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Big Data, Data Science, Machine
Intelligence and Learning:
Demystification, Trends, Challenges and
Opportunities for Official...
About myself (about.me/DiegoKuonen)
PhD in Statistics, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland...
1. Demystifying the ‘big data’ hype
• ‘Big data’ have hit the business, government and scientific sectors.
The term ‘big d...
• The following characteristics — ‘the four Vs’ — provide a definition:
– ‘Volume’ : ‘data at rest’, i.e. the amount of dat...
– ‘Veracity’ : ‘data in doubt’ or ‘trust in data’, i.e. the varying levels of noise and
processing errors, including the r...
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
9
• The above definition of big data is vulnerable to the criticism of sceptics that these
four Vs have always been there.
Ne...
‘Data are an infrastructural resource — a form of
capital that cannot be depleted. ... Data can be used
and re-used to ope...
‘Data is part of Switzerland’s infrastructure, such as
road, railways and power networks, and is of great
value. The gover...
Intermediate summary: the ‘five Vs’ of big data
‘Volume’, ‘Variety’ and ‘Velocity’ are the ‘essential’ characteristics of b...
‘Data are not taken for museum purposes; they are
taken as a basis for doing something. If nothing is to
be done with the ...
2. Demystifying the two approaches of ‘learning from data’
Data science, statistics and their connection
• The demand for ...
• Is data science ‘statistical d´ej`a vu’?
But, what is ‘statistics’?
Statistics is the science of ‘learning from data’ (o...
What distinguishes data science from statistics?
• Statistics traditionally is concerned with analysing primary (e.g. expe...
• Data science, on the other hand, typically is concerned with analysing secondary
(e.g. observational or ‘found’ or ‘orga...
• The two approaches of ‘learning from data’ are complementary and should proceed
side by side — in order to enable proper...
‘Neither exploratory nor confirmatory is sufficient
alone. To try to replace either by the other is
madness. We need them ...
3. Demystifying the ‘machine intelligence and learning’ hype
John McCarthy, one of the founders of ‘Artificial Intelligence...
‘In short, the biggest difference between AI then and
now is that the necessary computational capacity,
raw volumes of dat...
Source: ‘Historical cost of computer memory and storage’ (hblok.net/blog/storage/).
Copyright c 2001–2017, Statoo Consulti...
• However, without humans as a guide, current AI is no more capable than a computer
without software!
‘Business is not che...
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
25
‘In the anticipated symbiotic [man–computer]
partnership, men will set the goals, formulate the
hypotheses, determine the ...
4. Data-informed policy making
‘Without data we are flying blind, and we can not do
evidence-based policy decisions — or a...
• Policy makers want the top of the iceberg, but they need to remember the stuff
beneath sea (adapted from @HetanShah):
• ...
‘Data are the lifeblood of decision-making and the
raw material for accountability. Without high-quality
data providing th...
The policy cycle and the (big) data-revised policy cycle
The revised policy cycle (right) takes into account (big) data an...
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
31
5. Conclusion and opportunities
• Decision making that was once based on hunches and intuition should be driven by
data ( ...
Technology is not the real challenge of the digital transformation!
Digital is not about the technologies (which change to...
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
34
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
35
‘We can not solve problems by using the same kind
of thinking we used when we created them.’
Albert Einstein
Copyright c 2...
‘The only person who likes change is a wet baby.’
Mark Twain
Copyright c 2001–2017, Statoo Consulting, Switzerland. All ri...
‘It is not necessary to change. Survival is not
mandatory.’
W. Edwards Deming
Another version by W. Edwards Deming: ‘Survi...
Have you been Statooed?
Prof. Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting
Morgenstrasse 129
3018 Berne
Switzerlan...
Copyright c 2001–2017 by Statoo Consulting, Switzerland. All rights reserved.
No part of this presentation may be reprinte...
Big Data, Data Science, Machine Intelligence and Learning: Demystification, Trends, Challenges and Opportunities for Offic...
Big Data, Data Science, Machine Intelligence and Learning: Demystification, Trends, Challenges and Opportunities for Offic...
Big Data, Data Science, Machine Intelligence and Learning: Demystification, Trends, Challenges and Opportunities for Offic...
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Big Data, Data Science, Machine Intelligence and Learning: Demystification, Trends, Challenges and Opportunities for Official Statistics in the Context of Data-Informed Policy Making

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Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on March 14, 2017 at Eurostat's international conference `New Techniques and Technologies for Statistics (NTTS) 2017' in Brussels, Belgium.

