Production Processes of Official Statistics & Data Innovation Processes Augmented by Trusted Smart Statistics: Friends or Foes?

Production Processes of Oﬃcial
Statistics & Data Innovation
Processes Augmented by Trusted
Smart Statistics: Friends or Foes?
Prof. Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting, Berne, Switzerland
@DiegoKuonen + kuonen@statoo.com + www.statoo.info
‘Keynote Speech @ BDES 2018’, Soﬁa, Bulgaria — May 15, 2018

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).
Professor of Data Science, Research Center for Statistics (RCS), Geneva School of Economics
and Management (GSEM), University of Geneva, Switzerland (since 2016).
Founding Director of GSEM’s new MSc in Business Analytics program (started fall 2017).
Principal Scientific and Strategic Big Data Analytics Advisor for the Directorate and Board of
Management, Swiss Federal Statistical Office (FSO), Neuchâtel, Switzerland (since 2016).
Copyright c 2001–2018, Statoo Consulting, Switzerland. All rights reserved.
2

About Statoo Consulting (www.statoo.info)
• Founded Statoo Consulting in 2001.
2018 − 2001 = 17 + .
• Statoo Consulting is a software-vendor independent Swiss consulting ﬁrm
specialised in statistical consulting and training, data analysis, data mining
(data science) and big data analytics services.
• Statoo Consulting oﬀers consulting and training in statistical thinking, statistics,
data mining and big data analytics in English, French and German.
Are you drowning in uncertainty and starving for knowledge?
Have you ever been Statooed?

‘Just as haute cuisine must incessantly reinvent itself
in order to stay at the forefront of gastronomy,
official statistics is also confronted with a rapidly
changing context and needs. They are currently
facing an impressive number of challenges: the ‘data
revolution’ and the emergence of ‘big data’, the race
for eﬃciency which requires us to do ever better with
ever fewer resources, the need to measure new and
complex phenomena, such as sustainability, not to
mention the increasingly pressing calls for more
factual, evidence-based policies.’
Walter J. Radermacher, 2018
Source: Radermacher, W. J. (2018). Oﬃcial statistics in the era of big data opportunities and threats.
International Journal of Data Science and Analytics (doi.org/10.1007/s41060-018-0124-z33).
6

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 a ‘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 IT infrastructure which should ensure that the
underlying hardware, software and architecture have the ability to enable ‘learning
from data’ or ‘making sense out of data’, i.e. ‘analytics’ ( ‘data-driven decision
making’ and ‘data-informed policy making’).
7

The ‘Veracity’ (i.e. ‘trust in data’), including the reliability (‘quality over time’),
capability and validity of the data, and the related quality of the data are key!
Existing ‘small’ data quality frameworks need to be extended, i.e. augmented!
8

‘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.’
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’.
9

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.
10

11

2. Demystifying the ‘Internet of things’ hype
• The term ‘Internet of Things’ (IoT) — coined in 1999 by the technologist Kevin
Ashton — starts acquiring the trappings of a ‘new religion’!
Source: Christer Bodell, ‘SAS Institute and IoT’, May 30, 2017 (goo.gl/cVYCKJ).
However, IoT is about data, not things!
12

13

The ‘ﬁve Vs’ of IoT (data)
‘Volume’, ‘Variety’ and ‘Velocity’ are the ‘essential’ characteristics of IoT (data);
‘Veracity’ and ‘Value’ are the ‘qualiﬁcation for use’ characteristics of IoT (data).
14

‘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
Data are the fuel and analytics, i.e. ‘learning from data’ or ‘making sense
out of data’, is the engine of the digital transformation and the related data
revolution!
15

3. Demystifying the two approaches of analytics
Statistics, data science and their connection
Statistics traditionally is concerned with analysing primary (e.g. experimental or
‘made’ or ‘designed’) data that have been collected (and designed) for statistical
purposes to explain and check the validity of specific existing ‘ideas’ (‘hypotheses’),
i.e. through the operationalisation of theoretical concepts.
Primary analytics or top-down (i.e. explanatory and confirmatory) analytics.
‘Idea (hypothesis) evaluation or testing’ .
Analytics’ paradigm: ‘deductive reasoning’ as ‘idea (theory) ﬁrst’.
16

