1. Rob Gleasure R.Gleasure@ucc.ie www.robgleasure.com
IS6125/IS6145 Database Analysis and Design Lecture 1: Introduction to IS6125/IS6145 and the changing nature of data
Rob Gleasure

2. IS6145
Module objective
To provide students with the concepts and skills required to analyse organisational activities, information flows and to subsequently create the data models required to support these activities
Topics covered include
Requirements analysis
Introduction to datafication and emerging data capabilities
Data modelling (ERDs and normalisation)

3. IS6145
Learning objective
Analyse organisational activities to identify key data requirements
Generate ERDs to identify data sources and their relationships
Employ normalisation processes to assist in meeting the data integrity requirements
Identify and strategize around semi-structured and unstructured data capabilities

4. IS6145 Course Assessment Continuous assessment: 30 marks
In-class exam – 20 marks
Group report – 30 marks
Exam: 50 marks
Skills to add to your LinkedIn (or equivalent)
Data modelling
Entity Relationship Diagrams (ERDs)
Data Strategy

5. Course structure Or more specifically 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: Data Normalisation
Week 9: NoSQL and Hadoop
Week 10: Blockchain
Week 11: The Data Value Map
Week 12: Revision

6. Data a few years ago
Image from

7. Data center in the cloud
Web is overtaking/has overtaken desktop
Mobile is replacing local
Utility-based computing is replacing once-off purchase
Makes resources seem endless
Lowers risk in terms of usage (pay as you go)
Demand
Capacity
Time
Resources
Demand
Capacity
Time
Resources
Static data center
Data center in the cloud
Slide Credits: Berkeley RAD Lab

8. The Cloud The ‘Internet of things’ was born in about 2009
More (far more) devices connected to the Web than people…
Image from

9. The Cloud This has meaningful implications for data in terms of
Capacity
Measurement
Integration
Security
Privacy

10. Big data
All of this interaction with one linked information system means vast quantities of data can be captured throughout user interaction, often in real-time
 ‘big data’
The idea is that the vast amounts of interaction data allow for systems that are nuanced and responsive in ways that were previously not possible
Also a realisation that, if it can be analysed, this data is a huge commodity, meaning new business models are possible
Firms like Google, eBay, LinkedIn, Facebook, etc. are based on the principles of big data

11. 3 Vs of Big data: Volume Volume
Facebook generates 10TB of new data daily, Twitter 7TB
A Boeing 737 generates 240 terabytes of flight data during a flight from one side of the US to the other
We can use all of this data to tell us something, if we know the right questions to ask

12. All available information Analyze small subsets of data
3 Vs of Big data: Volume
From
Traditional Approach
Big Data Approach
Analyzed information
All available information analyzed
All available information
Analyze small subsets of data
Analyze all data

13. 3 Vs of Big data: Velocity
Clickstreams and asynchronous data transfer can capture what millions of users are doing right now
Make a change, then watch the response.
No guesswork required up front as to what to gather, we can induce the interesting stuff as we see it

14. 3 Vs of Big data: Velocity
From
Traditional Approach
Big Data Approach
Hypothesis
Question
Data
Exploration
Answer
Data
Insight
Correlation
Start with hypothesis and test against selected data
Explore all data and identify correlations

15. 3 Vs of Big data: Variety Variety
Move from structured data to unstructured data, including image recognition, text mining, etc.
Gathered from users, applications, systems, sensors
Increasingly comprehensive data view of our ecosystem
 The Internet of Things

16. The Internet of Things
From

17. The Internet of Things
RFID sensors, bluetooth, microprocessors, wifi all becoming easier to embed in ‘dumb’ devices
Move to mobile also means more data streaming from us at all times, e.g. location, call activity, net use

18. The Internet of Things Smart homes/smart cities
Temperature, lighting, food stocks, energy, security
Smart cars
Diagnostics, traffic suggestions, sensors, self-driving
Smart healthcare
Worn and intravenous computing detects issues early and monitors care outcomes remotely
Smart factories, farms
Machines coordinated efficiently, linked dynamically to consumption models

19. Big data Success stories Books
Barnes and Noble: Discovered that readers often quit nonfiction books less than halfway through. Introduced highly successful new series of short books on topical themes
Amazon: originally used a panel of expert reviewers for books. Data surplus allowed them to create increasingly predictive recommendations. Panel has since been disbanded and 1/3 of sales are now driven by the recommender system

20. Big data and the Internet of Things
Success stories (continued)
Transport
Flyontime.us: used historical weather and flight delay information to predict likelihood of flights get delayed
Farecast: looked at ticket prices for specific flights based on historical data, then advised users to buy or wait according to predicted fare costing trajectory
UPS: Uses a range of traffic data to calculate most efficient time/fuel efficient routes according to complex algorithm

21. Big data and the Internet of Things
Famous success stories (continued)
Healthcare
 Modernizing Medicine EMA dermatology system

22. Big data and the Internet of Things
Famous success stories (continued)
Social media
Google (data for information relevance)
Twitter (c.f. #RescuePH)
Facebook (social data)

23. Big data and privacy
Morey et al. argue people create roughly three types of data of increasing sensitivity
Self-reported data
Digital exhaust
Profiling data
Due to the growth in biotechnologies and sensors, there’s an argument that ‘profiling data’ could be further broken down to differentiate between ‘digital profile’ data and ‘biometric data’

25. Data beneficiaries
Companies may then use the data in three different ways
Improve product or service
Facilitate targeted marketing
Sell data to third parties
Google search is an example of a digital business that combines all of these
Your search behaviour becomes customised
Ads are placed in front of you according to your history and location
Click-through behaviour and user overviews are provided to third parties

27. Issues with big data Google Flu Trends
Life imitating data, imitating life?
No one is really average height
Your Xbox knows you like that Katy Perry song
Also, Target called to say your teenage daughter is pregnant.
Icecream sales and shark attacks…

28. Icecream sales and shark attacks continued (correlation, not causation)
From

29. Correlation, not causation (continued)
From

30. Descriptive vs prescriptive analysis
Image from

31. Target’s family monitoring continued

32. Assignment 1
UCC has established a business school in the last couple of years. This business school is now seeking accreditation from the Association to Advance Collegiate Schools of Business (AACSB).
Among other things, this requires data is gathered to quantify
What academic staff actually do
How those activities relate to the quality of research
How those activities relate to the quality of teaching
How those activities relate to the quality of other responsibilities
Working in groups, you will each create a report that provides
A data model (ERD) to capture and relate activities
A data strategy for capturing and analysing important data

33. Readings
Mayer-Schönberger, V. and Cukier, K. (2014). Big Data: A Revolution That Will Transform How We Live, Work, and Think, John Murray Publishers, UK.
Morey, T., Forbath, T. T., & Schoop, A. (2015). Customer Data: Designing for Transparency and Trust. Harvard Business Review, 93(5),
On Modernizing Medicine
On Spotify