DATA MANAGEMENT SOLUTION
Table of Contents
Business requirements and existing issues in data operations. 3
Handling legacy data – Data migration. 6
Case 2: Retail bank in Australia. 8
Business requirements and existing issues in data operations. 8
Handling legacy data – Data migration. 9
Introduction
Enterprise data management mainly refers to the ability of the organisation to precisely define and easily integrate as well as retrieve the data from internal applications and external communication. As per the views of Gendron et al. (2021), it is basically focused on the creation of accurate and consistent as well as transparent processes to help the company with accurate inventory and governing business data and getting the business organisation with the process. This report mainly focuses on implementing enterprise deter management solutions in eCommerce business and retail banking businesses, including Data architecture and quality measurement. Finally, expected benefits also have been provided in this report.
Case 1: eCommerce company
Business requirements and existing issues in data operations
After analysing the data of eCommerce companies, it has been identified that the company is struggling to manage a load of customer requirements and product inventory as well leads to customer dissatisfaction. The company is also using an Excel sheet which does not contain proper ownership and maintenance of the data, and all these systems are not properly managed. Right now, it is very much important to organise the data for the vice president of the organisation, and he needs a proper deter management solution for this eCommerce business. It will be very much important for the business to need to implement enterprise deter management solutions that majorly incorporate proper Data architecture and advanced analytics as well as cloud management. It will be best for an E-Commerce business to implement business process management as a part of EDM, and third-party product implementation will also give further benefits to the business.
Data management operations
As an E-Commerce business, the company is mainly dealing with customer data and product inventory data, as well as shipping data. As per the views of Du, Liu & Zhang (2019), customer data mainly identify the customer’s accounts and their order as well as the ratings and review the customers give based on the situations. Product inventory-related data are mainly referring to the warehouse inventory and the sales of products as well as the shipping of products within the prompt time. Finally, customer satisfaction gets measured through the implementation of a customer satisfaction rate and analysis of customer ratings and reviews on the website of an e-commerce business.
Data Architecture
In this case, the implementation of cloud solutions will be very important, and the company will easily obtain and store the customer’s data without having any problems. The case study also identifies the use of Excel sheets without proper ownership and maintenance of the data. This data storage interface in the cloud is very important, and there are multiple storage locations present such as object storage and XAM client as well as NFS and CIFS storage (Yeung, Wong & Tam, 2021). This will eliminate the further use of Excel sheets for storing the customer’s data, and as it contains multiple and popular interfaces operating it in The E-Commerce business environment will be fruitful. Finally, it will also give the benefits of managing the data anywhere in the world without keeping high offline storage or service systems.
Figure 1: Proposed data architecture in the cloud solution
(Source:Yeung, Wong & Tam, 2021)
Data quality measurements
For effectively measuring the data quality that is multiple quality metrics and software tools are present. For measuring the eCommerce business, a data ladder will be used, which is one of the leading data quality management tools containing flexible architecture and a wide array of tools for cleaning and standardising data storage systems (Vertakova,Mkrtchyan & Leontyev,2019). This solution contains informatics master Data management and has a precise trillium strategy which represents the graphical data for quality of the data, and it integrates as well as cleans the data in a precise way.
Metadata management
Metadata management can be explained as a process that allows managing metadata associated with data. It ensures better accessibility of data; however, currently, the ecommerce service provider is dealing with the challenges of data findability and consumer data and inventory data has become excessive to manage. Metadata is used for analytics, operation and compliance; thus, poor met data management impairs these data management operations also (Paik et al. 2019). This organisation can consider implementing a metadata management strategy in which met data collected can be stored as well as following metadata standards by following the enterprise data ecosystem. Thus this strategy can be beneficial for metadata management.
Handling legacy data – Data migration
Data migration can be explained as a process of sharing information, and currently, this e-commerce services provider has no systematic data migration process in terms of handling data legacy. Currently, there are issues with data accessibility, and the majority of data is managed through excel files which are highly vulnerable to data theft. Thus to solve this issue, this organisation can consider conducting live tests to identify areas of data breach and implementing strict measures. It became necessary for the organisation to explore and assess sources that allow well suitability within the targeted systems.
Data archival
Currently, the organisation has no archival data system, as data is stored in common data-based and managed to excel files that are highly vulnerable to data theft. It also leads to a lack of data accessibility as employees cannot access the excel data during remote access. The data management is very weak. Thus this organisation can incorporate interface and double privacy as well as firewall systems to protect data. The firewall helps to manage serious issues of data loss by limiting third-party accessibility.
Data governance measures
Data governance measures become necessary for this organisation as the data collected in the database has been highly vulnerable to data theft. A data governance strategy can be beneficial for this organisation. There are four stages for implementing data governance; these include making data available to others, both structured and unstructured data (Al-Ruthe, Benkhelifa & Hameed, 2019). Further, maintaining data consistency and accuracy fall under falls under data governance activities. Further, there is a need to support data security under data governance measures. It can help to foster data privacy as well as data management issues faced by the organisation.
