Overview of AuMake International Limited
For this particular assignment, the chosen organization is Aumake International limited that is one of the listed companies in Australian stock exchange operating in retail sector. It is a retail organization that creates a platform for connecting the suppliers of Australia with the consumers of China. Such connectivity is created via the Aumake stores that are located in Sydney New South Wales and online commerce e stores. Aumake not only connects with growing and significant tourist market and diagou in China but also offers a full service retail experience having a range of products of Australia across the four main categories that involves skin, body care and cosmetics, healthcare, dairy products, wool and leather products and baby food including infant formula (aumake.com.au 2018). The valuable factor offered by the company is one stop shopping that involves option of multiple payments, knowledgeable bilingual staffs and an in store logistic store for product delivery to China and anywhere across the world. Some of the multiple payments option involves Alipay, Wechat and Unionpay. Some of the brands operating under this organization are healthessence Australia, UGC Australia and Jumbuck UGG original (aumake.com.au 2018). The retail model of organization is based on six pillars such as warehouse showrooms, daigou hubs, retail store network, flagship stores, Aumake owned brand and Aumake online. This particular organization is dynamically connected with the information age and is using different platforms for payment options to be actively online.
AuMake International limited analysis in terms of privacy and security policy statements is available on the websites and has been mentioned in detail in this section. The security of data and governance of privacy is addressed using nine core principles Australian data governance draft code of practice. The safekeeping and data privacy of AuMake can be further studied by the nine main principles that are examined below:
““No harm rule” – In this underlined rule, use of best endeavours is done using this reporting entity for ensuring that disclosure, collection or use of personal information of individual does not intends to cause harm to subject individual. In addition to this, entity is acting with integrity such that usage of data is not regarded to be unethical. No personal information should be disclosed to third party and circumstances of collection of data should not be exploited. In regard to AuMake International limited, reasonable steps are taken for ensuring that these they are bounded by privacy obligations and confidentiality in relation to protecting the personal information. Some non-personally identifiable information is expressively collected by each visitor, however the information is not limited to version, browsing type, pages viewed, operating system, referring websites address and page access times. Information collected serves the purpose of gauging trends, visitor traffic and delivering personalized contents.
Privacy and Security Policy Statements of AuMake
Honesty and transparency- Under this principle, it is required by organization to act with honesty when using, collecting and disclosure of data. It must be ensured by organization that such data are collected and disclosed according to privacy policy and privacy notification statement. Any enquiry about the personal information should be made using an easy and clear accessible mechanism for individuals. Efforts must be taken by organization for raising awareness, promoting and encouraging codes adoption when disclosing personal information from third parties. Collection of data by Aumake International due to change in policy will be done by adhering to the updated practices. The customer information can be accessed for unanticipated use from time to time that might not be disclosed in privacy notice. Customer support features of company required customer to submit their personal identifiable information. Such information cannot be only restrained to log in details whether it is username/ password and providing any sensitive information for recovery of lost password. AuMake is bound by the privacy act 1988 and is committed to provide best possible customer experience. Sometimes, company hire other companies for offering services on their behalf and they are permitted to access only personal information’s they require for delivering services.
Fairness- The fairness of using collected and disclosing the personal information should be done by taking into account the factors such as reasonable expectations of community regarding use of personal information and risk of harm to subject individual. It should also determine the appropriate of time for which the retaining of personal information should be done. Moreover, the private information needs to be collected from the individual for the purpose of predictable production the company. The right to accessing of personal information by Aumake international limited is subjected to exceptions as permitted by privacy to law. It can be said that organization is not maintaining complete fairness as the information of customers can be used for some unanticipated use and they are not disclosed in notes to privacy. Therefore, it is required by AuMake to practice fairness and mention the same in the privacy notification statement.
Choice – AuMake international should ensure that there is properly developed mechanism that will help in proving choice for use and collection of personal information. However, the principle of personal information to which organization is currently aligning does not incorporate the facts that are listed in the principles of code of practice. The individual consent is important to re identify the external data that not only holds the private and sensitive information for the individual but also the same has been identified by the law (Shehata 2015).
Nine Core Principles of Australian Data Governance
Accuracy and Admission- In this underlined rule, use of reasonable steps are ensured for the data using this reporting entity so that it is not misleading or inaccurate. Adoption and development of industry standards should be encouraged so that it will help in effective implementation of code. The principle of use of personal information of AuMake International intends to collect the information personally and delivering personalized content to customers when they are at site (Thompson et al. 2015). In addition to this, mechanism should be ensured by organization that would provide choice for using and collecting personal information that are understandable and easily accessible to subject individuals.
