The document discusses big data and analytics in three concise summaries:
1. It defines big data using the "five Vs" - volume, variety, velocity, veracity, and value. It explains that volume is less important than variety, velocity, and veracity (data quality). The goal of big data is to enable learning from data through analytics.
2. It distinguishes data science from statistics, noting that data science typically analyzes secondary data using inductive reasoning to generate hypotheses, while statistics analyzes primary data using deductive reasoning to test hypotheses. Both approaches are needed for proper data-driven decision making.
3. It explains that analytics aims to describe what happened (descriptive),
The Power of Data Insights - Big Data as the Fuel and Analytics as the Engine...Prof. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 1, 2017, at the `Microsoft Vision Days - Intelligent Cloud' event of Microsoft Switzerland in Wallisellen, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 6)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on April 23, 2015, at the 'ZüKoSt: Seminar on Applied Statistics' of the ETH Zurich in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data, Data Science, Machine Intelligence and Learning: Demystification, T...Prof. Dr. Diego Kuonen
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/.
Demystifying Big Data, Data Science and Statistics, along with Machine Intell...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 25, 2016, at the `Statistics at Nestlé in Switzerland' event of `Nestlé' in Vevey, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Glocalised Smart Statistics and Analytics of Things: Core Challenges and Key ...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 18, 2017 within Eurostat's special topic session `STS021: From Big Data to Smart Statistics' at the `61st ISI World Statistics Congress' (ISI2017) in Marrakech, Morocco.
A Swiss Statistician's 'Big Tent' Overview of Big Data and Data Science in Ph...Prof. Dr. Diego Kuonen
'President's Invited Speaker' keynote talk given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on August 22, 2016, at the '37th Annual Conference of the International Society for Clinical Biostatistics (ISCB)' in Birmingham, United Kingdom.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as pharmaceutical development. 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 Swiss statistician's 'big tent' overview of these terms in pharmaceutical development, illustrates the connection between data science and statistics - the terms surrounding the 'sexiest job of the 21st century' - and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data as the Fuel and Visual Analytics as the Engine Mount of the Digital ...Prof. Dr. Diego Kuonen
Public keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 7, 2017, in the context of the `CAS Data Visualization' of the `Bern University of the Arts' (HKB) in Berne, Switzerland.
See https://www.hkb.bfh.ch/de/weiterbildung/design/cas-data-visualization and http://bka.ch/worte/rubriken/worte/kein-datensalat
The Power of Data Insights - Big Data as the Fuel and Analytics as the Engine...Prof. Dr. Diego Kuonen
Keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 1, 2017, at the `Microsoft Vision Days - Intelligent Cloud' event of Microsoft Switzerland in Wallisellen, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 6)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on April 23, 2015, at the 'ZüKoSt: Seminar on Applied Statistics' of the ETH Zurich in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data, Data Science, Machine Intelligence and Learning: Demystification, T...Prof. Dr. Diego Kuonen
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/.
Demystifying Big Data, Data Science and Statistics, along with Machine Intell...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 25, 2016, at the `Statistics at Nestlé in Switzerland' event of `Nestlé' in Vevey, Switzerland.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Glocalised Smart Statistics and Analytics of Things: Core Challenges and Key ...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 18, 2017 within Eurostat's special topic session `STS021: From Big Data to Smart Statistics' at the `61st ISI World Statistics Congress' (ISI2017) in Marrakech, Morocco.
A Swiss Statistician's 'Big Tent' Overview of Big Data and Data Science in Ph...Prof. Dr. Diego Kuonen
'President's Invited Speaker' keynote talk given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on August 22, 2016, at the '37th Annual Conference of the International Society for Clinical Biostatistics (ISCB)' in Birmingham, United Kingdom.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as pharmaceutical development. 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 Swiss statistician's 'big tent' overview of these terms in pharmaceutical development, illustrates the connection between data science and statistics - the terms surrounding the 'sexiest job of the 21st century' - and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data as the Fuel and Visual Analytics as the Engine Mount of the Digital ...Prof. Dr. Diego Kuonen
Public keynote presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on July 7, 2017, in the context of the `CAS Data Visualization' of the `Bern University of the Arts' (HKB) in Berne, Switzerland.
