Research paper 20

CA3 Assessment Brief

Module Title: Data & Digital Marketing Analytics

Module Code: B9DM105

Module Leader: MSc in Digital Marketing

Stage (if relevant): 9

Assessment Title: Data: Full report and analysis.

Assessment Number (if relevant): CA 3 of 3

Assessment Type: Project Report

Individual/Group: Individual

Assessment Weighting: 40%

Issue Date: 21/6/19

Hand In Date: 07/8/19

Mode of Submission: Moodle

Details of Assignment brief

You are working in the Big Data Dept. of the ‘Red Cloud’ company – a large multinational, reporting to the newly appointed CEO. As she is new to the role, she would like a report (3000 words) explaining the Information Management strategy of the company. You should include the following topics in this report: Data Analytics, Data Collection & Storage and the methods and technologies used in analysing the Data. Data Abstraction Layers, Data Warehousing and Data Mining. The role and benefits of your Big Data Dept prior to a restructure. She also wants a Data Visualisation to illustrate a relevant Data Set for 3 of the below headings. 1. Data analytics – suggested topics: Data as a business asset, Data organisation issues. The nature and use of information, Evolution of Information usage and modern data models. 2. Data Collection and storage – suggested topics: Data and information on a local system, files, file types, file systems & databases Storage devices and storage configurations Tracking codes Cookies and beacons Explain in detail in your report the methods and technologies used for analysing the data.

3.Data abstraction Layers – suggested topics: Data independence, data definition, data manipulation languages Relational data models, functional dependency theory and normalization, External, conceptual and internal schema of data. 4.Data warehousing and how it is done – suggested topics: Data warehouse features, OLTP VS OLAP, data mart vs data warehousing, generic data warehouse architecture. 5.Data mining – suggested topics: How new knowledge is discovered , algorithms used for modelling, classification, clustering prediction, sequence analysis and association analysis. 6. Big Data - As part of this report she would like you to explain the role and benefits of your Big Data dept prior to a restructure – suggested topics: The nature of Knowledge Management, Knowledge capturing and modelling AI, Natural language processing, Business performance Management, KPI's, Dashboards, and emerging Trends. 7: Data Visualisation: Use a D.V. tool (eg Data Studio, Power BI, Tableau) to create a visualisation to back up your research for at least 3 of the above subjects relating to your company.

Assessment criteria

Topic Weighting Outline of Data Analytics

5%

Data Collection & Storage types

15%

Data Abstraction Layers

15%

Data Warehousing explained

10%

Data Mining

10%

Roles & Benefits of Big Data and Knowledge Mgmt.

15%

Data Visualisations for 3 topics (min 3).

10%

Trends & Recommendations 20%

Learning Outcomes Assessed: L01, 02, 05 Overview: Strong reports will utilize both theory and best practice guidelines to explain all the aspects of data, using multiple sources.

Submission & Assessment: Submission Criteria – The Report should be uploaded to Moodle (Word or Google Docs with word count included) by Friday 02/08/19 6pm. Assessment Criteria Report:

General Assessment Submission Requirements for Students:

1. Online assignments must be submitted no later than the stated deadline.

2. All relevant provisions of the Assessment Regulations must be complied with. Students are required to refer to the assessment regulations in their Student Guides and on the DBS Quality Assurance Handbook Guide.

3. Extensions to assignment submission deadlines will not be granted, other

than in exceptional circumstances. To apply for an extension please contact the course administrator.

4. Students are required to retain a copy of each assignment submitted.

5. Assignments that exceed the word count will be penalised.

6. Dublin Business School penalises students who engage in academic

impropriety (i.e. plagiarism, collusion and/or copying). Please refer to the referencing guidelines on Moodle for information on correct referencing.

Late Submission

• Assignments submitted after the deadline published in the assessment specification, including any extension, are deemed to be ‘late’ and are penalized as follows:

• Where an assignment is submitted between 1 and 14 days late, 2 marks per day are deducted.

• Where an assignment is more than 14 days late, it is annotated at the discretion of the lecturer, but no marks can be awarded.