IS6145 Database Analysis and Design Lecture 12: Semester Review and Exam Preparation

1. IS6145 Database Analysis and DesignLecture 12: Semester Review and Exam Preparation Rob Gleasure R.Gleasure@ucc.ie www.robgleasure.com

2. Course structure • Outline • Week 1: Introduction • Week 2: Foundational Concepts of Data Modelling • Week 3: ER Modelling and Beyond the Presentation Layer • Week 4: Fine-Granular Design-Specific ER Modelling • Week 5: Enhanced Entity-Relationship (EER) Modelling • Week 6: Practice with ERDs • Week 7: In-Class Data Modelling Exam • Week 8: The Data Value Map • Week 9: Data Normalisation • Week 10: NoSQL and Hadoop • Week 11: Blockchain • Week 12: Revision

3. IS6145 • Today’s session • Subjects covered and the types of questions to expect • Essay style questions • Modelling questions • General stuff • Questions?

4. Types of learning Application Doing things Reflection Examples Understanding things Making new connections

5. Exam structure • Three questions, answer two • 90 minutes • You must answer Question 1 • You may choose to answer either Question 2 or Question 3 • All questions carry equal marks • Dictionaries may be used for international students, however you will need to coordinate with the International Office in advance • Students registered with DSS may be allocated additional time or alternative resources, however you will need to coordinate with the DSS Office in advance

6. Question 1 – doing things • ERDs • Can be SSADM and/or Chen’s • Modelling question will expect • A model • Constraints • Assumptions • You may also be asked to discuss issues, such as • Differences between stages of ER modelling • The reasons for a staggered approach to data modelling • Commonly encountered issues

7. Question 1 – doing things (cont.) • Normalisation • Question will expect you to normalise a table to the third form • Don’t be afraid to identify assumptions, where you feel they require further discussion • You may also be asked to discuss issues, such as • The reasons for normalising a table, i.e. redundancy • The three Armstrong axioms

8. Question 2 – understanding things • Topics covered • Big data • What are the three Vs? • When is data ‘big data’? • How and why did we get from ‘small data’ to ‘big data’? • What does big data let businesses do that they couldn’t do previously? • What businesses are a good example off this? • What are the issues and challenges arising from big data? • Can you use contrasting examples of different businesses to discuss each of these headings?

9. Question 2 – understanding things (cont.) • Topics covered • Datafication and the Internet of Things • What is data and how does something become ‘datafied’? • How and why did cloud technologies evolve? • What does it mean in terms of technological and business capabilities? • What is the Internet of Things? • What does the future hold? • Can you use contrasting examples of different businesses to discuss each of these headings?

10. Question 2 – understanding things (cont.) • Topics covered • Business intelligence and the different types of data • What types of data exist, e.g. self-reported, exhaust, profile • How do we move from descriptive analytics to prescriptive analytics? • How do we get from an individual case to a large-scale pattern, and back again? • What are the challenges of translating intelligence from an individual case to large-scale patterns, and back again? • What businesses exemplify the ability to generate intelligence from the increased capacity for data handling, and why? • Can you use contrasting examples of different businesses to discuss each of these headings?

11. Question 2 – understanding things (cont.) • Topics covered • Privacy and security and the ethical cost of data • Why is data sensitive? • What are the threats to privacy and security? • Why are digital businesses more reliant on trust than traditional businesses? • What is the cost of data for a user? • Why does data privacy and security matter? • Can you give examples where these issues were handled well and badly?

12. Question 2 – understanding things (cont.) • Topics covered • Data strategy • What are the different components of a data strategy and why are they important? • Acquisition • Integration • Analysing • Delivery • Governance • Business value • Why does having a data strategy matter? • Can you show contrasting examples of good/bad strategies?

13. Question 2 – understanding things (cont.) • Topics covered • Blockchain • How does it work and why is it different from other data platforms? • How and why did it emerge as a technology? • What are the potential uses of the technology beyond cryptocurrencies? • What are the obstacles to introducing blockchain-based systems? • Can you use contrasting examples to illustrate the potential of blockchain?

14. Question 3 – making connections • Presented with a statement or idea and asked for your opinion • E.g. Peter Drucker famously said “What gets measured gets managed”. Critically discuss this statement in the context of big data, using examples from specific businesses or your own experiences to illustrate your answer • Just one part (no breakdown into (a), (b), etc.) • You have to decide how to apply what you have learned to form a meaningful answer

15. Answering Questions • Exam technique • Manage your time • Plan your answers • Say it simply but use the keywords from the semester, e.g. if you are talking about ‘big data’, call it big data – you don’t need to get creative • Sketch out your diagrams very quickly as roughwork if you’re not sure how to make them fit together • Answer your best questions first • Use examples • Have these lined up as part of your revision

16. Sample rubric