their experience and do not require programming offixed rules or equations in order to analyse quantities of complex data and identify patterns from whichpredictions can be made (Taylor, 1997). The architecture of the neural network however, needs to be emulatedand the network itself requires training in order to operate (Artificial Neural Networks, 2008). There are anumber of software options for completing neural network analysis, those which are specific for NeuralNetworking include; Stuttgard Neural Network Simulator (SNNS), Emergent and Java NNS (Wikipedia, 2008).There are also a large number of software options available for data mining analysis which include neuralnetwork analysis including; Clementine, SAS Enterprise Miner and again Microsoft Visual Studio (Turban, et, al.,2007), while again open source software, such as WEKA, developed at Waikato University, also provide neuralnetwork analysis ability. While the techniques used to analyse the data in both decision tree and neuralnetwork data analysis differ conceptually, the results from both are simular, providing predictions andexamining the impact of certain variables on those predictions (Garver, 2002), however it is clear the initialeffort required to enable a neural network is considerably more. There are a large amount of software optionsavailable for Any one of the data mining techniques explored show large potential benefit if implemented withinGSS, it would be important to reconsider the organisations strategic objective and goals in order to assess thebest application of any technique in accordance with the intended purpose. One potential software optionwhich could easily be utilised within GSS to gain benefit from cluster, decision tree and neural network analysis,in conjunction with a number of further tools, is found in the implementation of Microsoft Visual Studio. Neuralcomputing would be considered top of the line for GSS, however, the implications of such a large projectimplementation within GSS is not apparent that is clearly feasible, especially considering the result from theinvestment could be achieved through other methods, such as neural network analysis and also decision treeanalysis (Garver, 2002). Decision tree analysis would also enable GSS to gain decision making support in a waythat allows for both the discovery of new information and creating predictive models (Turban, et, al., 2007,Bensen, et, al., 1999). The use of cluster analysis within GSS would be of clear benefit to understanding the trends andpatterns of their business, however to implement specific cluster analysis software within GSS would beinadequate to the intended purpose. It would be recommended that GSS utilise a software package option thatenables them to complete cluster analysis to verify finds from other next generation data mining techniques,by utilising tools and techniques provided within Microsoft Visual Studio, GSS would be able to create decisiontrees, cluster analysis, association rule analysis, linear and logistic regression, Naïve Bayes and even neuralnetworks, all within the same project. The potential benefit of using such analysis tools such as those utilisedthrough the implementation of software such as Microsoft Visual Studio could be experienced through variouslayers and units within GSS. 6 Jess Maher | 3328773 .
  8. 8. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .Task Two Prototype Project Plan for the implementation of Microsoft Visual Studio Data Mining Tools & Techniques within Global Stationery SuppliesProject BriefThe intention of this project is assess the feasibility of the implementation of data mining tools providedwithin Microsoft Visual Studio, for the potential benefit of a number of areas of GSS, with particular referencein this project to the procurement processes as well as sales and marketing functions of the business. Thereare a number of assumptions which have been made in order to appropriately consider the viability of suchimplementation within GSS, which include assumptions about organisation structure and processes, datacollection and storage. It is assumed that the procurement and marketing operations for GSS are basedprimary from head office location, with regional units based on store locations globally. It also assumed thatGSS will utilise an appropriate data warehouse architecture which will be the basis to provide consolidated,cleansed data for the purposes of data mining, allowing consideration from a store, region or enterpriseperspective which can be utilised by various layers and areas of the organisation.Ideally for the purposes of this prototype project, it would be recommended that a particular region beselected for analysis. By utilising Microsoft Visual Studio tools, GSS would be able to implement a number ofdata mining techniques, such as a decision tree and cluster analysis, to discover patterns, new data andpredictive models from data collected throughout the organisation. Such information gained from suggestedtechniques could be utilised for the potential benefit of the procurement team; understanding peaks andactivity of business in different regions could potentially assist them in allowing for such times, and also thesales, advertising and marketing functions; potential to benefit from a variety of information in a range ofways, such as understanding which consumers are most likely to purchase a particular product, which wouldenable them to more accurately customise their marketing efforts to the most appropriate audience.Project AimTo implement a regional prototype project implementation of Microsoft Visual Studio and the data miningtools and techniques it provides such as, decision tree and cluster analysis, in order to test its feasibility forgeneral application organisation wide into GSS, in order to provide benefit and support to decision makerswithin the organisation.Requirements for ProjectIt is assumed that a data warehouse will be utilised to provide information for the purposes of utilising thedata mining tools and techniques suggested and as the prototype project will only be considering informationand data collected within that region, then the scope of the requirements is limited to the selected region. Itis assumed that for the purposes of this prototype project that the focus for data mining use be the 7 Jess Maher | 3328773 .
