1. Official statistics and mobile network operators A business model for partnerships
Marc Debusschere, Statistics Belgium
Albrecht Wirthmann, Eurostat
Freddy De Meersman, Proximus
Brussels, NTTS 2017, 15 March 2017
Statbel.fgov.be

2. Overview
1. Context: the challenge of big data 2. Exploiting mobile phone data: the issues 3. How this might work in practice: a project 4. Conclusion: a business model?

3. Context: the challenge of big data
Data explosion: big data
Immense increase in volume, velocity, variety (complexity)
‘Digital footprint’ of persons and ‘things’
Specifically: mobile phone data
‘Exhaust’: by-product of operating mobile networks
Investment needed to ‘create’ data
‘Owned’ by mobile network operators: private & profit-oriented!
Challenge: from data to information!
Official statistics: the ‘third data revolution’
surveys => administrative data => big data
Network operators: for network optimisation & high-value commercialisation

4. Exploiting mobile phone data: the issues
What’s in it for statistics?
Faster, more detailed, cheaper official statistics
Entry to phenomena inaccessible until now
At a cost, however: unknown territory and new limitations!
What’s in it for mobile network operators?
Commercial potential – but high investment requiring high return
Network operators lack expertise (but often are unaware of this …)
Network operators lack potentially valuable extra data (ditto …)
Challenge: joining assets for mutual benefit
Statistics: domain and methodological expertise and (geocoded) datasets
Network operators: mobile phone data and technical expertise

5. How this might work in practice: a project
Statistics Belgium, Proximus, Eurostat, JRC
Objective
Jointly explore mobile phone data, focus on modest but concrete and quick results, with the ultimate aim of developing statistical and commercial use cases combining mobile phone and statistical data
Timing
Start December 2015, ongoing
First results foreseen and delivered end of April 2016
Eight papers published in 2016
Second stage kicked off December 2016: new datasets, developing statistical and commercial use cases

6. A collaboration project (continued)
Step by step approach
Focus first on actual present population
Next resident population (via ‘usual place of residence’), commuting, labour mobility, labour migration (adding ‘work place’), tourism, migration, time use, … (adding ‘usual environment’), …
Finally: ‘real’ statistical and commercial use cases
Innovative
Using network signals rather than CDRs: observations x 10 !
Combining mobile phone data and statistical datasets
No data handling or privacy issues (yet)
Aggregates resulting from queries in Proximus datawarehouse
Coupled via geocoding

7. Some results
Belgium: population density per km² based on mobile phone data (left) and 2011 Census (right).

8. Some results (continued)
Cells identified as ‘work’, ‘residential’ or ‘commuting’ on a weekday, with mapping.

9. Some results (continued)
‘Work’, ‘residential’ and ‘commuting’ cells in the region Brussels-Leuven

10. Publications so far
F. De Meersman, G. Seynaeve, M. Debusschere, P. Lusyne, P. Dewitte, Y. Baeyens, A. Wirthmann, C. Demunter, F. Reis, H.I. Reuter (2016): Assessing the Quality of Mobile Phone Data as a Source of Statistics (mirror site), Q2016 Conference paper, June 2016 (pdf download)
M. Debusschere, P. Lusyne, P. Dewitte, Y. Baeyens, F. De Meersman, G. Seynaeve, A. Wirthmann, C. Demunter, F. Reis, H.I. Reuter (2016a): Big data en statistiek: om het kwartier een volkstelling … (mirror site), Trefpunt Economie (PDF download) (in Dutch)
M. Debusschere, P. Lusyne, P. Dewitte, Y. Baeyens, F. De Meersman, G. Seynaeve, A. Wirthmann, C. Demunter, F. Reis, H.I. Reuter (2016b): Big data et statistiques : un recensement tous les quarts d'heure… (mirror site), Carrefour de l'Economie (PDF download) (in French)
M. Debusschere, F. De Meersman (2016): Statistiek en big data; een samenwerkingsmodel, STAtOR, 17/3, december 2016 (in Dutch) (article not yet freely available on the internet, see STAtOR issues)
M. Debusschere, J. Sonck, M. Skaliotis (2016): Official statistics and mobile network operator partner up in Belgium, The OECD Statistics Newsletter, Issue No 65, November 2016
F. Reis, A. Wirthmann, P. Lusyne, Y. Baeyens, F. De Meersman, M. Debusschere, H. Reuter, G. Seynaeve (2016), New opportunities for statistics on population and mobility from the use of mobile phone data, paper presented at the IAOS 2016 Conference, Abu Dhabi (UAE), Dec (article not available online)
G. Seynaeve, C. Demunter, F. De Meersman, Y. Baeyens, M. Debusschere, P. Dewitte, P. Lusyne, F. Reis, H.I. Reuter, A. Wirthmann (2016), When mobile network operators and statistical offices meet - integrating mobile positioning data into the production process of tourism statistics, paper presented at 14th Global Forum on Tourism Statistics (Venice, Italy, Nov. 2016) (PDF download)
A. Wirthmann, F. Reis, M. Skaliotis, F. De Meersman, G. Seynaeve, M. Debusschere, H. Reuter (2016), Big Data as a Source for Official Statistics: Assessment of Using Mobile Phone Data for Population, paper presented at Data for Policy 2016 Conference, Cambridge, Sept (article not available on the internet)

11. Emerging business model cooperation network operators & official statistics
Mobile network operator
Owns data, has big data infrastructure, technical expertise
Needs exploitation for network optimisation and commercialising
Lacks expertise to turn data into information and additional data
Statistical institute
Has geocoded datasets, statistical & domain expertise
Wants statistics faster, cheaper, less burdensome, more detailed
Lacks (access to) data, metadata, knowledge, infrastructure
Complementary contributions and needs, non- competing goals
 Mutually advantageous collaboration!
The statistical system of Belgium is a very complex one. It is the result of decisions related to the evolution of the Belgian federal system and to the economic governance of the state.
A long history of political decisions made the system evolve from a monolithic block to a system which is decentralised functionally as well as regionally.
Functional decentralisation was implemented in 1994, when the federal government set up the National Accounts Institute.
Regional decentralisation is the result of a far-reaching reform of the State in the last decades, when the majority of the competences of the State were attributed to the regions. One aspect of it has to be stressed: In Belgium, federal law does not overrule regional law. Keep this in mind when we come to the legal context of the new statistical system. Up to this year however, official statistics were not regionalised. They remained a prerogative of the federal level. Notwithstanding, the regions have recently set up their own statistical institutions.

12. But what if this won’t work? The alternatives
Legal compulsion at EU or national level
No legal framework yet – but will be created (maybe sooner than expected)
Huge investment, difficult to impose (but all operators will invest eventually)
External integrator of mobile phone data
Not subject to statistical legislation and code of practice
Competitor for official statistics when directly serving users
Cannot integrate other statistical datasets for higher value
Buying mobile phone data
Against principle of statistical inputs as public good
… and no money!
But (modest) processing fee might be worth considering

13. Bonus: lessons learned: do’s and don’ts for official statistics …
Find the window of opportunity
Operator who has invested in data but not obsessed with selling only
Talk to the right people (if you have a choice …)
Business development/innovation rather than research, marketing, legal
Invest in geocoded datasets and data science
Convince operators of your value to them
Guarantee absolute confidentiality and build trust
Be attentive to legal issues, especially privacy
Find (international) partners
Start low-threshold quick-result exploration project

14. Questions? Comments?