MIS609 Data Management and Analytics Case Study 3 Sample
Assessment Task
For this assignment, you are required to write a 2000-word case study report proposing data management solutions for the organisation presented in the case scenario.
Context
Module 5 and 6 explored the fundamentals of data management. This assignment gives you the opportunity to make use of these concepts and propose a data management solution for the organisation presented in the case scenario.
Assessment Instructions
1. Read the case scenario provided in the assessment area.
2. Write a 2000-word enterprise data management solution for the company
3. The solution should discuss how it helps the company to solve the technical or operational complexity of handling data.
Eg1: problem of securely maintaining customer data can be solved by implementing data security practises, setting up a security framework that establishes right users to get access to the data, continuous audit will help to monitor any malpractice etc.
Eg2: poor data quality issues can be solved by implementing data quality measures
4. Remember not to deep dive into any topics, the solution is more at a conceptual level
5. Please address the below areas
• Identifying the business requirements and existing issues in data operations (explain techniques used collecting requirements)
• Data management operations relating to the various kinds of data that the company deals with.
• Data Architecture (provide example of a proposed architecture that will help in processing the data e.g. ETL (data warehousing or cloud solution)
• Data quality measures
• Metadata management
• Handling legacy data - Data migration
• Data archival
• Data governance measures
• Data privacy
• Expected benefits
6. The areas listed above are indicative and are in no sequence. When addressing this in the solution, please ensure you write in an orderly fashion. Also, any other data management areas not listed above can also be covered.
7. You are strongly advised to read the rubric, which is an evaluation guide with criteria for grading your assignment.
Solution
Introduction
Proper handling of information is critical for organizations to stay successful and make educated decisions in today's data-driven environment. This paper attempts to deliver a holistic data management strategy for a retail bank, covering stated business objectives as well as current information operations concerns. Uni Assignment Help, Data architecture, quality of data regulations metadata management, managing historical data, data archival, information governance regulations and information security will all be covered in the paper. The work will also illustrate the anticipated benefits of using an enterprise information management system.
Identifying Business Requirements and Existing Issues in Data Operations
Several strategies may be used to determine the retail bank's business requirements and current challenges in the processing of data. These methods will aid in gathering data from important parties and comprehending the present situation of the organisation.
• Interviews: Stakeholder evaluations, such as those with the Chief Technology Officer, company executives, or data analysts, can give important perspectives into current difficulties and requirements. Based on the demands of the bank, these interviews might be planned or unplanned.
• Questionnaires: Sending out questionnaires to key participants can aid in the collection of both qualitative and quantitative data regarding the present condition of data processing. Questionnaires can be created to collect data regarding data management difficulties, system limits, and intended results.
• Workshops: Facilitating collaborative talks and brainstorming meetings with important players may be facilitated by organising seminars. These seminars can concentrate on particular information operations themes like quality of data, data governance, or confidentiality of data (Eryurek et al., 2021). The financial institution may collect needs and discover pain areas through these seminars.
• Documentation Analysis: Examining existing records, like system documents, data glossaries, and procedure papers, can reveal information about current information processes. This study can aid in the identification of gaps, discrepancies, and opportunities for enhancement.
In the instance of a retail bank, these strategies may be used to collect data regarding the present condition of data processes. Interviews with the CTO, company executives, and data scientists could shed light on the bank's difficulties, such as obsolete structures, bad quality data, & an absence of oversight and safety precautions (Cedersund, 2023). To acquire input on data-related challenges and demands, questionnaires can be issued to several participants, including bank staff and consumers. Seminars may be organised to bring key players together to discuss particular data management difficulties that the bank is facing. Analysis of documents may also be used to examine current documentation and find opportunities for data operations enhancement.
Using these strategies, the bank may acquire a thorough knowledge of the company's needs and current information operations difficulties, which can be used as a framework for designing an effective data handling strategy.
Data management operations
• Customer data, information regarding transactions, dispute data, plus operational information are all handled by the retail bank. To maintain the integrity of information, connectivity, and usefulness, efficient data-management procedures are required. This may be accomplished by using data integration, cleaning, alteration, & storage procedures.
• Data Integration: The integration of information is the process of combining data from several sources into a cohesive perspective. This may be accomplished by utilising Extract, Transform, Load (ETL) methods, in which data is retrieved from many systems, converted into an accepted form, as well as imported into a centralised data warehouse (Mokhamad Hendayun et al., 2021).
• Records Cleansing: The act of finding and fixing mistakes, discrepancies & duplication in data is known as data cleansing. To increase data quality, procedures like data profiling, validation of information, as well as information standardisation can be applied.
• Data Retention: A comprehensive data storage answer, like a warehouse of data or a cloud-based storage framework, can be implemented by the retail bank. A warehouse of data is a centralised warehouse for organising and storing information, allowing for quick data extraction & analytics. Cloud-based storage systems provide reliability, adaptability, & cost-effectiveness, enabling a financial institution to cope with an expanding quantity of data while also adapting to evolving business demands.