Data science — a rebranding of ‘data mining’ and as a term coined in 1997 by a
statistician — on the other hand, typically is concerned with analysing secondary
(e.g. observational or ‘found’ or ‘organic’ or ‘convenience’) data that have been
collected (and designed) for other reasons (and often not ‘under control’ or
without supervision of the investigator) to create new ideas (hypotheses or
theories).
Secondary analytics or bottom-up (i.e. exploratory and predictive) analytics.
‘Idea (hypothesis) generation’ .
Analytics’ paradigm: ‘inductive reasoning’ as ‘data ﬁrst’.
17

‘AI [(‘Artiﬁcial Intelligence’)] algorithms are not
natively ‘intelligent’. They learn inductively by
analyzing data.’
Sam Ransbotham, David Kiron, Philipp Gerbert and Martin Reeves, 2017
Source: Ransbotham, S., Kiron, D., Gerbert, P. & Reeves M. (2017). Reshaping Business With Artiﬁcial
Intelligence. MIT Sloan Management Review & The Boston Consulting Group (goo.gl/wnGqr3).
18

• The two approaches of analytics, i.e. deductive and inductive reasoning, are
complementary and should proceed iteratively and side by side in order to enable
data-driven decision making, data-informed policy making and proper
continuous improvement.
The inductive–deductive reasoning cycle:
Source: Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799.
19

‘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
20

Intermediate summary and demystifying ‘data innovation’
• In a world of (big) data and IoT (data), the veracity of data, i.e. the trustworthiness
of data, including the related data quality, is more important than ever!
21

• Analytics is an aid to thinking and not a replacement for it!
• Analytics should be envisaged to complement and augment (official) statistics, and
not a replacement for it!
Nowadays, with the digital transformation and the related data revolution, humans
need to augment their strengths to become more ‘powerful’: by automating
any routinisable work and by focusing on their core competences.
22

Technology is not the real challenge of the digital transformation!
Digital is not about the technologies (which change too quickly)!
23

Available at goo.gl/X27FGq .
• Current key challenges: (glocalised) standards of both IoT data and analytics, and
of ‘analytics of things’, i.e. IoT’s ‘analytics layer’, approaches, e.g. ‘edge analytics’.
Standardisation efforts needed (by oﬃcial statistics by augmenting existing ones?)!
24

‘Digital strategies ... go beyond the technologies
themselves. ... They target improvements in
innovation, decision making and, ultimately,
transforming how the business works.’
Gerald C. Kane, Doug Palmer, Anh N. Phillips, David Kiron and Natasha Buckley, 2015
Source: Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D. & Buckley, N. (2015). Strategy, not technology,
drives digital transformation. MIT Sloan Management Review (goo.gl/Dkb96o).
25

Available at goo.gl/tW85FP in English, German, French and Italian.
26

27

‘All improvement takes place project by project and
in no other way.’
Joseph M. Juran, 1989
28

‘It is getting better. . . A little better all the time.’
The Beatles, 1967
29

Do not let culture eat strategy — have them feed each other!
Culture change is key in the digital transformation!
30

‘If you can not describe what you are doing as a
process, you do not know what you are doing.’
W. Edwards Deming
31

4. Process models for continuous improvement
• The ‘Plan–Do–Check–Act’ (PDCA) cycle is often referred to as the Deming
cycle, Deming wheel or the Shewhart cycle.
Walter A. Shewhart proposed this approach in the ﬁeld of ‘quality control’ in the
1920s, and W. Edwards Deming later popularised PDCA as a general management
approach based on the scientiﬁc method.
32

The related ‘Plan–Do–Study–Act’ (PDSA) cycle
Source: Moen, R. D. & Norman, C. L. (2010). Circling back: clearing up myths about the Deming cycle
and seeing how it keeps evolving. Quality Progress, 43(11), 22–28.
33

‘Quality is never an accident, it is always the result of
intelligent eﬀort.’
John Ruskin
34

A process model for the production of oﬃcial statistics
• The ‘Generic Statistical Business Process Model’ ( GSBPM ) — coordinated
through the ‘United Nations Economic Commission for Europe’ (Version 5.0 as of
December 2013) — is consistent with the PDCA or PDSA cycles:
35

