A Statistician's View on Big Data and Data Science (Version 2)

A Statistician’s View on Big
Data and Data Science
Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting
Statistical Consulting + Data Analysis + Data Mining Services
Morgenstrasse 129, 3018 Berne, Switzerland
www.statoo.info
‘SMi Big Data in Pharma’, London, United Kingdom — May 12 & 13, 2014

Abstract
There is no question that big data have hit the business, gov-
ernment and scientiﬁc sectors. The demand for skills in data
science is unprecedented in sectors where value, competitiveness
and eﬃciency are driven by data. However, there is plenty of
misleading hype around the terms ‘big data’ and ‘data science’.
This presentation gives a professional statistician’s view on these
terms, illustrates the connection between data science and statis-
tics, and highlights some challenges and opportunities from a
statistical perspective.

About myself
• PhD in Statistics, Swiss Federal Institute of Technology (EPFL), Lausanne,
Switzerland.
• MSc in Mathematics, EPFL, Lausanne, Switzerland.
• CStat (‘Chartered Statistician’), Royal Statistical Society, United Kingdom.
• PStat (‘Accredited Professional Statistician’), American Statistical Association,
United States of America.
• CSci (‘Chartered Scientist’), Science Council, United Kingdom.
• Elected Member, International Statistical Institute, Netherlands.
• CEO, Statoo Consulting, Switzerland.
• Lecturer in Statistics, Geneva School of Economics and Management, University
of Geneva, Switzerland.
• President of the Swiss Statistical Society.
Copyright c 2001–2014, Statoo Consulting, Switzerland. All rights reserved.
2

About Statoo Consulting
• Founded Statoo Consulting in 2001.
• Statoo Consulting is a software-vendor independent Swiss consulting ﬁrm spe-
cialised in statistical consulting and training, data analysis and data mining ser-
vices.
• Statoo Consulting oﬀers consulting and training in statistical thinking, statistics
and data mining in English, French and German.
Are you drowning in uncertainty and starving for knowledge?
Have you ever been Statooed?
3

Who has already been Statooed?
• Statoo Consulting’s clients include Swiss and international companies like
ABB;
Alcan Aluminium Valais;
Alstom;
Arkema, France;
AstraZeneca, United Kingdom;
Barry Callebaut, Belgium;
Bayer Consumer Care;
Berna Biotech;
BMW, Germany;
Bosch, Germany;
Cargill;
Crédit Lyonnais, United Kingdom;
CSL Behring;
F. Hoffmann-La Roche;
GfK Telecontrol;
GlaxoSmithKline, United Kingdom;
H. Lundbeck, Denmark;
Lombard Odier Darier Hentsch;
Lonza;
McNeil, Sweden;
Merck Serono;
Mobiliar;
Nagra Kudelski Group;
Nestlé Frisco Findus;
Nestlé Research Center;
Novartis Pharma;
Novelis;
Novo Nordisk;
Pfizer, United Kingdom;
Philip Morris International;
Pioneer Hi-Bred, Germany;
PostFinance;
Procter & Gamble Manufacturing, Germany;
publisuisse;
Rodenstock, Germany;
Sanofi-Aventis, France;
Saudi Arabian Oil Company, Saudi Arabia;
Smith & Nephew Orthopaedics;
Swisscom Innovations;
Swissmedic;
The Baloise Insurance Company;
tl — Public Transport for the Lausanne Region;
Wacker Chemie, Germany;
upc cablecom;
4

as well as Swiss and international government agencies, nonprofit organisa-
tions, offices, research institutes and universities like
King Saud University, Saudi Arabia;
Lausanne Hotel School;
Institute for International Research, Dubai, United Arab Emirates;
Paul Scherrer Institute (PSI);
Statistical Office of the City of Berne;
Swiss Armed Forces;
Swiss Federal Institute for Forest, Snow and Landscape Research;
Swiss Federal Institutes of Technology Lausanne and Zurich;
Swiss Federal Office for Migration;
Swiss Federal Research Stations Agroscope Changins–Wädenswil and Reckenholz–
Tänikon;
Swiss Federal Statistical Office;
Swiss Institute of Bioinformatics;
Swiss State Secretariat for Economic Affairs (SECO);
The Gold Standard Foundation;
Unit of Education Research of the Department of Education, Canton of Geneva;
Universities of Applied Sciences of Northwestern Switzerland, Technology Buchs NTB
and Western Switzerland;
Universities of Berne, Fribourg, Geneva, Lausanne, Neuchatel and Zurich.
5

Contents
Contents 6
1. Demystifying the ‘big data’ hype 8
2. Demystifying the ‘data science’ hype 15
3. What distinguishes data science from statistics? 34
4. Outlook, challenges and opportunities (not only for statisticians!) 37
6