The presentation is also available at http://www.statoo.com/BigDataDataScience/.

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Big Data, Data Science, Machine Intelligence and Learning: Demystification, Trends, Challenges and Opportunities for Official Statistics in the Context of Data-Informed Policy Making

  1. 1. Big Data, Data Science, Machine Intelligence and Learning: Demystification, Trends, Challenges and Opportunities for Official Statistics in the Context of Data-Informed Policy Making Prof. Dr. Diego Kuonen, CStat PStat CSci Statoo Consulting, Berne, Switzerland @DiegoKuonen + kuonen@statoo.com + www.statoo.info ‘Keynote Address @ NTTS 2017’, Brussels, Belgium — March 14, 2017
  2. 2. About myself (about.me/DiegoKuonen) PhD in Statistics, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. MSc in Mathematics, EPFL, Lausanne, Switzerland. • CStat (‘Chartered Statistician’), Royal Statistical Society, UK. • PStat (‘Accredited Professional Statistician’), American Statistical Association, USA. • CSci (‘Chartered Scientist’), Science Council, UK. • Elected Member, International Statistical Institute, NL. • Senior Member, American Society for Quality, USA. • President of the Swiss Statistical Society (2009-2015). Founder, CEO & CAO, Statoo Consulting, Switzerland (since 2001). Adjunct Professor of Data Science, Research Center for Statistics (RCS), Geneva School of Economics and Management (GSEM), University of Geneva, Switzerland. Principal Scientific and Strategic Big Data and Data Science Advisor and Consultant for the Directorate, the Board of Management and the ‘New Data Sources’ working group of the Swiss Federal Statistical Office (FSO), Neuchˆatel, Switzerland. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 2
  3. 3. 1. Demystifying the ‘big data’ hype • ‘Big data’ have hit the business, government and scientific sectors. The term ‘big data’ — coined in 1997 by two researchers at the NASA — has acquired the trappings of religion. • But, what exactly are ‘big data’? The term ‘big data’ applies to an accumulation of data that can not be processed or handled using traditional data management processes or tools. Big data are a data management infrastructure which should ensure that the underlying hardware, software and architecture have the ability to enable ‘learning from data’, i.e. ‘analytics’ ( ‘data-driven decision making’). Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 6
  4. 4. • The following characteristics — ‘the four Vs’ — provide a definition: – ‘Volume’ : ‘data at rest’, i.e. the amount of data ( ‘data explosion problem’), with respect to the number of observations ( ‘size’ of the data), but also with respect to the number of variables ( ‘dimensionality’ of the data); – ‘Variety’ : ‘data in many forms’, ‘mixed data’ or ‘broad data’, i.e. different types of data (e.g. structured, semi-structured and unstructured, e.g. log files, text, web or multimedia data such as images, videos, audio), data sources (e.g. internal, external, open, public), data resolutions (e.g. measurement scales and aggregation levels) and data granularities; – ‘Velocity’ : ‘data in motion’ or ‘fast data’, i.e. the speed by which data are generated and need to be handled (e.g. streaming data from devices, machines, sensors and social data); Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 7
  5. 5. – ‘Veracity’ : ‘data in doubt’ or ‘trust in data’, i.e. the varying levels of noise and processing errors, including the reliability (‘quality over time’), capability and validity of the data. • ‘Volume’ is often the least important issue: it is definitely not a requirement to have a minimum of a petabyte of data, say. Bigger challenges are ‘variety’ (e.g. combining different data sources such as internal data with social networking data and public data) and ‘velocity’, but most important is ‘veracity’ and the related quality of the data . Indeed, big data come with the data quality and data governance challenges of ‘small’ data along with new challenges of its own! Existing ‘small’ data quality frameworks need to be extended, i.e. augmented! Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 8
  6. 6. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 9
  7. 7. • The above definition of big data is vulnerable to the criticism of sceptics that these four Vs have always been there. Nevertheless, the definition provides a clear and concise framework to communicate about how to solve different data processing challenges. But, what is new? Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 10
  8. 8. ‘Data are an infrastructural resource — a form of capital that cannot be depleted. ... Data can be used and re-used to open up significant growth opportunities, or to generate benefits across society in ways that could not be foreseen when the data were created.’ OECD’s Directorate for Science, Technology and Innovation (STI), OECD STI Policy Note, October 2015 Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 11
  9. 9. ‘Data is part of Switzerland’s infrastructure, such as road, railways and power networks, and is of great value. The government and the economy are obliged to generate added value from these data. Moreover, the state must play a pioneering role with its data.’ digitalswitzerland, November 22, 2016 Source: digitalswitzerland’s ‘Digital Manifesto for Switzerland’ (digitalswitzerland.com). The 5th V of big data: ‘Value’ , i.e. the ‘usefulness of data’. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 12