• GSBPM is a key conceptual framework for the modernisation (and standardisation
of the production) of official statistics.
But, where is the continuous (quality) improvement cycle?
36

‘The author believes the reason [operational] cost [of
diﬀerent parts of the statistical business process] has
not been a central focus is a diﬀerence between NSIs
focus on measuring quality of their products and
services, rather than continuous improvement of
quality.’
David A. Marker, 2017
Source: Marker, D. A. (2017). How have national statistical institutes improved quality
in the last 25 years? Statistical Journal of the IAOS, 33, 951–961.
Emphasis needs to move from measuring quality to improving quality!
37

Moreover, the GSBPM is a deductive reasoning and a sequential approach.
For example, the ﬁrst GSBPM steps are entirely focused on deductive reasoning for
primary data collection and are not suited for inductive reasoning applied to (already
existing) secondary data.
Moreover, the evaluation (‘Evaluate’ step) is only performed at the end.
This process model needs to be adapted to incorporate data innovation by taking
into account both approaches of analytics (i.e. inductive and deductive reasoning)
and through the usage of, for example, data-informed continuous evaluation at any
GSBPM step.
Current production processes of official statistics need to be augmented
and empowered by data innovation!
38

A process model for data innovation
• The CRISP-DM (‘CRoss Industry Standard Process for Data Mining’) process
— initially conceived in 1996 — is also consistent with the PDCA or PDSA cycles:
39

The complementary cycles of developing & deploying ‘analytical assets’
Source: Erick Brethenoux, Director, IBM Analytics Strategy & Initiatives, August 18, 2016 (goo.gl/AhsG1n).
40

‘The key to success is to make sure that the
beginning and ending steps of the analysis are well
thought out.’
Thomas H. Davenport and Jinho Kim, 2013
41

GSBPM («current statistical production»)
«Data innovation process model»
?
?

society,
economy policy,
media,
researchNSI
Public sector
collection processing
«Handle the new in new ways»
«Push computation out (partially)»
Source: Eurostat (May 2018)
processingprocessingprocessingprocessing
processingprocessingprocessingprocessingprocessingprocessingprocessingprocessing
Private sector
processing
New computational models must be adopted
between private and public actors
to guarantee mutual trust in the process.
Guarantee that data are processed
for the agreed purpose, by the
agreed method, respect of user
privacy & business confidentiality,
compliancy with legal provisions.
Trusted Smart
Statistics

Statistical
methods
and algorithms
Data platform
(data centre)
An output-driven value chain for official statistics
Validation
Integration
Official statistics mandate
● Legal bases
Processing
Thematic leaders
Politics
Instrumental
interpretation of data
Which data from
outside official
statistics help to
respond to information?
Which requirements
must data fulfil?
Data
Statistics (traditional)
deductive interpretation
of data
Lifestyle typology
Stratification theory
Relevance
of data
Long/short-term analysis
Process of lessons learned
© BFS – Diffusion und Amtspublikationen
Data innovation
Inductive
interpretation
of data
FSO data
Official
statistics
data
Administrative/register data
Data from universities etc.
Data from individuals
Service
Development
Stakeholder management
Sounding boards
Issues concerning
society as a whole
Information needs
(as driver for products) Services
Factory
Data
Ability to react
System-relevant events
New priorities
RIGHT TO A SAY
Analysis, visualisation,
contextualisation
Veracity
Quality and
veracity of data
Value
Added value
generated
by data
Editing,
machine readability,
metadata
Standardisation
Lessons learned

‘Coming together is a beginning. Keeping together is
progress. Working together is success.’
Henry Ford
45

As soon as it works, no one calls it ‘production process of official statistics
empowered by data innovation and augmented by trusted smart statistics’ any
more!
46

‘The transformation can only be accomplished by
man, not by hardware (computers, gadgets,
automation, new machinery). A company can not
buy its way into quality.’
W. Edwards Deming, 1982
47

‘The only person who likes change is a wet baby.’
Mark Twain
48

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

Copyright c 2001–2018 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: ‘BDES.2018/MyKeynote’.
Typesetting: LATEX, version 2 . PDF producer: pdfTEX, version 3.141592-1.40.3-2.2 (Web2C 7.5.6).
Compilation date: 09.05.2018.

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