‘Data is [are] arguably the most important natural re-
source of this century. ... Big data is [are] big news
just about everywhere you go these days. Here in
Texas, everything is big, so we just call it data.’
Michael Dell, 2014
7

1. Demystifying the ‘big data’ hype
• There is no question that ‘big data’ have hit the business, government and scientific
sectors.
Indeed, the term ‘big data’ has acquired the trappings of religion!
However, there are a lot of examples of companies that were into ‘big data’ before
they were called ‘big data’ — a term coined in 1998 by two researchers at SGI.
• But, what exactly are ‘big data’?
In short, the term ‘big data’ applies to information that can not be processed or
handled using traditional processes or tools.
Big data are an architecture and most related challenges are IT focused!
8

• The following characteristics — known as ‘the four Vs’ or ‘V4
’ — provide one
standard deﬁnition of big data:
– ‘Volume’ : ‘data at rest’, i.e. the amount of data ( ‘data explosion problem’);
– ‘Variety’ : ‘data in many forms’, i.e. diﬀerent disparate types of data (e.g.
structured, semi-structured and unstructured, e.g. text, web or multimedia data
such as images, videos, audio), data sources (e.g. internal, external, public) and
data resolutions;
– ‘Velocity’ : ‘data in motion’, i.e. the speed by which data are generated and
need to be handled;
– ‘Veracity’ : ‘data in doubt’, i.e. the varying levels of noise and processing errors.
9

Example. Possible challenges for life sciences’ data are ‘variety’ and ‘veracity’ (and
the related quality and correctness of the data).
Especially the combination of diﬀerent data sources (e.g. combining omics data
generated by various high-throughput technologies) — resulting from ‘variety’ —
provides a lot of insights.
‘Volume’ and ‘velocity’ are often the least important issues for life sciences’ data.
• The above standard 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 ‘business’ framework to
communicate about how to solve different data processing challenges.
But, what is new?
10

“Big Data’ ... is the simple yet seemingly revolutionary
belief that data are valuable. ... I believe that ‘big’
actually means important (think big deal). Scientists
have long known that data could create new knowledge
but now the rest of the world, including government
and management in particular, has realised that data
can create value.’
Source: interview with Sean Patrick Murphy, a senior scientist at Johns Hopkins University
Applied Physics Laboratory, in the Big Data Innovation Magazine, September 2013.
11

‘Big data and analytics based on it promise to change
virtually every industry and business function over the
next decade.’
Thomas H. Davenport and Jinho Kim, 2013
Source: Davenport, T. H. & Kim, J. (2013). Keeping Up with the Quants: Your Guide to
Understanding and Using Analytics. Boston, MA: Harvard Business Review Press.
12

‘The term ‘big data’ is going to disappear in the next
two years, to become just ‘data’ or ‘any data’. But, of
course, the analysis of data will continue.’
Donald Feinberg, 2014
13

‘Data are not taken for museum purposes; they are
taken as a basis for doing something. ... The ultimate
purpose of taking data is to provide a basis for action
or a recommendation for action.’
W. Edwards Deming
14

2. Demystifying the ‘data science’ hype
• The demand for ‘data scientists’ — the ‘magicians of the big data era’ — is
unprecedented in sectors where value, competitiveness and efficiency are driven by
data.
• The Data Science Association defined in October 2013 the terms ‘data science’ and
‘data scientist’ within their Data Science Code of Professional Conduct as follows (see
www.datascienceassn.org/code-of-conduct.html):
– Data science is the scientific study of the creation, validation
and transformation of data to create meaning.
– A data scientist is a professional who uses scientific methods
to liberate and create meaning from raw data.
15

• ‘Data-Driven Decision making’ (DDD) refers to the practice of basing decisions
on data, rather than purely on intuition:
Source: Provost, F. & Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media.
16

• Data science has been dubbed by the Harvard Business Review (Thomas H. Dav-
enport and D. J. Patil, October 2012) as
‘the sexiest job in the 21st century ’
and by the New York Times (April 11, 2013) as a
‘ hot new ﬁeld [that] promises to revolutionise industries
from business to government, health care to academia’.
But, is data science really new and ‘sexy’?
17

• The term ‘data science’ was originally coined in 1998 by the statistician Chien-Fu
Jeff Wu when he gave his inaugural lecture at the University of Michigan.
Wu argued that statisticians should be renamed data scientists since they spent
most of their time manipulating and experimenting with data.
• In 2001, the statistician William S. Cleveland introduced the notion of ‘data science’
as an independent discipline.
Cleveland extended the field of statistics to incorporate ‘advances in computing
with data’ in his article ‘Data science: an action plan for expanding the technical
areas of the ﬁeld of statistics’ (International Statistical Review, 69, 21–26).
18

