MIS609 Data Management and Analytics Case Study 3 Sample

MIS609 Data Management and Analytics Case Study 3

Assignment Brief

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.

The solution should discuss how it helps the organisation to solve the technical or operational complexity of handling their data. Do not deep dive into any topic. Rather provide the data management solution more at a conceptual level.

• Identifying the business requirements, data requirements and the existing issuesin data handling.

• For example, issuesmay be related to:

• Data Architecture

• Data integrity

• Data Storage

• Metadata management

• Handling legacy data

• Datamigration

• Data archival

• Data governance

• Data privacy

• Handling documents

• Data integration

• Identify data management operations needed to tackle the identified issues, to meet the data-based requirements and identify steps to be taken to improve their overall workflow and operational efficiency.

• Document these operations and steps as a DM strategy. Focus on how new approaches can be implemented or how existing methods and workflow can be improved etc. Your DM strategy should be relevant to each of the issues that you identify from the case scenario.

• Also provide a schematic of a suitable data architecture as ablock schematic (at a conceptual level)that describes how data is proposed to be Managed in this scenario– from storage and collection through to integration,transformation, distribution, and consumption.

• The areas / DM operations listed above are indicative and are in no particular sequence. When addressing each DM area in the proposed solution, ensure thatthere isrelevance to the case scenario provided andwrite in an orderly fashion.

• Importantly, any other data management areas / operation not listed above in point 3, but if identified from the case scenario, should be covered in the DM solution.

Referencing

It is essential that you use appropriate APA style for citing and referencing research. Please see more information on referencing here: http://library.laureate.net.au/research_skills/referencing

Solution

1. Introduction

It is a dynamic clothing retailer, with 50 stores on the ground across Australia and New Zealand, that is undergoing a major transition to support its growing online presence. The organisation has 500+ daily online sales, with the aim to optimise operations and improve service to compete with the ever-changing retail market. However, due to its malfunctioning data infrastructure, the company is facing several problems as the infrastructure is causing the system to be inefficient and hampering operations performance. This report aims to provide a comprehensive solution for data management to overcome challenges and assure scalability of the business, enhance security, and well as the needs of customers. A solution to this will involve unifying currently disparate data systems, improving the integrity of data across the company, and bringing in modern techniques to manage the expected growth in the retail industry.

2. Current Challenges and Requirements

Today's data management practices at Slow Fashion Pty Ltd are fragmented; there are various spread sheet, local disk, network storage and relation databases spread throughout the office. Good manufacturing practices would not require a single contact point and depends on a quality control unit for approval (Lavelle, n.d). Fragmentation leads to inefficiencies since staff spends a lot of time retrieving reconciling and customer employee data. There is no integration, and as such, workflows are hard, especially when wanting to know access to a purchase record, returns, refunds, employee details, etc. Additionally, the company suffers from the problem of storing legacy data in line with policies that dictate that such information is to be secured from old to current customer and staff data.

There's another layer of complexity with the retailer moving to digital channels. In addition to these features, its website has new features, a mobile app and online chat support – features that introduce semi-structured and unstructured data, making their integration as well as scalability challenging. Moreover, the digital channels on which these videos are uploaded create large amounts of customer data that need special security and privacy measures to guard sensitive info. With the company evolving, it needs a scalable efficient and secure way to manage its data from multiple data sources while at the same time ensuring data quality and enabling analytics to better drive decision making. The solution proposed must also be cost effective in order to support the company’s financial constraints.

3. Proposed Data Management Solution

A complete data management solution is proposed to manage the challenges that Slow Fashion Pty Ltd faces. As a solution, this lays emphasis on centralising data systems, improving integration, data governance, data quality and use of advanced analytics tools. These measures will assist the company in realising operational efficiency and customer service and support the long-term scalability of the company.

3.1 Centralised Data Architecture

The proposed solution is built on the cornerstone of centralised data architecture. This will allow us to migrate all fragmented data sources into a unified, cloud-based data warehouse making data access and management streamlined. Together, both structured and unstructured data can live on scalable infrastructure offered by cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud MBA assignment expert by consolidating data into one repository, staff will no longer have to move through different systems, improving operational efficiency. AWS offers almost over 100 prebuilt services with ease (Wittig, & Wittig, 2019). Whereas, Microsoft Azure holds significant stake in cloud computing world (Hyman, 2023).

In addition, this centralised architecture enables real-time data processing, giving you a chance to gain real-time insights into things like customer behaviour and sales or operational trends. Besides, this cloud-based system makes the high availability and reliability for the company to support digital growth. This architecture provides the flexibility for the smooth introduction of future data sources so that the system will continue to be flexible for organisation evolution.