  9. 9. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .procurement, sales, advertising and marketing functions of the organisation, therefore the scope of datarequirements is also limited within this assumption.Data required for prototyping purposesIn order to accuracy complete analysis suggested information will be required from a number of sources; • Transactional Data (information from sales, store records and client purchases). • Ordering and Stock holding Data (information about the patterns of ordering for stores, products they hold on hand and potentially product life cycle information). • Procurement and Logistics Data (information which allows categorisation of products and potential assessment of process). • Marketing and Advertising Data (information to assess the efforts of functions, benefits and relationship to sales).Platform and software intended on useFor the purposes of this prototype project, Microsoft Visual Studio will be utilised to access and assessinformation held within GSS’s data warehouse records. By utilising the Microsoft Visual Studio software andtools provided, GSS would be able to create not only the recommended decision tree and cluster analysis butalso have access to further analysis methods such as; association rule analysis, linear and logistic regression,Naïve Bayes and even neural networks, all within the same project.Project Implementation Concerns• Training of staffIn order to successfully utilise the tools and techniques provided within the Microsoft Visual Studio’s softwarepackage, training of those end users whom will be utilising the software will need to be undertaken to ensurethese processes can be accessed. For the purposes of this project, it is assumption that the regional andexecutive leaders from within the GSS structure would be the users of such tools to assist their decisionmaking within the procurement, marketing and advertising functions. Whilst the tools provided withinMicrosoft Visual Studio are relatively easy to use, if the user has not utilised the software before, it may bedifficult to navigate and a common cause of software implementation failure is a lack of understanding by itskey users.On the job training is generally the best application method for such software uses, as the ability to rememberinformation if learned in the same environment as it is applied is greatly high (Read, Hunt & Ellis, 2004). Withthis consideration in mind we would be required to utilise someone within the organisation as a trainer andmentor with the software and tools selected who would train the appropriate regional leaders. There is also aclear requirement that buy-in from the key members of the proposed project be gained in order to receivethe most from the knowledge and expertise they hold and have the best implementation of the toolsprovided in the prototype project. 8 Jess Maher | 3328773 .
  10. 10. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .• Project Development & Implementation CostsThe training cost for staff through the implementation of this project is only one aspect which must beconsidered, there are a number of other costs which must be also considered in order to complete a workingprototype of the potential utilisation of Microsoft Visual Studio tools, such as clustering and data mininganalysis. As it is assumed that a data warehouse will be utilised for the purposes of data retrieval for thisproject, there is no requirement for consideration of a data cleansing or consolidation, however, as thisproject utilises the services of software provided by Microsoft, GSS will need to arrange an appropriate licenceagreement to provide access to potential users of such systems. The Microsoft Visual Studio software can beexpensive to purchase however as multiple licences will be required by GSS in order to utilise such toolsthroughout regions and areas of the business, it is recommended that a package be sought out fromMicrosoft. For the purposes of this prototype project a trial version of Microsoft Visual Studio could beobtained free of charge from the Microsoft Website (http://office.microsoft.com/en-us/default.aspx).Feasibility Elements being testedThis prototype project has been designed to test the viability of potentially implementing the data miningtools (such as decision trees and cluster analysis) provided by Microsoft Visual Studio into GSS. There are anumber of areas of potential concern to GSS, the feasibility of which is intended to be considered through thedevelopment and execution of the prototype project. The areas of concern which feasibility will be consideredare outlined below; • Viability of data mining techniques selected (Can the right information be retrieved?) The prototype project will allow GSS to assess the feasibility of utilising decision tree and cluster analysis within regional units, this will be greatly assessed through the experiences of leaders to not only gain information from the data but utilise this within the business operations. • Potential benefit of information provided by data mining techniques to business units through implementation (Can the information be used?) The prototype project will allow GSS to assess the potential benefit received from information gained from analysis within the decision making and analysis of general business processes. Some additional training maybe required to assist leaders and executives in understanding how to utilise analysis to gain specific information required. • Viability of Microsoft Visual Studio as software to utilise data mining tools and techniques The prototype project will allow GSS to assess the softwares potential for application organisation wide, the assessment and feedback of key users within prototype project will greatly enrich the assessment made. • Appropriateness of Implementation for such tools and software The prototype project process, results and feedback of those involved will assist GSS assess the feasibility of the implementation approach taken to introduce such software and tools to the organisation as a whole. 9 Jess Maher | 3328773 .