Data privacy
As currently, the data is stored in excel and other files; thus, there are high chances of data theft, and personal consumer data can be on the verge of serious confidentiality issues. Thus as data privacy measures, this organisation can consider auditing sensitive data and assess internal and external risks associated with data stores (Eilifsen et al., 2020). This organisation can strictly follow the data privacy strategy and security management activities such as limiting accessibility data. Transparency maintenance is also a serious concern for data privacy, and this organisation can consider transparency about the data collected by customers. It will help to improve the privacy of data collected by the company.
Expected benefits
3 Expected benefits associated with the strategies considered include better data governance and maintenance of privacy. Second, the confidentiality of consumer data can be maintained by considering these strategies. As a third benefit, this organisation will be able to manage the safe metadata transfer of data as well as customers’ trust by ensuring brand reputation regarding governance.
Case 2: Retail bank in Australia
Business requirements and existing issues in data operations
The Retail Bank of Australia is facing the issue of excessive time consumption for developing financial reports. Currently, the systems are outdated as this bank uses excel and oracle versions that are outdated. Poor data analysis and excessive time consumption for reports lead to significant customer dissatisfaction. Thus this organisation is needed to improve its IT infrastructure to meet customer demand and database management system through the inclusion of modern and updated techniques.
Data management operations
The data management operation of this organisation is needed to be extended, and an updated data management system through an upgraded IT management system is needed to be maintained. There are eight operations that can be considered by this bank to update its infrastructure to improve data management operations. These operations include architecture for defining the process of data collection and database administration of monitoring data (Diène et al., 2020). Integration highlights ensuring data is not accessible to vulnerabilities and security focus on preventing unauthorised access. On the other hand, modelling of data for identification of the type of data to be collected and quality management of data needed to be ensured. There is also a need to focus on analytics and governance to ensure updated data management operations.
Data Architecture
Data architecture is needed to be flexible and with updated IT infrastructure. Thus to modify the architecture, certain technologies can be implemented, including a NoSQL Database that allows speeding up data management and real-time streaming of data in the database. On the other hand, microservices can allow for achieving a flexible data architecture. In this context, bank micro-services will lead to faster data analysis and report generation.
Data quality measurements
For measuring the data quality, indicators will be used, which are mainly the descriptors used in the computer file system for recording the quality attributes of the data. As per the views of Deng et al. (2019), this is particularly useful, and this process uses time variables so that the settings can be determined with the value participating in computation and understand the computation proceeds for analysing the high data quality or low data quality.
Metadata management
For managing the media data, it will be very much important to improve the innovation and collaboration inside the business and also enable the data citizen to achieve high-quality and trusted data. As said by Sawadogo & Darmont (2021), combining this thing will ensure the incorporation of the right data for delivering accurate insights.
Handling legacy data – Data migration
For conducting the data migration or handling legacy data, it is very much important to go with a data migration verification tool. It will first check the database security and data integrity for different possible sample records (Calderón Godoy & González Pérez, 2018). Furthermore, it will help to check and ensure that all the data are earlier supported by the functionality inside The legacy system and just need to work as expected inside the new system.
Data archival
There are nearly five different data archival strategies present such as checking the inventory and determining the proper data that needs to be archived as soon as possible. It is also very much important properly compliance all the regulations assigned with the retention and all these retentions must need to be done in a schedule for a different category (Mkpojiogu et al. 2020). It is also very much important for businesses to develop all-inclusive archive policies and must need to implement proactive protection for the data archive integrity. It is also very much important properly choose the data that needs to be archived as soon as possible with the given product, and finally, the evaluation of internal processes and external storage is recommended for ensuring the data has been properly archived.
Data governance measures
For conducting successful data governance, it is important to improve the data quality scores and just need to implement data management standards and processes inside the business. It is also very much important to reduce the risk events and just need to identify the data rectification cost inside the business.
Data privacy
To ensure data privacy, it is important to properly establish backup and recovery options for the data. It is also important to set users’ permission by keeping strong passwords and ensuring encryption for every piece of data (Gürsoy et al., 2022). Running several tests in the science database for conducting and maintaining the confidentiality of data is also very much important properly ensure data privacy.
Expected benefits
It will be very much helpful for the bank to ensure the highest quality of data, and it will also further eliminates inefficient reporting and analytics and also help to maintain proper data quality, which will give benefit the business by concentrating on new business-building ideas rather than wasting time.
Conclusion
It can be concluded that it is very much important properly implement a data management solution as it not only helps to ensure the proper handling of data but also the confidentiality of data. It is also particularly beneficial for ensuring Swift’s management operation and a high level of customer satisfaction
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