See https://www.hkb.bfh.ch/de/weiterbildung/design/cas-data-visualization and http://bka.ch/worte/rubriken/worte/kein-datensalat
A Statistician's `Big Tent' View on Big Data and Data Science in Health Scien...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on April 18, 2016, at the `Nestlé Institute of Health Sciences' in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as health sciences. 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 Swiss statistician's 'big tent' view on these terms in health sciences, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 9)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 1, 2015, at the `Joint SCITAS and Statistics Seminar' of the EPFL in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Overview of Big Data, Data Science and Statistics, along with Digitalisation,...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 29, 2016, at the `University of Applied Sciences of Western Switzerland' (`Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud', HEIG-VD) in Yverdon-les-Bains, Switzerland.
Data as Fuel and Analytics as Engine of the Digital Transformation: Demystic...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on September 19, 2017, at the "ITU-Academia Partnership Meeting: Developing Skills for the Digital Era" in Budapest, Hungary.
See https://www.itu.int/en/ITU-D/Capacity-Building/Pages/events/academia2017.aspx
Big Data, Data-Driven Decision Making and Statistics Towards Data-Informed Po...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 20, 2015, at the Swiss Statistical Society's celebration of the `World Statistics Day 2015' in Olten, Switzerland.
Further information are available at https://worldstatisticsday.org/blog.html?c=CHE
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on May 13, 2014, at the `SMi Big Data in Pharma' conference in London, United Kingdom.
ABSTRACT
There is no question that big data have hit the business, government 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 statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Swiss Statistician's 'Big Tent' View on Big Data and Data Science (Version 10)Prof. Dr. Diego Kuonen
Keynote talk given by Dr. Diego Kuonen, CStat PStat CSci, on October 21, 2015, at the `Austrian Statistics Days 2015' in Vienna, Austria.
ABSTRACT
There is no question that big data have hit the business, government 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 Swiss statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
(Big) Data as the Fuel and Analytics as the Engine of the Digital TransformationProf. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on March 15, 2018, within the TIBCO webinar entitled "Demystifying the Hype: [Big] Data as Fuel and Analytics as the Engine of Digital Transformation"; see
https://www.tibco.com/events/demystifying-hype-big-data-fuel-and-analytics-engine-digital-transformation
---------------
ABSTRACT
---------------
The digital revolution is truly underway: terms such as big data, cloud, internet of things, internet of everything, the fourth industrial revolution, smart cities and data economy are no longer just concepts - they are changing our lives in new and exciting ways.
Digital Transformation started with a first wave of digitalisation, which resulted in the (big) data revolution. But now a second wave of digitalisation is needed to enable learning from (big) data and to generate increased value for both business and society as a whole.
This presentation discusses how analytics, the science of "learning from data" or of "making sense out of data", becomes the engine of a new wave of Digital Transformation, and illustrates that the biggest challenge therein is the veracity of the "data pedigree", i.e. the trustworthiness of the data, including the reliability, capability, validity, and related quality of the data.
This presentation looks at demystifying concepts and terms surrounding Digital Transformation and big data. Along with machine intelligence and learning, the connection between data science and statistics is illustrated, and trends, challenges, opportunities, and the related digital skills and principles needed to succeed at Digital Transformation are highlighted.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 5)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 21, 2014, at the 'Research Seminar in Statistics' of the University of Geneva in Geneva, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's View on Big Data and Data Science in Pharmaceutical Developm...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 13, 2014, at `F. Hoffmann-La Roche' in Basel, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as the pharmaceutical industry. 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 in pharmaceutical development, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's Introductory View on Big Data and Data Science (Version 7)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on May 12, 2015, at the 'SAS Forum Switzerland' in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 introductory view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 8)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on July 2, 2015, at 'Swiss Re' in Adliswil, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on August 26, 2014, at the `Zurich Machine Learning and Data Science' meetup in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 and illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government 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 and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
"Data as the Fuel and Analytics as the Engine of the Digital Transformation -...Prof. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on May 14, 2019, within the StatSoft webinar entitled "Data as the Fuel and Analytics as the Engine of the Digital Transformation - Demystication, Challenges, Opportunities and
Principles for Success"; see https://www.statsoft.de/en/dates/webinars/
Big Data, Data Science, Machine Intelligence and Learning: Demystification, C...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 28, 2019 at the "Swiss Cyber Security Days 2019" on February 27-28, 2019 in Fribourg, Switzerland; see https://swisscybersecuritydays.ch/.
