Databases and Business Intelligence
Databases and Business Intelligence – KAP Speciality Chocolates Database Coursewor
This individual coursework for the Databases and Business Intelligence module provides practical experience of database systems through the realistic KAP Speciality Chocolates case study. Students are required to design and implement a relational database solution that supports the management of products, stock, suppliers and purchase orders for KAP Speciality Chocolates Ltd. The case study describes a growing chocolate business with a shop and a storeroom that requires a database system to reduce operational errors and improve stock control. The first task requires students to analyse the case study and identify appropriate entities, attributes, relationships, primary keys, foreign keys and dependencies. Students must produce an extended Entity Relationship Diagram showing how the identified elements should be related. The diagram must include participation and cardinality constraints, and students must clearly document the assumptions made and the notation used. A range of modelling notations and software tools may be used, provided that the required database elements are clearly represented. The second task requires students to translate the ER design into a relational database implementation by writing SQL Data Definition Language statements. The SQL must define suitable tables, attributes, data types, primary and foreign keys and relevant constraints. The chosen data types should be appropriate and foreign-key data types must correspond correctly to their related primary keys. The third task focuses on SQL Data Manipulation Language and requires students to create queries addressing a range of business information needs. These include producing a product price list, identifying purchase orders due for delivery on a specified day, generating shop re-stocking lists, changing product cost and selling prices, identifying products with outstanding purchase-order deliveries, producing a re-order list for products below their minimum warehouse stock level, and identifying suppliers that provide multiple cartons. Students must populate their tables with sufficient suitable data to demonstrate that the SQL works and provide evidence of testing through screen captures of SQL statements and results. The fourth task requires students to demonstrate that the database design is normalised to Third Normal Form (3NF). The normalisation process must begin with unnormalised data and show the stages through First Normal Form (1NF), Second Normal Form (2NF) and Third Normal Form (3NF), explaining the changes and reasons at each stage. The final normalised solution must remain consistent with the ER diagram, assumptions and entity and attribute names used in the database implementation. The final submission must combine all tasks into one report written in English, using Arial 12-point font and single spacing. The report must contain the extended ER diagram and assumptions, SQL table definitions, SQL queries with explanations and testing evidence, and a written explanation of the normalisation process. The completed report must be submitted as a single PDF through SurreyLearn by the specified deadline.
Read Model Answer →
Data Management / Business Analytics
2,500 words
Data Design Management (BS514) — Data Strategy Consultancy: Relational Database Design, SQL Implementation and Pipeline Transformation
This Level 7 assessment places the writer in the role of a Data Strategy and Analytics Consultant appointed by an organisation operating in a realistic industry sector. The brief is entirely simulated, so the work sets out a defensible set of assumptions about the organisation's data environment before any design begins, and populates the resulting database with synthetic but realistic records. The deliverable is a slide deck carrying full explanatory notes, submitted as a single PDF, and weighted across three connected tasks. Task one establishes the business case. It describes how the chosen organisation currently collects, stores and uses data across customer interactions, sales transactions, operational processes and digital channels, and identifies where that fragmented picture costs the business in efficiency, resource use, retention and decision quality. A SWOT analysis benchmarks the organisation against a named real-world competitor in the same sector, drawing on publicly available market information rather than assertion. The section closes with a critical evaluation of modern relational database advancements — cloud-hosted SQL services, distributed architectures and data warehousing — assessed not in the abstract but against what each would actually change about this organisation's business model. Task two carries the heaviest weighting and is the technical core. Key business entities are identified from the scenario, a current-state data flow diagram traces how data moves from collection points through to storage and reporting with the existing ETL approach made explicit, and a future-state ER diagram is then built with full attributes, primary and foreign keys, relationships and cardinality. The design is normalised to third normal form with the decomposition reasoning shown. Implementation follows in SQL: tables created with appropriate integrity constraints, at least ten realistic sample records inserted per table, and five business questions answered through working queries — highest-performing product or campaign, average conversion by category, workload distribution across staff, accounts with overdue or pending items, and most effective service channel. Outputs accompany every script. A transformation demonstrating query optimisation is included with before-and-after samples so the improvement is evidenced rather than claimed. Task three steps back to the technology decision. Two widely used data processing platforms are compared in tabular form across integration, cleaning, transformation and automation capability, judged specifically against this organisation's constraints, with a reasoned justification for the tool finally selected. The transformed dataset is then used to answer two management-level questions — where investment should be prioritised and how retention might be improved from observed behavioural patterns — each interpreted briefly and tied back to a concrete recommendation. Slide structure follows the prescribed layout, SQL scripts sit in the notes section, and the complete script file is reproduced in the appendix.
Read Model Answer →