Data Architecture
A retail bank's suggested data architecture might include a mix of data warehouses & cloud options. This design will aid in processing information efficiency while also providing flexibility and scaling capacity for future enhancements.
• Data Warehousing: As a central repository for keeping and organising data, the financial institution can build a data centre. Based on the financial institution's needs, the data repository could be created using a schema based on stars or a snowflake structure. This will make it possible for more efficient monitoring & data analysis, plus complicated searches and data extraction.
• ETL (Extract, Transform, Load): the financial institution can employ ETL operations to create the data store. Data may be retrieved from a variety of sources like transactional networks, client databases, as well as grievance management platforms. • Once the retrieved data has been translated into a uniform format, it may be used to ensure data integration as well as integrity. The modified data may then be placed into the data repository.
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Figure 1: ETL Procedure
(Techcanvass , 2021)
• Cloud Solution: For data handling and storage, the retail banking may want to investigate using solutions that are cloud-based. Flexibility & affordability of cloud-based services enable the bank to manage massive amounts of data while adapting to changing business demands. Cloud-based applications also have integrated safety and recovery from disasters features.
Retail banks may profit from greater data processing features, more data availability, & lower infrastructure expenses by implementing a data warehousing or cloud-based platform. This design will allow the bank to manage increasing amounts of information in a cost-effective way, facilitate sophisticated data analysis, and offer an adaptable structure for future expansion.
Metadata management
Metadata management is the capture, organisation, and maintenance of metadata that offers insight and context to information. To record information sources, data descriptions, data history, and information linkages, the retail bank might use management of metadata techniques and tools.
The following are some of the advantages of integrating the handling of metadata in a retail bank:
• Better Data comprehension: Metadata management improves data comprehension by recording sources of information, descriptions, & linkages. This allows customers to quickly find and obtain the data they want for evaluation and choice-making (Warin & Stojkov, 2021).
• Improved Data Governance: Metadata administration supports the oversight of data activities by giving transparency into the data lifespan and maintaining complying with regulations. It aids with data lineage tracing, identifying data possession, & maintaining data quality requirements. This results in enhanced data governance and greater authority over the assets of data.
• Improved Productivity and Efficacy: With an organised metadata storage facility, employees can rapidly find and comprehend the info they want, minimising the time needed looking for information. This boosts efficiency & helps people concentrate on valuable duties like analysing and making choices instead of spending time on information finding and comprehension.
Data Archival
The practise of shifting inactive or less often accessible data from main storage to secondary storage for long-term retention is known as data archiving. This aids in the optimisation of storage resources and the enhancement of system performance. To handle an increasing amount of data and assure effective data storage as well as extraction, the retail bank might employ data archive solutions. Data that is no longer actively utilised can be archived, freeing up precious storage space & lowering the expenses related to preserving big data quantities (Näktergal, 2021).
Data Quality Measures
Data quality assurance is critical for trustworthy making choices and effective data operations. To increase the correctness, accuracy, uniformity, as well as dependability of its information, the retail the financial institution might apply a variety of data quality assurance initiatives.
• Data Profiling: Information profiling is the process of examining the structure and content of information in order to find discrepancies abnormalities, and problems with data quality. The financial institution may acquire knowledge about the reliability of its information and find areas for development by doing data profiling.
• Data cleaning is the process of finding and fixing mistakes, contradictions, and duplication in data. To increase data quality, procedures like verification of data, standardisation, and augmentation could be applied. Relying on the bank's needs, cleaning data activities might be computerised or handled manually.
• Data Regulation: The total administration of data excellence, comprising the implementation of rules, processes, and safeguards to assure data integrity & uniformity is referred to as data governance. The financial institution may assure continuing data integrity enhancement by employing data governance mechanisms such as data stewardship responsibilities, data quality guidelines, and information quality checks.
The retail bank may increase the confidence and reliability of its information by applying these information quality standards, resulting in more precise disclosure, improved decision-making, and greater satisfied clients.
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Figure 2: Strategy for successful data management
(Subramaniam, 2022)
Handling legacy data
Handling historical data and data transfer is a critical component of deploying a retail bank information management solution. Legacy data is data that has been preserved in obsolete systems or representations and must be transferred to a fresh platform or style. To manage historical data movement, the retail bank might use the following method:
• Information Mapping: The process of mapping data entails determining the linkages and connections between old information and the desired system. This involves integrating legacy data areas, forms of data, and info architectures to the new system. Data mapping guarantees that the transferred data is suitably matched with the needs of the newly installed system.
• Execution of Data Migration: After the data visualisation, cleaning, and conversion operations are completed, the real-world data migration may begin. The data is extracted from the old system, transformed as per visualisation criteria, and loaded into the newly implemented system. To guarantee data quality and integrity, extensive examination and verification must be performed during the transfer of data (Nelito, 2019).