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 26, 2018 at TIBCO's "Data Innovation Event" in Zurich, Switzerland; see https://www.tibco.com/events/tibco-data-innovation-event
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 7, 2018 at the "5th Swiss Conference on Data Science (SDS|2018)" in Berne, Switzerland; see https://sds2018.ch/.
Production Processes of Official Statistics & Data Innovation Processes Augme...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on May 15, 2018 at the conference "Big Data for European Statistics (BDES)" in Sofia, Bulgaria; see
https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/BDES_2018
A Statistician's `Big Tent' View on Big Data and Data Science in Health Scien...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on April 18, 2016, at the `Nestlé Institute of Health Sciences' in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as health sciences. 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 Swiss statistician's 'big tent' view on these terms in health sciences, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 9)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 1, 2015, at the `Joint SCITAS and Statistics Seminar' of the EPFL in Lausanne, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Overview of Big Data, Data Science and Statistics, along with Digitalisation,...Prof. Dr. Diego Kuonen
Presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on November 29, 2016, at the `University of Applied Sciences of Western Switzerland' (`Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud', HEIG-VD) in Yverdon-les-Bains, Switzerland.
Data as Fuel and Analytics as Engine of the Digital Transformation: Demystic...Prof. Dr. Diego Kuonen
Invited presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on September 19, 2017, at the "ITU-Academia Partnership Meeting: Developing Skills for the Digital Era" in Budapest, Hungary.
See https://www.itu.int/en/ITU-D/Capacity-Building/Pages/events/academia2017.aspx
Big Data, Data-Driven Decision Making and Statistics Towards Data-Informed Po...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 20, 2015, at the Swiss Statistical Society's celebration of the `World Statistics Day 2015' in Olten, Switzerland.
Further information are available at https://worldstatisticsday.org/blog.html?c=CHE
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on May 13, 2014, at the `SMi Big Data in Pharma' conference in London, United Kingdom.
ABSTRACT
There is no question that big data have hit the business, government 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 statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Swiss Statistician's 'Big Tent' View on Big Data and Data Science (Version 10)Prof. Dr. Diego Kuonen
Keynote talk given by Dr. Diego Kuonen, CStat PStat CSci, on October 21, 2015, at the `Austrian Statistics Days 2015' in Vienna, Austria.
ABSTRACT
There is no question that big data have hit the business, government 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 Swiss statistician's 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
(Big) Data as the Fuel and Analytics as the Engine of the Digital TransformationProf. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on March 15, 2018, within the TIBCO webinar entitled "Demystifying the Hype: [Big] Data as Fuel and Analytics as the Engine of Digital Transformation"; see
https://www.tibco.com/events/demystifying-hype-big-data-fuel-and-analytics-engine-digital-transformation
---------------
ABSTRACT
---------------
The digital revolution is truly underway: terms such as big data, cloud, internet of things, internet of everything, the fourth industrial revolution, smart cities and data economy are no longer just concepts - they are changing our lives in new and exciting ways.
Digital Transformation started with a first wave of digitalisation, which resulted in the (big) data revolution. But now a second wave of digitalisation is needed to enable learning from (big) data and to generate increased value for both business and society as a whole.
This presentation discusses how analytics, the science of "learning from data" or of "making sense out of data", becomes the engine of a new wave of Digital Transformation, and illustrates that the biggest challenge therein is the veracity of the "data pedigree", i.e. the trustworthiness of the data, including the reliability, capability, validity, and related quality of the data.