  11. 11. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .Outline of Process • Determine the region, people and information which will specifically be utilised by the prototype project. • Work with regional managers and other members involved with prototype project to gain buy-in to project and utilise knowledge and expertise held in development and execution of project ensuring the most potential benefit is gained in the process. • Clearly define the variables for consideration based on the intended question or pattern being analysed. • Assess and define the best decision tree analysis algorithms and variables to discover trends and patterns within GSS business data and appropriate cluster analysis to support and build on information intended to gain in analysis. • Define and advise appropriate trial parties on required methods and procedure in order to gain beneficial results and utilise in staff training for project implementation. • Assess viability of results gained by regional unit from defined decision tree and cluster analysis completed as well as any other supporting assessments or investigations completed independently within the regional unit in order to determine viability of data mining tools themselves, investigations defined and process of implementation and training of prototype project. • Use information gained from prototype project and feedback and assessment of those involved to develop a proposed plan for the potential enterprise wide utilisation of data mining tools to assist different layers and areas of GSS to enable better decision making.Expected Outcomes:The defined algorithms, variables and analysis options recommended for use by the prototype projectregional unit is only intended as a guide. It is expected that benefit will be obtained in the decision analysistools utilised in this project, however potentially more benefit is expected from the use and exploration ofsuch regional leaders within this prototype. The knowledge and expertise held by members of theprocurement, sales and marketing and indeed all areas and functions of the business within GSS can providethe best basis for predicative modelling and data discovery. By implementing software such as MicrosoftVisual Studio, which provides a range of data mining tools and techniques, and providing adequateexplanation and training to key members, GSS can truly understand the potential benefit available throughimplementation of such tools enterprise wide. 10 Jess Maher | 3328773 .
  12. 12. INFOMGMT 393 Individual Assignment | Semester One, 2008 | Global Stationery Supplies .ReferencesAgosta, L., (2003, 1 Dec), Hub-and-Spoke Architecture Most Popuplar for Data Warehousing , DM Review,New York, Vol. 13 (12), Retrieved 18 April, 2008 from Proquest Computing databaseArtifical Neural Networks, (2008), Artifical Intellegence Technologies Tutorials, Retrieved 22 April, 2008 from;http://www.learnartificalneuralnetworks.com/Berry, M. J. A., Linoff, G., (2004), Data Mining Techniques: For Marketing, Sales & Customer RelationshipManagement , John Wiley & Sons, Indianapolis, pp 6-40Berson, A., Smith, S., Thearling, K., (1999), An Overview of Data Mining Techniques, Building Data MiningApplications for CRM, Retrieved 20 April, 2008 from;http://www.thearling.com/text/dmtechniques/dmtechniques.htmGarver, M. S., (2002, 16 Sep), Try New Data-Mining Techniques, Marketing News, Chicago, Vol 36 (19),Retrieved 20 April, 2008 from Proquest Computing databaseHsieh, C., Lin, B., (2003) Web Based data warehouseing: Current status and perspective , Journal of ComputerScience Information Systems, 43(02), Retrieved 18 April, 2008 from Proquest Computing databaseKing, T., (2006, 18 Sep), A Road Map for Decision Making: Data Warehouse Architecture , iNews: Planning,architecture & development, IST Division, UC Berlkey, Retrieved 16 April, 2008 from;http://istpub.berkeley.edu:4201/bcc/Fall2006/929.htmlTaylor, P., (1997, 21 June), A Head for Business: New Software can mimic human thought when processinginformation, Financial Post, Retrieved 21 April, 2008 from CBCA Business databaseThomas, E., (2004, Jan), Data Mining: Definations and Decision Tree Examples, Stony Brook State Universityof New York, Retrieved 20 April, 2008 from;http://airpo.binghamton.edu/conference/jan2004/Thomas_data_mining.pdfTurban, E., Aronson, J. E., Liang, T., Sharda, R., (2007), Decision Support and Business Intellegence Systems,Eighth Edition, Pearson Prentice Hall, New Jersey, pp. 206-244, 302-337Wikipedia, (2008), Neural Network Software, Retrieved 22 April, 2008 from;http://www.en.wikipedia.org/wiki/Neural_network_softwareWikipedia, (2008, 11 April), Cluster Analysis, Retrieved 21 April, 2008 from;http://www.en.wikipedia.org/wiki/Data_clusteringWilliams, N., (1994, Mar), Data Mining with Neural Networks, Insurance Systems Bulletin, 9(7), Retrieved 21April, 2008 from ABI/INFORM Global Database 11 Jess Maher | 3328773 .

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