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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.


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.


Business Requirements and Existing Issues

The Status of the retail bank this time exposes severe issues brought on by out-of-date IT systems, which impede efficient data management and reduce client satisfaction. The bank has a sizable client base and a desire to grow even more, but it fails to manage new demands effectively. For Assignment Help, The problem is made worse by the dependence on uncoordinated technologies like Excel, Access DB, and outdated Oracle DB, which slows the generation of reports as a result of dispersed and inconsistent data. Additionally, the bank's capacity to respond quickly to problems and raise client satisfaction is hampered by insufficient data storage and management of consumer complaints. Modernisation, centralisation, and improvement of data processing are obviously necessary.

An complete business data management system is suggested to handle these problems. This approach ensures effective data management and integrity by replacing obsolete systems with contemporary database technology. The implementation of a centralised data repository will improve data quality and compliance when combined with strong data governance and security measures. Advanced analytics technologies may also help with the analysis and quicker response of consumer complaints (Orazalin, 2019, p.493). Business users may concentrate on innovation rather than mundane administration by automating manual data processes. Overall, the bank will be able to manage its expanding client base and update its IT environment thanks to this solution's simpler processes, increased customer happiness and increased business agility.

Data Management Operations

The retail bank is facing major difficulties with data management and its related operations given the status of the industry. The bank has a long history that dates back to the 1970s, has amassed a sizeable client base, and has a good reputation throughout Australia. The bank's quick client expansion has, however, resulted in operational inefficiencies brought on by outmoded IT systems and data management procedures. As a consequence of the current method's reliance on Excel sheets, Access databases, and outdated Oracle databases, data processing is fragmented, report production takes a long time, and data integrity is compromised.


Figure 1: Data Management Operations in Banks
Source: (Recode Solutions, 2022)

By year's end, the bank hopes to have one million customers, thus an updated and comprehensive data management system is required. The shortcomings in the present system make it difficult to handle consumer complaints effectively, resolve problems quickly, and make data-driven decisions. Furthermore, the problems are made worse by a lack of adequate data governance and security procedures. In order to enable business users to concentrate on strategic development rather of being bogged down by manual data activities, the Chief Technology Officer is aware of the necessity for a complete data management plan.

To address these concerns, implementing an enterprise data management solution is paramount. This solution will streamline data collection, storage, processing, and reporting, resulting in enhanced operational efficiency, improved customer satisfaction through faster complaint resolution, and better-informed decision-making (Reis et al., 2022, p.20). Additionally, the solution will establish robust data governance and security protocols, ensuring data quality, privacy, and compliance. Ultimately, the holistic approach aims to facilitate data-driven growth, enabling the bank to achieve its customer expansion goals while maintaining operational excellence and reputation.

Data Quality Measures

The current state of the retail bank highlights several data-related challenges that need to be addressed through an effective data management solution. The bank, with a long-standing reputation and an expanding customer base, is undergoing IT modernization to accommodate growth. However, the existing data infrastructure comprising Excel sheets, Access DB, and an outdated Oracle DB poses significant obstacles. These range from inefficient handling of customer requests and operations management to compromised customer satisfaction due to delayed complaint resolution. Generating reports from disorganized and disparate datasets is time-consuming, primarily due to data integrity issues stemming from poor data quality (Grimes et al., 2022, p.108).

There are several advantages to using an enterprise data management system. First, more accurate reporting and analytics will be possible because to enhanced data quality and integrity, which will allow for more informed choices. Second, by immediately resolving concerns, simplified procedures and effective data processing will raise customer satisfaction. Sensitive data will also be protected by the solution's governance and security safeguards, guaranteeing compliance with data protection laws. In the end, this solution will relieve business users of the stress of repetitive data administration duties and allow them to concentrate on strategic projects, promoting innovation and development.

Metadata Management

Metadata management is crucial for addressing the data-related challenges faced by the retail bank. Currently, the bank's operations are hindered by outdated systems and processes, leading to inefficiencies in managing customer data and operations. The bank's customer base is rapidly growing, and the use of Excel sheets, Access databases, and an old version of Oracle DB is causing data disarray and integrity issues. Due to scattered data sets and low data quality, it is difficult to provide timely reports for decision-making. Inadequate data storage and analytics skills also make it difficult for the bank to manage and report consumer concerns (Grimes et al., 2022, p.104).

It is crucial to establish a strong metadata management system in order to prevent these problems. In order to manage data definitions, linkages, and provenance across systems, a central repository must be established. With the help of this solution, data consistency, quality, and reporting and analytics will all be improved. Implementing governance and security measures will also improve compliance and data protection. The system will encourage innovation and development by automating data administration duties and allowing business users to concentrate on their main responsibilities. In the end, this strategy will speed up the processing of client complaints, increasing customer happiness and loyalty while also putting the bank in a position to easily meet its objective of one million customers by year’s end.

Data Governance Measures

A strong data governance policy has to be built to deal with these issues. Modern database systems will be used to centralise data storage, along with procedures for data integration and data quality implementation, as well as the establishment of distinct roles and responsibilities for data ownership. Data security measures should also be implemented to safeguard sensitive consumer information and guarantee compliance with relevant legislation, such as data privacy laws.

Figure 2: Types of Data Governance Measures
Source: (Nero, 2018, December 7)

The bank will profit in several ways by using an enterprise data management system. A faster response of client complaints will increase customer happiness and retention, which is the first benefit of streamlining data procedures. Second, more accurate reporting made possible by enhanced data integrity will lead to better decisions. Last but not least, the decreased human labour required for data administration chores would free up business users to concentrate on innovation and growth efforts, leading to the creating of new business possibilities and income.

