f. Coursework Requirements: This coursework is based on a case study of your choice. The case study should represent a scenario where data can be used to improve the decision-making process. The submission should be in the form of a report of no more than 2400 word, see below for more details. Your report will be based on the material covered in the lectures and labs, but you are also expected to carry out your own research. Your submission should contain the following sections: v The Case Study (15 marks) Ø A brief overview of your chosen case study, which clearly describes the scenario where data can be used to improve the decision-making process. A brief overview of the case Your organization Objectives of the organization Problems encountered in current objectives Scenario where data can be used to improve the decision-making process. How data can be used? What data is used? v The Data Requirements (15 marks) Ø A detailed discussion of the data requirements of your scenario. This should include a discussion of both: Ø Example Scenario: Using data to improve customer loyalty rate. Ø Customer Lifetime Value (CLV) is an important metric for businesses as it helps you to see the value of a long-term relationship, rather than a single transaction. It’s also tied directly to the company’s bottom line, which makes CLV especially useful for marketers and customer success teams because it allows them to quantify the value of an organisation’s customer experience (CX) efforts. Ø The simplified ROIC formula can be calculated as: EBIT x (1 – tax rate) / (value of debt + value of + equity). § Internal data, data collected locally. · Customer Master . Customer Transactions. Selling Costs § External data, data collected by external bodies. · Benchmark statistics · Government · Big Data (Social Media) v The Data Warehouse (30 marks) [Internal Data] Ø A discussion of data identified which would be suitable for storage in a data warehouse. § Subject Oriented Database § Dimensions Ø An example and brief discussion of one suitable data schema. E.g. A star, snowflake or galaxy schema. Star Vs Snowflake – Draw a sample schema diagram (With measure appropriate to your scenario) with some dimensions (3 – 4 dimensions). – Pro and Cons and your selected schema. Ø Using examples from your scenario (e.g. CLV), discuss: § The ETL process. · E – Method (Bulk Vs Change) · T – What data cleansing process? What data integration? (Customer -> Transaction; Customer -> Financial data) . Multistage Data Transformation Vs Pipelined Data Transformation (https://www.stitchdata.com/etldatabase/etl-transform/) · L – Loading (Incremental vs. Full load ; Off-line vs. on-line ) § Online Analytical Processing (OLAP). · What OLAP process is related to your scenario · Customer profitability analysis as an example · Understand measure by dimension analysis ¨ E.g. customer profitability by region · Possible operations in OLAP and show your example. ¨ Roll up & Drill down ¨ Slice & Dice ¨ Pivot v Big Data (25 marks) Ø A discussion of data identified which would not be suitable for storage in a data warehouse. § 3 criteria of big data – 3V + Veracity (ETL – Transform. Data cleansing) Ø A discussion of reasons the data is not suitable for storge in a data warehouse. Ø Using examples from your scenario, discuss a framework that could be used to collect, store and analyse this data. § RTAP § Real-Time Analytics Processing on AWS / Azure § v Conclusion (15 marks) Ø This should include a summary of the report and suggest the most appropriate data strategy for your scenario.
i have selected pet food distributor, i have the business scenario ready, and the business challenges definition ready
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