Invited orientation talk for network support staff on trending data-related topics at Janet Networkshop43 at Exeter University in March 2015.

1. Supporting Big Data, Open Data, Data
Analytics and Data Science
Dr Simon Price
Research IT Manager

2. • Bristol is a research-intensive university
• 6 Faculties: Social Science & Law, Science, Engineering,
Arts and two Medical Faculties
• Employs 2000+ researchers (excluding PhDs)
• Each year (approximately):
• 1500 research funding applications
• £100M research income
• 4500 research outputs
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3. Outline
1. Big Data
2. Open Data
3. Data Analytics
4. Data Science
5. Implications for IT support
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4. 4
Big Data

5. Big Data
• Lots and lots of technology buzzwords!
• Some important ones:
• MapReduce
• The Hadoop stack
• Distributed file systems
• Query languages & programming languages
• NoSQL databases (columns, document, graph, ...)
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6. MapReduce in a nutshell
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Image source: https://developers.google.com/appengine/docs/python/dataprocessing/

7. Big Data
• Trends in Hadoop stack
• Near realtime analytics
• Streaming analytics
• In-memory
• Trends in NoSQL
• Relational and NoSQL moving closer together
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8. Open Data
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9. Open Data - data.bris
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• Each PI allocated 5TB "forever"
• Research Data Management
• Open Data Publication

10. Open Data - public data
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11. 11
 140+ datasets live on opendata.bristol.gov.uk
 Some real time data
 Transport API repository now available
 Examples
 Government: Elections since 2007
 Community: Quality of Life survey
 Education: School Results
 Energy: Installed PV, Energy Use in Council Buildings
 Environment: Real time & Historic Air Quality, Flood Alerts (EA)
 Land use: 2013 Planning applications
 Health: Life expectancy/ Mortality, Obesity, NHS Spend
Bristol is Open - datasets

12. Data Analytics
• Operational focus
• variables are "known knowns and known unknowns"
• Descriptive
• summarisation known variables and alerting
• Predictive
• correlations between known variables
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13. Data Science
• Multidisciplinary data-intensive research
• Focus on research insights, causation and prediction
• Usually involves Machine Learning and Statistics
• Different perspectives:
• Computer Scientists view DS as a research domain
• Statisticians view DS as a research domain
• Other academics view DS as a service
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14. 14
17 March 2017

15. 15
17 March 2017

16. Implications for IT support
• Governance
• Shift from IT-owned to academic-owned (Shadow IT)
• Skills
• IT experts need to train and trust academics
• Nurture internal skills pipeline (interns, postgrads)
• Systems
• Mixed economy of internal and external
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