• Although the term ‘data scientist’ may be relatively new, this profession has existed
for a long time!
For example, Johannes Kepler published his first two laws of planetary motion,
which describe the motion of planets around the sun, in 1609.
Kepler found them by analysing the astronomical observations of Tycho Brahe.
Kepler was clearly a data scientist!
Or, for example, Napoleon Bonaparte (‘Napoleon I’) used mathematical models to
help make decisions on battlefields.
These models were developed by mathematicians — Napoleon’s own data scien-
tists!
19

Another (famous) example of that same time period is the following map (‘carte
figurative’) drawn by the French engineer Charles Joseph Minard in 1861 to show the
tremendous losses of Napoleon’s army during his Russian campaign in 1812-1813,
where more than 97% of the soldiers died.
Sources: Van der Lans, R. (2013). The data scientist at work. BeyeNETWORK,
October 24, 2013 (www.b-eye-network.com/view/17102).
Tufte, E. R. (2001). The Visual Display of Quantitative Information
(2nd edition). Cheshire, CT: Graphics Press.
20

Minard was clearly a data scientist!
21

‘Data science in a way is much older than Kepler — I
sometimes say that data science is the ‘second oldest’
occupation.’
Gregory Piatetsky-Shapiro, November 26, 2013
22

‘I keep saying the sexy job in the next ten years will be
statisticians.’
Hal Varian, 2009
Source: interview with Google’s chief economist in the McKinsey Quarterly, January 2009.
23

‘And with ongoing advances in high-performance com-
puting and the explosion of data, ... I would venture
to say that statistician could be the sexy job of the
century.’
James (Jim) Goodnight, 2010
24

‘I think he [Hal Varian] is behind — using statistics
has been the sexy job of the last 30 years. It has just
taken awhile for organisations to catch on.’
James (Jim) Goodnight, 2011
25

• Looking at all the ‘crazy’ hype in the media around the terms ‘big data’ and ‘data
science’, it seems that ‘data scientist’ is just a ‘sexed up’ term for ‘statistician’.
It looks like statisticians just needed a good marketing campaign!
26

But, what is statistics ?
– Statistics can be deﬁned as the science of ‘learning from data’ (or of making
sense out of data).
It includes everything from planning for the collection of data and subsequent
data management to end-of-the-line activities such as drawing conclusions of
numerical facts called data and presentation of results.
– Statistics is concerned with the study of uncertainty and with the study of deci-
sion making in the face of uncertainty.
27

• However, data science is not just a rebranding of statistics , large-scale statistics
or statistical science!
• Data science is rather a rebranding of ‘data mining’!
‘The terms ‘data science’ and ‘data mining’ often are
used interchangeably, and the former has taken a life
of its own as various individuals and organisations try
to capitalise on the current hype surrounding it.’
Foster Provost and Tom Fawcett, 2013
28

‘Data science is a train that is going 99% faster than
the rails its on can support. It could derail, go oﬀ a
cliﬀ, and become nothing but a failed fad.’
Mark Biernbaum, 2013
29

‘It is already time to kill the ‘data scientist’ title. ...
The data scientist term has come to mean almost any-
thing.’
Thomas H. Davenport, 2014
Source: Thomas H. Davenport’s article ‘It is already time to kill the ‘data scientist’ title’
in the Wall Street Journal, April 30, 2014.
30

But, what is data mining?
Data mining is the non-trivial process of identifying valid, novel, poten-
tially useful, and ultimately understandable patterns or structures or models
or trends or relationships in data to enable data-driven decision making.
‘Non-trivial’: it is not a straightforward computation of predeﬁned quantities like
computing the average value of a set of numbers.
‘Valid’: the patterns hold in general, i.e. being valid on new data in the face of
uncertainty.
‘Novel’: the patterns were not known beforehand.
‘Potentially useful’: lead to some beneﬁt to the user.
‘Understandable’: the patterns are interpretable and comprehensible — if not
immediately then after some postprocessing.
31

The data science (or data mining) Venn diagram
Source: Drew Conway, September 2010 (drewconway.com/zia/2013/3/26/the-data-science-venn-diagram).
32

‘Statistics has been the most successful information
science. Those who ignore statistics are condemned
to re-invent it.’
Brad Efron, 1997
33

3. 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’ .
• Data science (or data mining), on the other hand, typically is concerned with
analysing secondary (e.g. observational) data that have been collected for other
reasons. The usage of these data is to create new ideas (hypotheses).
Secondary data analysis or bottom-up (exploratory and predictive) analysis.
‘Idea (hypothesis) generation’ .
Knowledge discovery.
34