Safety, security and de identification- AuMake under this principle should organize and design its security according to the recognized industry standards that would be suitable fro addressing any harms resulting from breach of security. Moreover, a trusted individual is required and should be nominated that withholds the accountability of data security that is binding in nature. The data sets collected for the storage of multiple aligned data sets should be taken care as of private and sensitive information unless the techniques measures of de identifications and security assessment are identified by organization. In addition to this, the procedure of “de identification” should be considered vigorous for the revision and testing of the techniques on a regular interval with reference to the recognized standards of industry (Marshall 2016). Organization should also seek to undertake reviewing of industry standards for de identification.
Accountability- This principle guide’s organization to maintain and create catalogue classes of private knowledge such that this information holds the disclosure of involvement to third parties and the way it is further used in the system, gathered and unveiled. AuMake takes rational steps that are bounced by certain obligations discretion in relation to private information. However, in case of unveiling of information to other third parties, it is possibly ensured by privacy obligations and service. In regard to accountability of personal information of customers, personal information is provided to the third parties when they are required to deliver service. Since, AuMake is required to hire third parties for providing service to customers and such services are not limited to processing transactions, handling enquiries of customer support and customer freight shipping and processing transactions. The doctrine in codes is often guaranteed with the response in dealing with third parties that are assembled b the company itself (Cuomo et al. 2016). In order to ensure compliance with the code and demonstrate the commitment, there must be a clear and publicly available statement indicating that there is strict adherence with the established code of conduct.
Importance of Transparency, Fairness and Stewardship
Stewardship- While reviewing the data security and privacy policy of Aumake International limited, it can be inferred that there is no mentioning of stewardship principle. Therefore, it is required by organization to implement this principle that would require them to appoint a relevant officer for creating compliance with this particular code. An appropriate internal process should be developed and implemented that would help in ensuring and supporting compliance with the code (Datagovernanceaus.com.au 2018). Furthermore, it should also be ensured that adequate training is provided to employees regarding the handling practices of data so that wide compliance with the code is promoted.
Enforcement- This particular principle regarding the compliance with this respective code must be enforced by organization from time to time. The guideline issued by the code should be appropriately implemented. Enforcement of the codes should be properly agreed by the organization as it is pursuant to the code authority charter as being a condition of membership draft code exercise of data governance.
The diabetes data with EDA analysis under rapid miner studio is primarily required in understanding the way to fit and build the complete process. The course of carrying out EDA is started with importing data in rapid miner and save it in local storage. Once the data is stored, the very next step is to drag the dataset in process layout section and connect it with output port. After this, once the run button is pressed, rapid miner will return the dataset as well as basic statistics in summarized form as mentioned.
Cleaning process that is the first part of the analysis is performed on the given raw data set, which is the diabetes dataset for this study. The study of the dataset involves one dependent and other eight independent variables. However, as per the given study, it can be suggested that no all variables can be taken in consideration to predict the dependent variable (Perveen et al. 2016). According to the analysis, a set of 5 to 6 variables are adequate to predict the dependent variables, in this case, the outcome, that is, prediction of diabetes.
The analyst has performed three step analyses, to identify those top 5 variables that are adequate to predict diabetes of a patient. There are various research works that have shown that age, insulin amount, glucose level in body, blood pressure, BMI and others are main characteristics to determine whether there is a chance of diabetes or not (Iyer et al. 2015). All these variables were included and major 5 variables were undertaken for the analysis by the analyst. Nevertheless, the analyst as the primary step tried to identify those variables was plotting scatter diagrams. Below are the scatter plots which in this case does not shed effective light. However, considering all these scatter plots, it can be assumed that age, BMI, insulin, glucose level, etc were keys to predict diabetes.
Enforcement of Policies
Since, plottting scatter diagram did not provide clear indication, the analyst performed correlation study as revealed as given under. The basis of correlation analysis indicates tha the all the independet variables are positively linked with the product variable. On the other hand, the result even illustrates the detail that skin thickness and BP are showinng positive yet weak and negligient positive association with the response variable. Therefore, skin thickness and BP cannot be considered from the given list and rest 6 factors can be taken into consideration.
The analysis that had been carried out further is on 6 independent variables are efficient and effective. The test that suited for the analysis was chi square test which is one of the good responses on the analysis of the categorical variable. The underlined figure further quantifies the study showing weight of chi square statistic for individual variables. The analysis as stated above does not possess two variables that is mainly BP and skin thickness but further highlights the elimination of pregnancies variable. Hence, the analyst resulted in depicting that diabetes level can be predicted by 5 key variables.
