Sample MIS609 Ass Data Management and Analytics Assignment

MIS609 Ass 1 Data Management and Analytics Assignent Sample

Report

Task Instructions

1. Please read the attached case scenario.

2. Write a 1500-word data management pre-proposal for the organisation.

3. The pre-proposal should not only discuss the technical but also the managerial aspects (cost, manpower, resources, etc.). Please keep in mind that you are writing a pre-proposal and not a detailed proposal.

4. Please ensure that you remain objective when writing the pre-proposal.

5. Your pre-proposal should ideally answer (but not be limited to) the following questions:

a) What would the data management strategy be?
b) Which kind of data would be managed by your organization and how?
c) How many staff members at your organization would manage data of this retailer; what would be the team hierarchy and what would their expertise be?
d) What resources would be required from the retailer?
e) What deliverables (hard and soft) would be provided to the retailer?
f) What would general data management operations look like?
g) How would data management policy be set and how would it be implemented?
h) How would metadata be managed?
i) How would data quality be managed?
j) How would data management practices be audited and how would quality be assessed?
k) How will user and business requirements be collected from the clients?
l) Which data architectures and platforms would be used?
m) How would legacy data be taken care of?
n) How would risks be managed?
o) What benefits would the retailer have as a result of outsourcing this service to your organisation?
p) Others....

6. The questions mentioned above are written randomly, in no particular sequence. When addressing these questions in your pre-proposal, please ensure that you write in a systematic way. Make use of the web to find out what pre-proposals look like.

7. You are strongly advised to read the rubric, which is an evaluation guide with criteria for grading your assignment. This will give you a clear picture of what a successful pre-proposal looks like.

Solution

Introduction

Data management function is a necessity to operate Slow Fashion Pty Ltd. to process and manage datasets developed from organisational proceedings holding the non-core operational data. The given report addresses a pre-proposal identifying the data management function variabilities and requirements that is to be met by the company.

Benefits of outsourcing the data management function

Outsourcing data management services for assignment help would increase operational efficiency as the non-core executive of the company can be more focused on their performance with easy access to client, customer and employee information as needed with increased accuracy in the data obtained. It will help in easy retrieval of data without need to update information manually as it will ensure auto-update standards while managing the data (Bayrak, 2015). Data loss can be reversed, which is often the most common risk when handling data management and their activities single-handedly by the company executives in Slow Fashion due to changing company production demands.

Resources Needed

The major resources needed from the retailer are based on the following:

- Objective of the company is to establish data management function priorities and the overall objective of each non-core activity to process information focusing on the objective while presenting information during retrieval accordingly.

- Gathering information on external and internal assets of Slow Fashion that will be impacted by the non-core operations of the company.

- Company’s existing IT infrastructure design to develop a clear protocol and data management architecture that aligns with the existing structure

- Budget to proceed with the development and the overall human resource availability to run and maintain the data functions

Deliverables to be provided to the retailer

The deliverables to be provided to Slow Fashion Ltd are stated as follows

Software: A data management system software that will be linked with the non-core operations of the company along with the main database system of the company to retrieve information of the core operations. The software system will be installed with the major hub of the computing devices in the form of a website application form where information regarding company operations, production development, supplier data, and other customer services can be stored and retrieved as needed (Rahul and Banyal, 2020).

Hardware: A pre-installed processor to be provided to the retailer containing four or more 3.3GHz Intel Xeon class servers with an internal memory of terabytes.

Data Management Strategy

The data management strategy is based on the roadmap that Slow Fashion would follow steps and formats identifying the potential effectiveness of the data management function to be developed. The management strategy is to be based on the DAMA framework that involves eleven functions that are to be taken into account by the data management system or function being outsourced for development.

Figure: DAMA Framework
(Source: Damadach.org, 2021)

The DAMA framework further identities the strategies to be present in the form of developing data architecture, modelling and designing of the function system, storage and operation development, maintenance of data security, integration of information regarding governance, documentation and content management, warehousing data, metadata and establishing standards of data quality to be managed.

Data management operations to look like

The general data management operation to be followed is based on the core recruitment for the data management function by Slow Fashion. It should include operations that are focused on the data pipeline for four major functional data, which are sales data, CRM data, Third-party data, and non-core activity data. The data warehouse would be able to conduct three primary activities, which are analytics, business intelligence, and ML modelling through the data obtained.

