Slow fashion is becoming a strong organization and developing strong brand awareness as a click-and-mortar business. The organization was initially responsible for producing garments through a brick-and-mortar store but started increasing its sales by introducing an online service for the customers. It is identified that a large number of customers tend to order for the products of the company through online platforms and it is thus very important for the management of the online data to ensure effective security of the sensitive pieces of information shared by the customers. This pre-proposal is developed to analyze the various methods that are important to manage the data on the online platforms for Slow fashion and ensure effective enhancement in business.
To ensure effective management of the online pieces of information for Slow Fashion a Customer management system must be developed that will ensure effective management of the vital pieces of information shared by the customers of the company. This is mainly because the customers are responsible for trusting the company with a range of sensitive information that can affect the company if not handled in a secure manner (Chen et al. 2019). The data managed by the company will involve the data related to the customers including their account details which are highly sensitive and must be managed with the use of significant security systems. To ensure effective security of this system the technology of blockchain must be used which will enable the analysis of every activity on the CRM system and detect any suspicious activity ensuring high security.
To ensure effective management of the system for Slow Fashion a team of five members will be required. These team members must be effective in ensuring proper management of the system and should be trained in the aspects of information technology and blockchain-related aspects. A developer will be required in the team who will be responsible for developing the programs and managing the back-end activities (Zhang et al. 2020). A design expert should also be present who will be responsible for designing the front-end. A blockchain expert must also be present who will ensure effective management of the security aspects. The other members in the team will involve a testing expert to ensure debugging of the system and a year leader who will be responsible for managing the activities of the team members.
To ensure effective management of the system the retailer must provide all the basic information that is related to the products and the database links that are responsible for storing the details of the customers (Saura, 2021). The retailer will be provided with a hard copy of the agreement regarding the system being developed and a sift copy of the system such that the retailer is also able to access the system in an effective manner. The general data management systems will mainly look like an online system that will allow a user to create an account and enable the customers to order their products and ensure secure payments. The system will also provide information regarding the delivery of the products and help the retailer to enhance the experience of the customers (Blazquez, and Domenech, 2018). The system will also ensure recording of the supplies and dispatches of the company such that the retailer is able to analyze the financial aspects related to sales in the online system.
To ensure effective development of data management policy the management team must identify the significant objectives of the system and develop data processes that are strong and secure. The team must also develop systems for the governance of data by selecting the right pieces of technology. Once the data policies including privacy, security, data quality, and access are selected, the policy will be ready for implementation (Miles, Huberman, and Saldaña, 2018). The implementation of the data management policy will involve the setting up of an online system that will be flexible and managed by the policies of data management being developed. This will also involve the setting of the significant responsibilities of various users of the system and ensure effective security for the management of sensitive data.
To ensure management of metadata the team of data experts must tend to ask the significant range of questions that are crucial for the pieces of information being managed. The second step of management will involve the identification of the various attributes and sources that are important for the retailer (Marcus, and Papaemmanouil, 2018). Once these attributes are identified the team will need to develop a range of strong protocols and ensure consistent management of these policies. To ensure management of data quality it is very important for the auditing of data profiles such that the reporting and monitoring of significant pieces of information can be effectively managed. This will help in the analysis of errors in the datasets and ensure the effective resolution of these errors. The process of data auditing is done in a number of steps. The first step of data auditing involves the planning of the audit process. The next steps of the process involve the analysis of the notifications and the arrangement of an opening meeting (Saiz-Rubio, and Rovira-Más, 2020). Once the meeting is complete the auditing process involves audit field activities which are followed by the drafting of an audit report and a closing meeting. The closing meeting helps to analyze the response of the audit and helps in the development of an auditing process. The collection of user information will be done with the help of an automated system that will enable every client to develop personal accounts by entering a significant range of personal information that will help to identify the customers in an effective manner.