Once the identification process is carried, the chances of diabetes and its prediction is the another step aligned to carry the analysis forward. To further examine, a decision has been constructed but this decision tree made had some modifications to be sustained for the existing data set and operators and the changes in set or conversion to nominal scale, etc. Finally, the “Decision tree” operator was built and analysed in the process.
This decision tree begins with the glucose level and chances to depict the predictability of diabetes (Kalaiselvi and Nasira, 2015). According to this, if the glucose level is higher or same as 166.5, then there is a chance that a patient will have diabetes. On the other hand, a score below 166.5 need further analyses of rest variables. Now, this even explains that any score lower to 154.5 highlights the results to “false” with no chances of the presence of diabetes. In this way, the decision tree helped to analyse the prediction of diabetes.
As per the given study, logistic regression is even underlined. The analyst used weka extension in rapid miner to perform this logistic regression analysis. Likewise the decision tree process, here also a list of operators were incorporated into the process as shown in below picture. Further, the result figure is showing coefficients and odd ratios. In relation to the study of odds ratio which is incidence of result in the existence of a specific exposure, with the incidence of result in the nonexistence of a specific exposure. The results highlight that value > 1 gives direct relationship whereas value < 1 gives inverse relationship. Hence, from the end result it can be further stated that the insulin affects diabetes directly. In other words, increase in insulin level must increase the diabetes odds in the model.
The processes underlined give the possibility on the close by prediction based on the occurrence of diabetes with reference to five explanatory variables. However, in this section, the analyst has performed a comparison of performances of both models. In order to do so, the analyst has revised both decision tree and logistic regression models and incorporated few additional operators such as Binominal Classification Operator; Cross validation or Apply Model Operator in various ending process models of data mining (Roiger, 2017). Further, as output, numerous performance matrices like Sensitivity, Miscalculation Rate, Accuracy, True and False Positive Rates, (AUC), Precision, F measure, etc. were considered.
Besides this, a table is depicted a comparison between the model are shown models and comparison can be made on which model surpassing the other in the study.
The comparison analysis in the table above is based on those selected performances matrices. According to this, logistic regression is giving better prediction than decision tree analysis (Kotu and Deshpande, 2014). The reason is that in most cases, like, true and false prediction recall, sensitivity, etc. the percentage is high than decision tree analysis. Hence, it can be concluded that logistic regression will be the first preference on the diabetes chances.
The graphical depiction shows the affect of wildlife hits with aircraft over time for Arkansas. This indicates that most of the cases, the impact is none, mean the aircraft run the journey without any issue. However, the graph is also indicating that there were significant numbers of precautionary landing made by the aircraft as there were many wildlife hits. The trend has been same on the changes in the state origin across all the states.
The study is analysed with the use of Tableau and screenshot as “Tableau vieew’ is given below in which the first time period of occurrence of aircraft with response to wildlife hits is highlighted. The approach phase during day and night time had maximum wildlife hits/ strikes. The take off phase and landing roll after the approach phase had second maximum strikes during the day.
The depiction of tableau result given under processes analyses the strike frequency of aircraft in response to the occurrence of the chances of damage is depicted. The maximum strikes have been caused by unknown bird with the maximum damage of $69,54,217 as illustrated.
The Geo map view generated by Tableau portrays the cost incurred by each orgin state because of wildlife hits in changed time periods. Based on the results, the maximum number of wildlife hits has been observed by California over different time periods. This graph is showing the status of all these states of United Nation only. Data of states outside United Nation is ignored using the tableau filter option.
A dashboard designing on account of analysis and understanding of the information provided on the visualisation of the data. Hence, a draft of key variables needs be executed that are revealed graphically makes the dashboard (Kale and Balan, 2016). Also, presentation of dashboard does not hold that importance because of the display of a key concept (Fezarudin et al. 2017). The study of designing a dashboard is based on the graphical representation with view of different aspects that were presented in each section so that comprehension of the study using dashboard is provided. Taken for example, if the third graph is taken into account, where it was asked to evaluate chances of occurrences of damages with frequency of aircraft strike with wildlife species. This information the analyst could have visualise using text table format. However, using such option was not a great idea, as the viewer had to use scroll down option to cross check maximum number of spices information. Unlike this text view table, use of hit map has shown all these information with clear orientation.
References
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