  10. 10. Intermediate summary: the ‘five Vs’ of big data ‘Volume’, ‘Variety’ and ‘Velocity’ are the ‘essential’ characteristics of big data; ‘Veracity’ and ‘Value’ are the ‘qualification for use’ characteristics of big data. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 13
  11. 11. ‘Data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action.’ W. Edwards Deming, 1942 Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 14
  12. 12. 2. Demystifying the two approaches of ‘learning from data’ Data science, statistics and their connection • The demand for ‘data scientists’ — the ‘magicians of the big data era’ — is unprecedented in sectors where value, competitiveness and efficiency are data-driven. The term ‘data science’ was originally coined in 1997 by a statistician. Data science — a rebranding of ‘data mining’ — is the non-trivial process of identifying valid (that is, the patterns hold in general, i.e. being valid on new data in the face of uncertainty), novel, potentially useful and ultimately understandable patterns or structures or models or trends or relationships in data to enable data-driven decision making. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 15
  13. 13. • Is data science ‘statistical d´ej`a vu’? But, what is ‘statistics’? Statistics is the science of ‘learning from data’ (or of making sense out of data), and of measuring, controlling and communicating uncertainty. It is a process that includes everything from planning for the collection of data and subsequent data management to end-of-the-line activities such as drawing conclusions of data and presentation of results. Uncertainty is measured in units of probability, which is the currency (or grammar) of statistics. Statistics is concerned with the study of data-driven decision making in the face of uncertainty. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 16
  14. 14. What distinguishes data science from statistics? • Statistics traditionally is concerned with analysing primary (e.g. experimental) data that have been collected to explain and check the validity of specific existing ideas (hypotheses). Primary data analysis or top-down (explanatory and confirmatory) analysis. ‘Idea (hypothesis) evaluation or testing’ . ‘Deductive reasoning’ as ‘idea (theory) first’. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 17
  15. 15. • Data science, on the other hand, typically is concerned with analysing secondary (e.g. observational or ‘found’ or ‘organic’) data that have been collected (and designed) for other reasons (and not ‘under control’ of the investigator) to create new ideas (hypotheses). Secondary data analysis or bottom-up (exploratory and predictive) analysis. ‘Idea (hypothesis) generation’ . ‘Inductive reasoning’ as ‘data first’. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 18
  16. 16. • The two approaches of ‘learning from data’ are complementary and should proceed side by side — in order to enable proper data-driven decision making and to enable continuous improvement. Source: Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 19
  17. 17. ‘Neither exploratory nor confirmatory is sufficient alone. To try to replace either by the other is madness. We need them both.’ John W. Tukey, 1980 Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 20
  18. 18. 3. Demystifying the ‘machine intelligence and learning’ hype John McCarthy, one of the founders of ‘Artificial Intelligence’ (AI) (now sometimes referred to as ‘machine intelligence’) research, defined in 1956 the field of AI as ‘getting a computer to do things which, when done by people, are said to involve intelligence’, e.g. visual perception, speech recognition and language translation. AI is about (smart) machines capable of performing tasks normally performed by humans ( ‘learning machines’), i.e. ‘making machines smart’. In 1959, Arthur Samuel defined ‘Machine Learning’ (ML) as one part of a larger AI framework ‘that gives computers the ability to learn’. ML explores the study and construction of algorithms that can learn from and make predictions on data, i.e. ‘prediction making’ through the use of computers. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 21
  19. 19. ‘In short, the biggest difference between AI then and now is that the necessary computational capacity, raw volumes of data, and processing speed are readily available so the technology can really shine.’ Kris Hammond, September 14, 2015 Source: Kris Hammond, ‘Why artificial intelligence is succeeding: then and now’, Computerworld, September 14, 2015 (goo.gl/Q3giSn). Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 22
  20. 20. Source: ‘Historical cost of computer memory and storage’ (hblok.net/blog/storage/). Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 23
  21. 21. • However, without humans as a guide, current AI is no more capable than a computer without software! ‘Business is not chess; smart machines alone can not win the game for you. The best that they can do for you is to augment the strengths of your people.’ Thomas H. Davenport, August 12, 2015 Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 24