3.2 Data Integration Framework

A robust data integration framework must be implemented to assure consistency and access to the data across all the sources. To consolidate data from different systems into a centralised platform, it is recommended to use the Extract, Transform, Load (ETL) process. It will extract data from disparate sources, transform it into a standardised format, and then load it into the unified repository.

The ETL process will be driven by automation and manual intervention will be reduced to a minimum while accuracy will be maintained. This will formulate a company’s current fragmented systems into one holistic view of customer interactions and employee records. Also, automated integration will enable real time updates to keep customer and staff data current and actionable.

3.3 Data Governance and Security

Since customer and employee information is vulnerable and is part of the proposed solution, data governance and security are very important. With a robust data governance framework in place data access, usage, and retention will have policies and protocols. Role based access controls will prevent unauthorised access of data to personnel who should not be allowed to do so, reducing the risk of unauthorised access or data breach.

Data encryption for in-transit and at-rest data will be used to improve security. To identify and remedy potential vulnerabilities quickly, regular security auditee ring practices with existing real time monitoring tools will be used. Secondly, the governance framework will ensure the company's compliance with legal regulations pertaining to the privacy management ministry, such as the Australian Privacy Act and GDPR, thereby eliminating the probability of the company facing reputational and legal risks. The GDPR act is levied upon all kind of data controllers and processors which handled by EU residents’ person data (IT Governance, 2020).

3.4 Data Quality and Metadata Management

Data quality is important to the reliability of decisions and efficient operation. Poor data quality remains crippling to the data scientists (Salon, & Safari, 2019). The inconsistent, duplicated and erroneous records in the existing datasets will be identified and corrected using data profiling tools. All of this will also increase the accuracy and reliability of customer and employee records.

Organising and categorising data assets will have a pivotal role played by metadata management as well. Staff can easily find and retrieve information when metadata is associated with each dataset. This approach increases searchability and is useful for analytics and reporting using data efficiently. Using Data Profiling in conjunction with Metadata Management does add to the assurance that the company's data stays up to date, consistent, and organised.

3.5 Scalable Content Management

At present, where Slow Fashion Pty Ltd is trying to extend its digital footprints online, an effective content management system (CMS) is necessary to manage online operations. With a CMS, the company will be able to update and manage website content easily, to eventually give customers a much better experience. The CMS enjoys features like personalised recommendations, dynamic pricing, and real-time inventory updates to bring in better engagement as well as a higher level of satisfaction to the customers.

The CMS will help with omnichannel strategies as well and will guarantee consistency between the company's website, its mobile app, and various other digital portals. The company can adapt to increasing online volumes of data yet maintain operational efficiency using a scalable CMS.

3.6 Automation and BI Tools

To extract actionable insights from the company's data, means of automation and Business Intelligence (BI) tools are crucial. The data architecture can integrate with advanced analytics (tableau or Power BI) for real time dashboards and reports. They will help the company to analyse customer behaviour, predict sales trends and optimise inventory management.

Predictive capabilities can be enhanced further with machine learning algorithms which help the company predict what customers will prefer and what demand patterns will be. And freeing up the staff from routine tasks, such as report generation and data cleaning, to focus on strategy, such as product development or supplier management.

4. Conceptual Data Architecture Schematic

Slow Fashion Pty Ltd's conceptual data architecture is built to centralise and streamline data flow from collection to consumption. The main architecture consists of a cloud-based data warehouse that serves as a centralised data repository incorporating data from sources including physical store transactions, online sales, mobile Apps, and interaction related to customer support services. The processing follows data collection from these diverse sources through an automated ETL (Extract, Transform, Load) framework. The ETL framework actually performs structural, semi-structural and non-structural data standardisation, cleansing and integration into the warehouse.

The next layer performs data transformation processes to transform the data into analysis ready data formats in order to ensure the data is consistent. Next, the data is fed into analytics and BI tooling, where you can take actionable insights and get canned methodologies from stakeholders in the form of dashboards and reports. The data governance layer that controls access, data integrity and security (to comply with local legal and regulatory standards) is part of the architecture as well. Such a schematic is scalable, relied on and guarantees efficiency, making it perfectly fit to handle the operational challenges and growth aspirations of the organisation.

5. Implementation Plan

5.1 Step-by-Step Plan

• Data Audit and Assessment: Perform a thorough audit of what data already exists in sources, systems and workflows to determine how it is and is not being handled consistently, redundantly, and in a gap.

• Infrastructure Setup: Develop and deploy a cloud-based platform tailored to the organisation's needs and allow easy scalability and cost efficiency. You need to set up the right tools for integration, security and analytics.