• The two approaches of ‘learning from data’ are complementary and should proceed
side by side — in order to enable proper data-driven decision making.
Example. When historical data are available the idea to be generated from a bottom-
up analysis (e.g. using a mixture of so-called ‘ensemble techniques’) could be
‘which are the most important (from a predictive point of view) factors
(among a ‘large’ list of candidate factors) that impact a given output?’.
Mixed with subject-matter knowledge this idea could result in a list of a ‘small’
number of factors (i.e. ‘the critical ones’).
The conﬁrmatory tools of top-down analysis (statistical ‘Design Of Experiments’,
DOE, in most of the cases) could then be used to conﬁrm and evaluate this idea.
By doing this, new data will be collected (about ‘all’ factors) and a bottom-up
analysis could be applied again — letting the data suggest new ideas to test.
35

‘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
36

4. Outlook, challenges and opportunities (not only for statisticians!)
• Data should be regarded as key strategic assets!
• Extracting useful knowledge from data to solve ‘business’ problems must be treated
systematically by following a process with reasonably well-deﬁned stages.
Like statistics, data science (or data mining) is not only modelling and prediction,
nor a product that can be bought, but a whole iterative problem solving cycle/process
that must be mastered through team effort.
Phases of the reference model of the methodology called CRISP-DM (‘CRoss
Industry Standard Process for Data Mining’; see www.statoo.com/CRISP-DM.pdf):
37

38

‘If I had only one hour to save the world, I would spend
fifty-five minutes defining the problem, and only five
minutes finding the solution.’
Albert Einstein
39

• One might rightly be sceptical about big data because the idea is still fuzzy and
there is an awful lot of marketing hype around.
However, big data are here to stay and will, over time, impact every single ‘busi-
ness’!
• There is convincing ‘evidence’ that data-driven decision making and big data tech-
nologies will substantially improve ‘business’ performance.
• Statistical rigour is necessary to justify the inferential leap from data to knowledge.
• Lack of expertise in statistics can lead (and has already led) to fundamental errors
(e.g. using omics data)!
40

‘To have any hope of extracting anything useful from
big data, ... , eﬀective inferential skills are vital. That
is, at the heart of extracting value from big data lies
statistics.’
David J. Hand, 2014
41

‘Statisticians have spent the past 200 years figuring
out what traps lie in wait when we try to understand
the world through data. The data are bigger, faster
and cheaper these days — but we must not pretend
that the traps have all been made safe. They have not.’
Tim Harford, 2014
Source: Tim Harford’s article ‘Big data: are we making a big mistake?’ in the
Financial Times Magazine, March 28, 2014.
42

‘There are a lot of small data problems that occur in
big data. They do not disappear because you have got
lots of the stuff. They get worse.’
David Spiegelhalter, 2014
43

• The challenges from a statistical perspective include
– the need to think really hard about a problem and to understand the underlying
mechanism that drive the processes that generate the data ( ask the ‘right’
question(s)!);
– the provenance of the data, e.g. the quality of the data, the characteristics of the
sample ( ‘sampling bias’, i.e. is the sample representative to the population it
was designed for?);
– the ethics of using (big) data, particularly in relation to personal data, i.e. ethical
issues related to privacy ( ‘information rules’ need to be deﬁned), confiden-
tiality (of shared private information), transparency (e.g. of data uses and data
users) and identity (i.e. data should not compromise identity);
44

– the visualisation of the data;
– the validity of generalisation, and the replicability and reproducibility of findings;
– the balance of humans and computers.
‘Data and algorithms alone will not fulﬁl the promises
of big data. Instead, it is creative humans who need
to think very hard about a problem and the underlying
mechanisms that drive those processes.’
David Park, 2014
45

‘The numbers have no way of speaking for themselves.
We speak for them. We imbue them with meaning. ...
Data-driven predictions can succeed — and they can
fail. It is when we deny our role in the process that
the odds of failure rise. Before we demand more of
our data, we need to demand more of ourselves.’
Nate Silver, 2012
Source: Silver, N. (2012). The Signal and The Noise: Why Most Predictions Fail but Some Do Not.
New York, NY: The Penguin Press.
46

‘As big data [data science] and statistics engage with
one another, it is critical to remember that the two
fields are united by one common goal: to draw reliable
conclusions from available data.’
Kaiser Fung, 2013
Source: Fung, K. (2013). The pending marriage of big data and statistics. Signiﬁcance, 10(4), 22–25.
47

‘Most of my life I went to parties and heard a little
groan when people heard what I did. Now they are all
excited to meet me.’
Robert Tibshirani, 2012
Source: interview with Robert Tibshirani, a statistics professor at Stanford University,
in the New York Times on January 26, 2012.
48

Have you been Statooed?
Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting
Morgenstrasse 129
3018 Berne
Switzerland
email kuonen@statoo.com
@DiegoKuonen
web www.statoo.info
/Statoo.Consulting

Copyright c 2001–2014 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, me-
chanical, 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: ‘SMi.BigDataPharma.London.May.2014’.
Typesetting: LATEX, version 2 . PDF producer: pdfTEX, version 3.141592-1.40.3-2.2 (Web2C 7.5.6).
Compilation date: 14.05.2014.

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