This presentation looks at demystifying concepts and terms surrounding Digital Transformation and big data. Along with machine intelligence and learning, the connection between data science and statistics is illustrated, and trends, challenges, opportunities, and the related digital skills and principles needed to succeed at Digital Transformation are highlighted.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 5)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 21, 2014, at the 'Research Seminar in Statistics' of the University of Geneva in Geneva, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's View on Big Data and Data Science in Pharmaceutical Developm...Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 13, 2014, at `F. Hoffmann-La Roche' in Basel, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government and scientific sectors, as well as the pharmaceutical industry. 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 in pharmaceutical development, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's Introductory View on Big Data and Data Science (Version 7)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on May 12, 2015, at the 'SAS Forum Switzerland' in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 introductory view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
A Statistician's 'Big Tent' View on Big Data and Data Science (Version 8)Prof. Dr. Diego Kuonen
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on July 2, 2015, at 'Swiss Re' in Adliswil, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 'big tent' view on these terms, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on August 26, 2014, at the `Zurich Machine Learning and Data Science' meetup in Zurich, Switzerland.
ABSTRACT
There is no question that big data have hit the business, government 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 and illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government 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 and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
"Data as the Fuel and Analytics as the Engine of the Digital Transformation -...Prof. Dr. Diego Kuonen
Webinar presentation given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on May 14, 2019, within the StatSoft webinar entitled "Data as the Fuel and Analytics as the Engine of the Digital Transformation - Demystication, Challenges, Opportunities and
Principles for Success"; see https://www.statsoft.de/en/dates/webinars/
Big Data, Data Science, Machine Intelligence and Learning: Demystification, C...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on February 28, 2019 at the "Swiss Cyber Security Days 2019" on February 27-28, 2019 in Fribourg, Switzerland; see https://swisscybersecuritydays.ch/.
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 26, 2018 at TIBCO's "Data Innovation Event" in Zurich, Switzerland; see https://www.tibco.com/events/tibco-data-innovation-event
Data as the Fuel and Analytics as the Engine of the Digital Transformation: D...Prof. Dr. Diego Kuonen
Keynote speech given by Prof. Dr. Diego Kuonen, CStat PStat CSci, on June 7, 2018 at the "5th Swiss Conference on Data Science (SDS|2018)" in Berne, Switzerland; see https://sds2018.ch/.
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https://webgate.ec.europa.eu/fpfis/mwikis/essnetbigdata/index.php/BDES_2018
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The term 'Data Scientist' arose fairly recently to express the specialised recruitment needs of certain well-known data-driven Silicon Valley firms. It signifies a mix of diverse and rare talents, mostly drawing from Computer Science (with emphasis on Big Data), Statistics and Machine Learning. In this talk, we will attempt to briefly survey the state-of-the-art both in terms of problems and solutions at the vanguard of Data Science. We will cover both novel developments, as well as centuries-old best practices, in an attempt to demonstrate that Data Science is indeed a Science, in the full sense of the word. This talk represents part of a seminar series that the speaker has given across the world, including Google (Mountainview), Cisco (San Jose) and Aviva Headquarters (London), and represents joint work with Professor David Hand (OBE).
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Big Data as the Fuel and Analytics as the Engine of the Digital Transformation
1. Big Data as the Fuel and
Analytics as the Engine of the
Digital Transformation
Prof. Dr. Diego Kuonen, CStat PStat CSci
Statoo Consulting, Berne, Switzerland
@DiegoKuonen + kuonen@statoo.com + www.statoo.info
‘Information Builders Think Tank Lunch’, Zurich, Switzerland — June 13, 2017
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).
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 (starting fall 2017).
Principal Scientific and Strategic Big Data Analytics Advisor for the Directorate and Board of
Management, Swiss Federal Statistical Office (FSO), Neuchˆatel, Switzerland (since 2016).
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
2
3.
4. About Statoo Consulting (www.statoo.info)
• Founded Statoo Consulting in 2001.