Handling Legacy Data - Data Migration

For the bank’s modernization efforts and operational effectiveness, it is crucial to adopt a complete data management solution given the situation of the company today and the associated data-related problems. The bank's antiquated Oracle DB and legacy systems, such as Excel spreadsheets, Access databases, and Access databases, are unable to handle the growing client base and new demands. As a result, creating reports takes a lot of time, the data's quality is poor, and client satisfaction is suffering. A solid data transfer plan is suggested as a solution to these problems. In this approach, the current data from historical systems is moved to a more sophisticated and scalable platform, such a contemporary relational database or a cloud-based solution (Roskladka et al., 2019, p.15).

This method of data movement has several advantages. The bank would be able to manage the projected increase in clients with ease since it would first guarantee a smoother transfer to contemporary technology. Second, by consolidating data into a single repository, creating reports would be more quickly and cost-effectively. Additionally, increased data integrity would increase analytics' accuracy, resulting in better decision-making. In the end, the data transfer approach will lessen the load of legacy systems, allowing business users to concentrate on core duties, innovation, and customer-centric initiatives instead of being bogged down in manual data administration responsibilities. This transition to effective data management creates the groundwork for a bank that is more adaptable, responsive, and customer-focused.

Data Architecture

In response to the current challenges faced by the bank, a proposed enterprise data management solution aims to address data-related issues while supporting growth objectives. Given the bank's outdated systems and processes coupled with a growing customer base, a robust solution is imperative.

To streamline data operations, a modern data architecture is recommended, leveraging cloud-based technologies. This architecture involves Extract, Transform, Load (ETL) processes to efficiently collect data from sources like Excel sheets, Access DB, and Oracle DB. The data will then be cleansed, transformed, and loaded into a centralized cloud-based data warehouse. This repository ensures real-time access to accurate and consolidated data, thereby enhancing reporting efficiency and maintaining data integrity.

Figure 3: Data Architecture of Bank
Source: (Fernandez, 2019, August 20)

Benefits of this solution include improved operational efficiency, expedited decision-making, and elevated customer satisfaction. By automating data processes, bank employees can redirect their focus towards innovative business initiatives. Swift access to reliable data aids in promptly addressing customer complaints and boosting overall satisfaction. Furthermore, this data management approach establishes robust governance and security measures, mitigating risks associated with data mishandling. In conclusion, this holistic solution aligns with the bank's modernization goals and supports the CTO's vision of utilizing data for strategic growth (Grimes et al., 2022, p.171).

Data Privacy

One of the critical issues is the inability to manage customer complaints effectively, impeding the bank's goal of swift complaint resolution and improved customer satisfaction. The absence of a well-structured data storage mechanism for complaints, coupled with poor data quality, hinders insightful reporting and analytics. Furthermore, the lack of defined governance and security measures exposes the bank to potential risks associated with data mishandling. To address these challenges, implementing an enterprise data management solution is imperative. This solution would streamline data processes, centralize data storage, and enhance data quality through standardized practices. The integration of modern data management tools and technologies would enable efficient report generation and analytics, aiding decision-making processes. Moreover, by enforcing proper governance and security measures, the bank can ensure data privacy and mitigate potential breaches (La Torre et al., 2021, p.14)

Benefits of this data management solution include enhanced customer satisfaction through quicker complaint resolution, optimized operational efficiency, and improved data privacy and security. By liberating business users from tedious data management tasks, they can focus more on innovation and value creation, aligning with the Chief Technology Officer's vision for the bank's growth and modernization.

Data Archival

An enterprise data management system is essential to optimise operations and assure future scalability in response to the existing conditions of the company and the data-related difficulties encountered by the retail bank. Excel sheets, Access databases, and out-of-date versions of Oracle DB are just a few of the systems and procedures the bank now uses that make it difficult to handle data effectively and keep up with the growth in its client base. This leads to operational inefficiencies and a poor response to client concerns.

These problems will be solved and a number of advantages will result from using an extensive data management system. First, a crucial part of this approach will be data archiving. The bank may free up space on its current systems and improve the speed and responsiveness of those systems by preserving past client data and transaction records. Second, centralised data integration and storage will enhance data integrity and quality, allowing for more rapid and precise reporting. Furthermore, clear governance and security policies will guarantee data compliance and protect sensitive data. Overall, this solution will relieve business users of data administration duties so they can concentrate on core company responsibilities and innovation. Additionally, increased complaint resolution and operational efficiency will increase customer satisfactions.

Expected Benefits

The first step of the strategy is to centralize data storage and switch from outdated Oracle DB and separate systems like excel and Access DB to a more up-to-date, integrated database architecture. This change will improve data integrity, decrease duplication, and promote effective data retrieval, enabling the creation of reports more quickly and accurately. Second, the system has sophisticated data analytics and reporting features that will enable the bank to examine data on customer complaints, spot patterns, and respond quickly to resolve problems—all of which are in line with their objective of raising customer happiness.

The suggested solution also incorporates strong data governance and security mechanisms that guarantee regulatory compliance and protect private consumer data. By doing this, the bank's data handling procedures will be in line with industry best practices, reducing the likelihood of data breaches. Overall, the data management solution will allow business users to concentrate on strategic development objectives instead of being burdened by manual data management duties, freeing up critical time from them to do so.


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