“Big Data” is a term that’s come from nowhere in the last 5 years or so, and is now practically ubiquitous within IT. But is it useful or even meaningful? Doesn’t it put too much emphasis on size over content or value? Does it add anything to discussions at all? Or does it actually impede communication, by obscuring crucial differences between diverse kinds of data that all require different tools, algorithms and strategies? (Talk presented at "Big Data for the Public Sector and Business Enterprise", London 2013)

2. There’s no such thing as “Big Data”
Dr. Andrew Clegg
Data Analytics & Visualization Team
Pearson Technology
Twitter: @andrew_clegg
Opinions are my own.

3. Speaking of Twitter…
Many companies think they have a “big data” problem
when they really have a big “data problem.”
-- @dbasch (Diego Basch), 17 Nov 2012
Followed the next day by:
I interact with companies whose “Big Data” problems
can be solved on laptops.
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4. What is “Big Data” anyway?
Customer preference and behaviour data is.
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5. What is “Big Data” anyway?
Apparently sensor data is too.
Image from http://en.wikipedia.org/wiki/Wind_farm (cc) Tomasz Sienicki
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6. What is “Big Data” anyway?
So is web crawl data.
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7. What is “Big Data” anyway?
Definitely social media data…
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8. What is “Big Data” anyway?
… and social media metadata.
Image from http://www.facebook.com/note.php?note_id=469716398919
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9. What is “Big Data” anyway?
Video, audio and other media, surely.
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10. What is “Big Data” anyway?
Health, medical and life sciences data might well be.
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11. What is “Big Data” anyway?
So particle physics data must be.
Image from Flickr (cc) “Image Editor”
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12. What do all these types of data have in common?
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13. What do all these types of data have in common?
Nothing.
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14. What about size?
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15. Size isn’t everything.
All laptop-sized data sets.
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16. What about storage?
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17. Pick the right architecture for the job.
Table-structured, Relational database
E-commerce data
transactional (good old SQL)
Simple, high-volume, Columnar database,
Sensor data
often aggregated Hadoop sequencefile
JSON-based, textual, Document database,
Social feeds e.g. Twitter
nested structures search engine
Networks e.g. social Directed or undirected Graph database
connections graphs (Aurelius, Neo4j)
Processed sequentially, Distributed filesystem
Multimedia
opaque internal structure (HDFS, Mogile, S3)
Biochemical sequences Complex 2D or 3D Specialized file formats
and models structure and tools
These are just examples. Make the right choice for your application.
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18. Pick the right architecture for the job.
Relational database
(good old SQL)
Columnar database,
NoSQL?
Hadoop sequencefile
Document database,
These are all as different from each other… search engine
Graph database
… as each one of them is from this. (Aurelius, Neo4j)
Distributed filesystem
(HDFS, Mogile, S3)
Specialized file formats
and tools
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19. What about analysis?
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20. Pick the right methodology for the job.
Text → topic modelling, sentiment analysis, information extraction
E-commerce data → propensity analysis, collaborative filtering
Multimedia → speech-to-text, audio fingerprinting, face recognition
Clickstream logs → frequent pattern mining, sequence analysis
Proton-proton collisions from LHC → I have absolutely no idea
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21. So why does any of this matter?
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22. Because all of these phrases…
“rethinking our Big Data strategy”
“enterprise-ready Big Data solution”
“facing the Big Data challenge”
… are pretty close to meaningless.
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23. And statements like these…
“We can’t do any of that clever Big Data stuff because we’re not
collecting or storing any Big Data.”
“We can’t do any of that clever Big Data stuff because we don’t
have big enough hardware or expensive tools.”
… are signs of opportunities being missed.
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24. What should we call it then, Andy?
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25. What should we call it then, Andy?
Data.
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26. Thank you for listening!
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