2017 − 2001 = 16 + .
• 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?
5.
6. Contents
Contents 6
1. Demystifying the ‘big data’ hype 8
2. Demystifying the two approaches of ‘learning from data’ 16
3. Questions ‘analytics’ tries to answer 23
4. Demystifying the ‘machine intelligence and learning’ hype 26
5. Conclusion and key principles for success 31
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
6
7. ‘Data is arguably the most important natural
resource of this century. ... Big data is big news just
about everywhere you go these days. Here in Texas,
everything is big, so we just call it data.’
Michael Dell, 2014
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
7
8. 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’.
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
8
9. • 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);
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9
10. – ‘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!
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10
12. • 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?
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12
13. ‘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’.
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
13
14. 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.
14
15. ‘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.
15
16. 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.
16
17. • 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.
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17
18. What distinguishes data science from statistics?
• Statistics traditionally is concerned with analysing primary (e.g. experimental or
‘made’ or ‘designed’) data that have been collected to explain and check the validity
of specific existing ideas (hypotheses), i.e. through operationalisation of theoretical
concepts.
Primary data analysis or top-down (explanatory and confirmatory) analysis.
‘Idea (hypothesis) evaluation or testing’ .
‘Deductive reasoning’ as ‘idea (theory) first’.
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18
19. • 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’.
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19
20. Do not forget the term ‘science’ in ‘data science’!
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20
21. • 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.
21
22. ‘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
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22
23. 3. Questions ‘analytics’ tries to answer
Source: Jean-Francois Puget, Chief Architect, IBM Analytics Solutions, September 21, 2015 (goo.gl/Vl4l2d).
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
23
24. Data-driven decision making : refers to the practice of basing decisions on
‘analytics’ (i.e. ‘learning from data’), rather than purely on gut feeling and intuition:
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24
26. 4. 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, language translation
and visual 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.
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26
27. ‘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).
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27
28. • 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
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28
30. ‘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.
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30
31. 5. Conclusion and key principles for success
• Decision making that was once based on hunches and intuition should be driven by
data ( data-driven decision making, i.e. muting the HIPPOs).
• Despite an awful lot of marketing hype, big data are here to stay — as well as the
‘Internet of Things’ (IoT; a term coined in 1999!) — and will impact every single
domain!
• 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!
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
31
32. 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.
32
33. ‘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).
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
33
34. My key principles for analytics’ success
• Do not neglect the following four principles that ensure successful outcomes:
– use of sequential approaches to problem solving and improvement, as studies
are rarely completed with a single data set but typically require the sequential
analysis of several data sets over time;
– having a strategy for the project and for the conduct of the data analysis;
including thought about the ‘business’ objectives ( ‘strategic thinking’ );
– carefully considering data quality and assessing the ‘data pedigree’ before,
during and after the data analysis; and
– applying sound subject matter knowledge (‘domain knowledge’ or ‘business
knowledge’, i.e. knowing the ‘business’ context, process and problem to which
analytics will be applied), which should be used to help define the problem, to
assess the data pedigree, to guide data analysis and to interpret the results.
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34
35. ‘All improvement takes place project by project and
in no other way.’
Joseph M. Juran, 1989
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35
36. ‘By ‘augmenting human intellect’ we mean increasing
the capability of a man to approach a complex
problem situation, to gain comprehension to suit his
particular needs, and to derive solutions to problems.’
Douglas C. Engelbart, 1962
Source: Engelbart, D. C. (1962). ‘Augmenting human intellect: a conceptual framework’ (1962paper.org).
Copyright c 2001–2017, Statoo Consulting, Switzerland. All rights reserved.
36
37. ‘We can not solve problems by using the same kind
of thinking we used when we created them.’
Albert Einstein
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37
38. ‘The only person who likes change is a wet baby.’
Mark Twain
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38
39. ‘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.’
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39
40. 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
41. Copyright c 2001–2017 by Statoo Consulting, Switzerland. All rights reserved.
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mechanical, photocopying, recording, scanning or otherwise), without the prior
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