1. Legal and Technical Long
Term Aspects of Big Data
and Knowledge
Banji Adenusi
September 2015
Delivered at the
Information Science, Security and Computing Class
EULISP 2015, Leibniz University Hannover

2. Outline
• Big data phenomenon
• Infographics
• Technical aspects
• Legal aspects
• Video: An intro to the legal implication of big data
• Further Reading

3. Big Data Phenomenon
“Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative
forms of information processing that enable enhanced insight, decision making, and process automation.”
- Gartner IT Glossary
A misnomer and industry jargon for large data or
information that represents an exponential increase in the
scale and scope of knowledge about a given subject matter.
”Big Data Word Bubble" by Rachel Serpa
“data sets that are too large and complex to manipulate
or interrogate with standard methods or tools”
- Oxford English Dictionary
An umbrella
term

4. The next frontier for innovation,
competition and productivity
– McKinsey Global Institute
”BigDataWordBubble"byRachelSerpa
Volume
• Amount of data
(data at scale)
• Datasets between
1TB and 1PB or
more
Velocity
• Many forms of data
and data sources
• Structured, semi-
structured and un-
structured data
Variety
• Data in motion
• Real time data
creation, streaming
and analyses
Veracity
• Valuable but uncertain
information
• Creating context for
uncertainty (data fusion
& optimization)
In the same manner in
which social media has
shaped the world around
us, Big Data will similarly
have a substantial impact
on our present-day reality.

5. Infographics
”Big Data Word Bubble" by Rachel Serpa
Evolution of Big Data – An IBM Expose´

7. ”Infographic" by IBM Big Data & Analytics Hub
”Big data infographic" by Wikibon blog

8. Technical Aspects of Big Data
Heterogeneity of data
Inconsistency and
incompleteness Scale & timeliness Collaboration Privacy
Data volume scaling faster than compute
resources. Processor technology, move towards
cloud computing and transformative change of
the I/O technology.
The larger the data set to be processed, the
longer it will take to analyze.
Machines expect
homogenous, structured
data. Human information
is however heterogeneous
and unstructured.
Managing data privacy is
both a technical and
sociological issue.
Location based services
typically broadcast user
data without permission.
Human collaboration is
necessary for big data,
especially for data
analyses and
interpretation. Crowd-
sourced data can contain
errors and uncertainty
What happens if one or more
pieces of information is
unavailable? Even after data
cleaning and error correction,
some incompleteness and some
errors in data are likely to remain

9. Legal Aspects
• Central concern is with respect to Intellectual
Property rights and ownership in relation to dataData Creator
• Central concern is in relation to privacy and data
protection. Is consent of the individual required in
data mining, use and re-use?
The User/
customer
• Motivated by profit and monetary gains, and
establishing monopolies.
The
corporation

10. • Data creation & exploitation
• What is copyrightable (mass
digitization projects)?
• Database rights?
• Property rights?
• Liability for incorrect data
Ownership &
IP Rights
• Digital data (right to be
forgotten)
• Scope of lawful data
processing
• Personality rights?
• Privacy by design (PbD)
• Need for consent?
• EC Directive 95/46/EC
Data Protection
& Privacy • Free and unrestricted access
to market
• Issues around primary and
secondary market
• A common legal framework?
• Draft EC Trade Secrets
Directive 2013
Competition &
regulation

11. An intro to the legal implications of Big Data
https://youtu.be/-ub0KO55Y1g?t=3

12. Further Reading
• Laney, Douglas. 3D Data Management: Controlling Data Volume, Velocity and Variety (PDF). Gartner. Retrieved 10 September 2015 at
http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
• Computing Research Association. White Paper on Challenges and Opportunities with Big Data. 2015. Last accessed on 10 September 2015 at
http://cra.org/ccc/wp-content/uploads/sites/2/2015/05/bigdatawhitepaper.pdf
• McKinsey Global Institute. Big data: The next frontier for innovation, competition, and productivity. 2011. Last accessed on 10 September 2015 at
http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
• World Economic Forum. Big Data, Big Impact: New Possibilities for International Development. 2012. Last accessed on 10 September 2015 at
http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf
• Ascent. Digital Preservation in the Age of Cloud and Big Data. 2015. Last accessed on 10 September 2015 at
https://atos.net/content/dam/global/ascent-whitepapers/ascent-whitepaper-digital-preservation-in-the-age-of-cloud-and-big-data.pdf
• NESSI. White Paper on Big Data A New World of Opportunities. 2012. Last accessed on 10 September 2015.
http://www.nessi-europe.com/Files/Private/NESSI_WhitePaper_BigData.pdf