• Data Migration: Phased migration of the data happens from fragmented systems to the centralised repository. At this stage, data will be cleansed and standardised, and the accuracy and integrity will be preserved.

• Integration and Automation: Automate ETL pipelines to integrate and synchronise data from various sources with less manual intervention and errors.

• Governance Framework: Put forth strong data governance policies, such as role-based access control, encryption protocols and regular audits, among others.

• Training and Change Management: Give your employees comprehensive training in the new systems and processes to avoid a hitched start and also to avoid resistance.

• Testing and Optimisation: Test the new data management system thoroughly to find and iron out any problems. Workflows are optimised, and the system is fine-tuned to feedback.

• Go-Live and Monitoring: Continue to roll out of the new system across all operations monitor continuously the performance, and address any emerging challenges on the spot.

5.2 Timeline and Milestones

The implementation plan will be executed over a six-month period, with key milestones as follows:

• Month 1: We then performed a data audit and infrastructure setup.

• Month 2-3: Data migration, including phased migrations and ETL frameworks.

• Month 4: Governance framework setup and staff training; these are all integration testing.

• Month 5: System optimisation coupled with pilot testing in selected operations.

• Month 6: Rollout of the full system with monitoring.

This timetable enables an orderly and expedited way for one to transition without disruption of the ongoing operations.

5.3 Risk Management

Data loss during migration, resistance from staff to change and integration issues with the legacy system are the potential risks during implementation. To mitigate these risks:

• Data Backup: During the migration process, it will take regular backups to make sure that data will not be lost.

• Change Management: If you communicate early and effectively with staff, and the training is thorough, adoption will be easy.

• Integration Testing: Testing the ETL framework will entail extensive testing to identify the integration issues with the legacy systems and ensure a hassle-free transition.

With proactive monitoring and contingency planning, risks will be minimised, and the project will stay on track.

6. Expected Outcomes and Benefits

The implementation of the proposed data management solution is expected to yield significant benefits across multiple areas:

• Operational Efficiency: Centralising data systems and automating processes will save staff lots of time they were spending on going via manual data retrieval and reconciliation. Those activities will require less time as a result of this efficiency and will mean more focus on important aspects like product development and customer service.

• Enhanced Scalability: The cloud-based architecture allows the system to handle new volumes of data created when digital channels produce semi structured and unstructured data.

• Improved Customer Experience: Personalised marketing, accurate demand forecasting and faster response to customer queries for improving customer experience will be possible with integrated data analytics and BI tools.

• Data Security and Compliance: Sensitive information will be protected by utilising robust governance frameworks and encryption protocols that will also help to ensure compliance with legal and regulatory requirements, reducing the risk of data breaches.

• Actionable Insights: Real time insight into sales trends and customer behaviour, and operational performance would also be delivered to you, helping you make data driven decisions.

• Cost Savings: The solution will be financially viable as it cuts operational costs by automating data management and streamlining workflows.

The results will ultimately transform Slow Fashion Pty Ltd into a data driven organisation ready to face future challenges and opportunities of the competitive retail market.

7. Conclusion

The data management solution proposed, solves Slow Fashion Pty Ltd.'s critical challenges and meets Slow Fashion Pty Ltd.'s goals in operational efficiency, improved customer service, and scalable capacity. The company will overcome current inefficiencies by centralising data systems, improving integration and setting up effective governance frameworks, thus putting together a strong base for future growth. Advanced analytics tools will be included to provide actionable insights that will assist with strategic decision-making and a competitive edge in the market. Through this solution, data management can be implemented with a structured plan, timelines, and risk proactiveness in order to ride with long-term success for the organisation.

8. References

Hyman, J. A. (2023). Microsoft Azure (2nd edition). John Wiley & Sons, Inc. https://lesa.on.worldcat.org/oclc/1370891487

IT Governance (Organization). Privacy Team. (2020). EU General Data Protection Regulation (GDPR) : an implementation and compliance guide (Fourth edition). IT Governance Publishing. https://lesa.on.worldcat.org/oclc/1225145238

Lavelle, L. (n.d.). Data Management Practices. Biopharm International, 33(2), 35–36. https://lesa.on.worldcat.org/oclc/8558615019

Salon, D., & Safari, an O’Reilly Media Company. (2019). A Product Development Approach to Improving Data Quality (1st edition). Data Science Salon. https://lesa.on.worldcat.org/oclc/1196891071

Wittig, M., & Wittig, A. (2019). Amazon Web Services in action (Second edition). Manning Publications. https://lesa.on.worldcat.org/oclc/1061561915