  22. 22. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 25
  23. 23. ‘In the anticipated symbiotic [man–computer] partnership, men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. Computing machines will do the routinizable work that must be done to prepare the way for insights and decisions in technical and scientific thinking. ... In one sense of course, any man-made system is intended to help man, to help a man or men outside the system.’ Joseph C. R. Licklider, 1960 Source: Licklider, J. C. R. (1960). Man–computer symbiosis. IRE Transactions on Human Factors in Electronics, 1, 4–11. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 26
  24. 24. 4. Data-informed policy making ‘Without data we are flying blind, and we can not do evidence-based policy decisions — or any decision at all.’ Johannes P. J¨utting, 2015 Source: Johannes J¨utting, Manager of the Partnership in Statistics for Development in the 21st Century (Paris21), quoted in Sarah Shearman’s article ‘Data ’crucial’ to eradicating poverty’ in the Guardian, September 28, 2015 (goo.gl/DBTwza). Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 27
  25. 25. • Policy makers want the top of the iceberg, but they need to remember the stuff beneath sea (adapted from @HetanShah): • In a world of post-truth politics, the veracity of (big) data is more important than ever! Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 28
  26. 26. ‘Data are the lifeblood of decision-making and the raw material for accountability. Without high-quality data providing the right information on the right things at the right time; designing, monitoring and evaluating effective policies becomes almost impossible.’ IEAG, 2014 Source: United Nations Secretary-General’s ‘Independent Expert Advisory Group on a Data Revolution for Sustainable Development’ (IEAG), A Word That Counts: Mobilising The Data Revolution for Sustainable Development, November 6, 2014 (www.undatarevolution.org/report/). Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 29
  27. 27. The policy cycle and the (big) data-revised policy cycle The revised policy cycle (right) takes into account (big) data analytics using data-informed continuous evaluation at any stage ( ‘e-policy cycle’). Source: H¨ochtl, J., Parycek, P. & Sch¨ollhammer, R. (2016). Big data in the policy cycle: policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26, 147–169. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 30
  28. 28. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 31
  29. 29. 5. Conclusion and opportunities • Decision making that was once based on hunches and intuition should be driven by data ( data-driven decision making). • The key elements for a successful (big) data analytics and data science future are statistical principles and rigour of humans! • Statistics, (big) data analytics and data science are aids to thinking and not replacements for it! • Data are key for policy making and for accountability in all countries of the world ( data-informed policy making). They should be envisaged to complement and augment (official) statistics, not replacements for it! Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 32
  30. 30. Technology is not the real challenge of the digital transformation! Digital is not about the technologies (which change too quickly)! Note: edge computing is also referred to as fog computing, mesh computing, dew computing and remote cloud. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 33
  31. 31. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 34
  32. 32. Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 35
  33. 33. ‘We can not solve problems by using the same kind of thinking we used when we created them.’ Albert Einstein Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 36
  34. 34. ‘The only person who likes change is a wet baby.’ Mark Twain Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 37
  35. 35. ‘It is not necessary to change. Survival is not mandatory.’ W. Edwards Deming Another version by W. Edwards Deming: ‘Survival is not compulsory. Improvement is not compulsory. But improvement is necessary for survival.’ Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved. 38
  36. 36. Have you been Statooed? Prof. Dr. Diego Kuonen, CStat PStat CSci Statoo Consulting Morgenstrasse 129 3018 Berne Switzerland email kuonen@statoo.com @DiegoKuonen web www.statoo.info
  37. 37. Copyright c 2001–2017 by Statoo Consulting, Switzerland. All rights reserved. No part of this presentation may be reprinted, reproduced, stored in, or introduced into a retrieval system or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording, scanning or otherwise), without the prior written permission of Statoo Consulting, Switzerland. Warranty: none. Trademarks: Statoo is a registered trademark of Statoo Consulting, Switzerland. Other product names, company names, marks, logos and symbols referenced herein may be trademarks or registered trademarks of their respective owners. Presentation code: ‘NTTS.2017/MyKeynoteAddress’. Typesetting: LATEX, version 2 . PDF producer: pdfTEX, version 3.141592-1.40.3-2.2 (Web2C 7.5.6). Compilation date: 14.03.2017.

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