To ensure effective management of the data the architecture of cloud storage will be used. It is identified that a number of cloud platforms tend to exist in the market and a suitable platform can be used to ensure effective management of the customer’s management system. The legacy data of the customers will be managed using a web-based tool that is used for archiving (Sansone et al. 2019). This archiving tool will be effective in ensuring effective management of the sensitive pieces of information shared by the customers of the company and also ensure high security. The risks of the data management system can be analyzed by detecting the errors and shortcomings of the system by the process of testing. Once the risk factors are analyzed the management team can ensure resolution of these risks by the actions like debugging which will help to resolve the error and ensure elimination of risks.
It is identified that the development of such a system will help the company to manage the clients in an effective manner and also ensure high security of the sensitive pieces of information (Rousi, and Laakso, 2020). This will help the company to ensure effective enhancement in the management of the online customer base and ensure an enhanced experience for the customers. The outsourcing of this activity is proved to be beneficial for the company as the cost for developing and managing the system by the team of consultancy will be significantly lower as compared to the cost of developing and managing the system by the use of internal workers. This is because the development and management of such an advanced and secure system will require a range of IT professionals who are experienced and will be able to ensure effective development and management of the system. To recruit such a team the company will have to develop a recruitment process which will tend to involve high cost as a number of candidates needs to be examined to ensure selection of the best team. Moreover, the company must also ensure the effective salary of the team members selected such that these people are able to work with high engagement and ensure management and up-gradation of the system (Huang et al. 2018). Moreover, the company will also require to invest in a significant range of systems such that the activities of the IT team can be enhanced. This is because the development and management of an advanced Dara management system will require an advanced range of technologies and devices that are expensive and will require high investment from the company.
Conclusion
Thus it can be concluded that a cloud-based data management system must be developed for the Slow Fashion company such that the customer relationship can be effectively managed. This system will enable the customers to ensure ordering the required products and will also enable secure payment options. The system will also 0rovide information regarding the delivery of the products. Moreover, the system will be managed by blockchain technologies such that high security can be provided for securing the sensitive pieces of information shared by the customers. This will help to enhance the experience of the customers and allow the company to increase the base of online customers such that the sales can be maximized. Moreover, the development of such a secure online system will also help to enhance the brand image of the company among the range of target customers.
Reference List
Blazquez, D. and Domenech, J., 2018. Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130, pp.99-113.
Chen, I.M.A., Chu, K., Palaniappan, K., Pillay, M., Ratner, A., Huang, J., Huntemann, M., Varghese, N., White, J.R., Seshadri, R. and Smirnova, T., 2019. IMG/M v. 5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic acids research, 47(D1), pp.D666-D677.
Huang, Y., Chen, Z.X., Tao, Y.U., Huang, X.Z. and Gu, X.F., 2018. Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17(9), pp.1915-1931.
Marcus, R. and Papaemmanouil, O., 2018, June. Deep reinforcement learning for join order enumeration. In Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (pp. 1-4).
Miles, M.B., Huberman, A.M. and Saldaña, J., 2018. Qualitative data analysis: A methods sourcebook. Sage publications.
Rousi, A.M. and Laakso, M., 2020. Journal research data sharing policies: a study of highly-cited journals in neuroscience, physics, and operations research. Scientometrics, 124(1), pp.131-152.
Saiz-Rubio, V. and Rovira-Más, F., 2020. From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2), p.207.
Sansone, S.A., McQuilton, P., Rocca-Serra, P., Gonzalez-Beltran, A., Izzo, M., Lister, A.L. and Thurston, M., 2019. FAIRsharing as a community approach to standards, repositories and policies. Nature biotechnology, 37(4), pp.358-367.
Saura, J.R., 2021. Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge, 6(2), pp.92-102.
Zhang, D., Gao, F., Jakovli?, I., Zou, H., Zhang, J., Li, W.X. and Wang, G.T., 2020. PhyloSuite: an integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies. Molecular ecology resources, 20(1), pp.348-355.
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