• Validation of Data and Inspection: Once the data has been migrated, it is critical to check and verify it to guarantee its fullness and correctness. This might include comparing transferred data to old data, doing data quality examinations, and executing validation operations.
Retail bank can efficiently handle old records and guarantee a seamless and effective data transfer procedure by adopting these procedures.
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Figure 3: Secure data movement
(Näktergal, 2021)
Data Governance Measures
Data governance is the entire management of a company's data assets. This entails developing policies, processes, as well as controls to assure the accuracy, protection, and conformity of data. Data governance methods may be implemented by the retail bank to set out duties and obligations for handling data, create data quality regulations, and enact confidentiality and safety of data regulations. Data governance contributes to better data integrity, legal compliance, & comprehensive management of data practises.
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Figure 4: Data Oversight
(Gaur, 2022)
Data Privacy
The safeguarding of sensitive and personally identifiable information against unauthorised access, utilisation, or exposure is referred to as data security. To comply with privacy requirements and secure client data, the retail bank must deploy data privacy procedures. Integrating limits on access, encryption mechanisms, along with privacy procedures are examples of this. Security of information measures aid in the development of customer confidence, the reduction of the danger of data violates, and the observance of confidentiality regulations.
Expected Benefits
Using an enterprise system for managing data in the retail banking sector can provide various advantages:
• Increased Data Quality: The financial institution can enhance the accuracy and dependability of its data by using data management practises like data cleaning, validation of information, and data administration. This results in more accurate reporting, more informed making choices, and greater satisfied customers (Garg et al., 2021).
• Legislative Compliance: Putting in place information oversight procedures ensures that regulatory obligations, like confidentiality regulations, are met. This assists the bank in avoiding sanctions & negative press connected with infringement.
• Financial Savings: The bank may save money on storage facilities and upkeep by optimising data storage, employing data archiving techniques, & decreasing data redundancy.
• Improved client Experience: By implementing enhanced data management practises, the financial institution will be able to deliver a more personalised and smoother customer service. It involves enhanced data quality and faster reaction times to client concerns.
Conclusion
Finally, deploying an enterprise data administration system in bank will offer various advantages. This will increase the accuracy of data, effectiveness in operation, adherence to regulations, monetary savings, & customer satisfaction as a whole. The bank may simplify its data processes and prioritise emerging markets by resolving existing data-related issues such as obsolete technology, low quality data, particularly an absence of accountability. It will eventually result in enhanced client loyalty, improved choice-making, & an economic competitive edge.
References
Cedersund, M. (2023). Artificial Intelligence in banking: the future of the banking work environment. Pp. 1-193. https://www.theseus.fi/bitstream/handle/10024/803304/Cedersund_Michel.pdf?sequence=2
Eryurek, E., Gilad, U., Lakshmanan, V., Kibunguchy-Grant, A., & Ashdown, J. (2021). Data Governance: The Definitive Guide. " O'Reilly Media, Inc.". https://books.google.co.uk/books?hl=en&lr=&id=jQYiEAAAQBAJ&oi=fnd&pg=PP1&dq=holistic+data+management+strategy+for+a+retail+bank,&ots=jHtLa3P2iW&sig=LRITSjOu-gAhhCmieRr5IdWEkJ8#v=onepage&q&f=false
Garg, P., Gupta, B., Chauhan, A. K., Sivarajah, U., Gupta, S., & Modgil, S. (2021). Measuring the perceived benefits of implementing blockchain technology in the banking sector. Technological forecasting and social change, 163, 120407. https://doi.org/10.1016/j.techfore.2020.120407
Gaur, C. (2022, December 20). Data Governance Tools, Benefits and Best Practices. Xenonstack.com; Xenonstack Inc. https://www.xenonstack.com/insights/big-data-governance
Mokhamad Hendayun, Yulianto, E., Jack Febrian Rusdi, Setiawan, A., & Benie Ilman. (2021). Extract transform load process in banking reporting system. 8, 101260–101260. https://doi.org/10.1016/j.mex.2021.101260
Näktergal. (2021, June 3). Secure data migration and decommissioning legacy systems in banks - Näktergal. Näktergal . https://naktergal.tech/blog/data-migration/
Nelito. (2019, September 4). Data Archival Compliance for Banks & Finance Institutions. Nelito.com. https://www.nelito.com/blog/data-archival-compliance-for-banks-finance-institutions.html
Subramaniam, A. (2022, July 12). How to Build a Robust Data Modernization Strategy Roadmap for Banking & Financial Services. KANINI. https://kanini.com/blog/data-and-analytics/data-strategy-roadmap-for-banks-and-financial-services-organizations/
Techcanvass (2021, April 22). What is ETL (Extract, Transform, Load)? Techcanvass . https://businessanalyst.techcanvass.com/etl-extarct-transform-load/
Warin, T., & Stojkov, A. (2021). Machine learning in finance: a metadata-based systematic review of the literature. Journal of Risk and Financial Management, 14(7), pp. 1-31. https://doi.org/10.3390/jrfm14070302
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