Data Science and Official Statistics Myth or Reality

Data Science and Official Statistics Myth or Reality ?

Prof. Dr. Bertrand Loison Vice Director at Federal Statistical Office & Professor of Information Systems at University of Applied Sciences Western Switzerland

Swiss Conference on Data Science (SDS2020)

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Online, 26 June 2020

Digital Transformation Leads to… A New Reality

« Ensuring statistics accurately reflect a changing economy is one of the hardest challenges NSIs face.

The economy’s complexity and structure are becoming increasingly difficult to capture within the basic conceptual framework that underpins the national accounts. When the statistical framework was first devised, the economy was one in which most businesses were engaged in the production of reasonably homogenous goods in a single country.

The reality today is rather different, with many businesses operating across national borders and producing a range of heterogeneous goods and services that may be tailored to the tastes of individual consumers. »

Source: Charles Bean, Independent Review of UK Economic Statistics, (2016). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/507081/2904936_Bean_Review_Web_Accessible.pdf, pp. 116.

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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How Do We Measure This New Reality ?
1. The Need to adapt the way NSI measure our

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Source: NTTS 2015, (2015). https://www.researchgate.net/publication/273909550_Statistics_40

Can Big Data and Data Science Help Measure This New Reality ?

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Agenda

1. Demystifying the ‘big data’ hype 2. Demystifying the ‘Internet of things’ hype 3. Demystifying the two approaches of analytics 4. Demystifying ‘analytics of things’ 5. Process models for continuous improvement 6. Conclusion

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Data Collection (input) - Characteristics of Survey, Administrative and Big Data

Surveys

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Registers and

2007

administrative data 2

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New data sources

2017

(big data)

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Source: Rob Kitchin, The opportunities, challenges and risks of big data for official statistics, National University of Ireland, Maynooth, (2015). https://www.researchgate.net/publication/282421109_The_opportunities_challenges_and_risks_of_big_data_for_official_statistics, pp. 9.

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Big Data History

• 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’).

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Big Data Characteristics

• ‘Volume’, ‘Variety’ and ‘Velocity’ are the ‘essential’ characteristics of (big) data.
• ‘Veracity’ and ‘Value’ are the ‘qualification for use’ characteristics of (big) data.
• The 5th V of big data: ‘Value’ , i.e. the ‘usefulness of data’.

Source: B. Loison, D. Kuonen, FSO’s Data Innovation Strategy, Seminar on Data Science Campus activities in Kigali, UNGWG, (2019). https://www.slideshare.net/BertrandLoison/fsosdata-innovation-strategy-kigali-Rwanda, slide 12.

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Big Data (New Data Sources) – Tentative Taxonomy

Social Networks (human-sourced information)

Traditional Business Systems (process-mediated data)

Internet of Things (machine-generated data)

• Social Networks: Facebook, Twitter, Tumblr, etc.
• Blogs and comments • Personal documents • Pictures: Instagram, Flickr,
Picasa, etc. • Videos: YouTube, etc. • Internet searches • Mobile data content: text
messages • User-generated maps • E-Mail

• Data produced by Public Agencies • Medical records
• Data produced by Businesses • Commercial transactions • Banking/stock records • E-commerce • Credit cards

• Fixed sensors • Home automation • Weather/pollution sensors • Traffic sensors/webcam • Scientific sensors • Security/surveillance videos/images
• Mobile sensors (tracking) • Mobile phone location • Cars • Satellite images

Source: Big Data: Potential, Challenges, and Statistical Implication, IMF, (2017). https://www.imf.org/en/Publications/Staff-Discussion-Notes/Issues/2017/09/13/Big-Data-PotentialChallenges-and-Statistical-Implications-45106, pp. 9 - 10.

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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Potential Use of Big Data in Official Statistics

Source: Big Data: Potential, Challenges, and Statistical Implication, IMF, (2017). https://www.imf.org/en/Publications/Staff-Discussion-Notes/Issues/2017/09/13/Big-Data-PotentialChallenges-and-Statistical-Implications-45106, pp. 11.

7th Swiss Conference on Data Science – Data Science and Official Statistics Myth or Reality | Swiss Federal Statistical Office | Prof. Dr. Bertrand Loison | Online| 26 June 2020

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