Types of data to manage and its strategies

There would be three major data types that are to be managed by the data management function system to be developed. The data are based on the non-core operations such as employee performance management and training need updates, everyday employee attendances, workplace equity information, team meeting updates, logistics operations in terms of delivery to customers, and data that include market research and future forecasts for the company growth’s requirements. The strategies to be followed are based on identifying the general objectives of each data type obtained, identifying tools to assess the data and formulating the data retrieved as needed to meet retail goals (Tekinerdogan et al. 2020).

Management teams and their responsibility along with hierarchy pattern

The management team of the data management function would include data scientists, data engineers, and data analysts. Hence, the data management team will be based on a three-level structure that will be following the consulting model (Vassakis et al 2018).It would include the CEO of Slow Fashion on the top, the analytics group, business units and functions at the second level while the data management executives at respective sections to be present at the third level reporting directly to their team leads, which then reports to the managers, present at the second level. The role of data scientists is to analyse, model, process data, and further interpret data as needed. The engineer is to develop the system and maintain them while the data analyst will interpret the data obtained by the data scientists.

User and business requirement collection process

The user and business requirements will be collected from the clients by using email networks. On the other hand, the data management system will be holding a segmented structure to upload requirements of the Slow Fashion clients, which will be analysed and directed to the company executives according to their designations and production responsibilities.

Data architecture and platforms used

The data architecture will be based on three-tier architecture as the given data management function system will be outsourced (Lee et al. 2015).Hence the application of the three-tier architecture would be effective as it will hold the inclusion of a third layer between the client machine and the server machine leading to an indirect communication with the server application that would be acting as the outsourcing team that uses the internal database system of the company provides the necessary information. The platform to be used is SAS Data Management Suite to ensure minute detailing of the information to be obtained by capturing, migration, data mastering, analysis, management, integration, and quality control. Data management policy setting strategies and their implementation

Figure: Data management strategies
(Source: Altexsoft, 2021)

Data management and privacy policy : Slow Fashion Pty Ltd has followed strategies such as defining data architecture by implementing the role of the data architect. The data modeling will be based on key business concepts of clicks and mortar and the role will be implemented by data modeler and data scientist. The database administration is done with database management and ensuring the availability of data (Babar et al. 2019).

Data quality management policy: There has to be quality management by the company's quality data engineer so that the business infrastructure will be governed by data requirements. There will be the management of integration and consolidating data into a single place. This helps in monitoring data analytics.

Metadata management and quality management

Good data is manifested through the implication of metadata management and quality management. The retail company has to look into their SOX for financial data, HIPAA for healthcare data and regulatory compliance of the data program has to be managed with accurate data definitions. Data quality management will be increased so that the affirmativeness of the data error will be increased from 2% to 7%. This will help in the control of data with appropriateness with proper data profiling. This will help in the reduction of time and resources that are manifested within metadata management and data quality management (Anuradha, 2015).

Strategies to Audit data management practices and quality assessment

The strategies to audit data management and quality assessment will be done with a DQA tool that will help in understanding the indicators of problematic data transfer. This will provide agility to the data management system of Slow Fashion Pty Ltd. Integration of the data audit will be done in 19 steps and 6 phases. Access to all the 50 places within the reporting period is required. There will be notification and documentation as per national standards. The audit visits will be taken into consideration and the documents are to be reviewed in all the steps. There will be the inclusion of data management systems review with trace and validity results. Data aggregation and consolidation management will be found with DQA tools.

Figure: Data auditing system
(Source: Measure evaluation, 2021)

Managing legacy data

The management of legacy data will be done with state and federal regulations and this will provide information to all the complex legal issues that are based on data-driven frameworks of Australia and New Zealand where Slow Fashion Pty Ltd is functioning. The cost, analytics, security, and retention of data will be upgraded as per federal government laws and this will help in the understanding of IT resources and budget that is included. The data migration and legacy system had to be made with concise network and security systems (Fan et al. 2015).

Managing Risks

The data risks can be based on the better decision-making process and it should involve the inclusion of information and resource management. The financial health of the organization is to be considered. The management will help in the monitoring and management of data with proper synchronization. The brand reputation is to be measured with the data management systems. Preventive measures are to be taken such as patches, firewall and help in the management of centralized data management. This will help in the management of accessible, searchable, and customizable features.

Conclusion

Thus, the outsourcing of data management functions would be critical to ensure Slow Fashion Pty Ltd attains sufficient information to manage its non-core operations that usually consumes a major performance ability of the employees. The data management function will be following a three-tier architecture using the SAS Data Management platform to increase the efficiency of the data management activities.
 

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