Modernization of European Official Statistics ESS Conclusion... · develop EMOS-labelled Master ... How to deal with auditing ... Napoli, Pisa, Durham, London, GENES . Grazie mille!!

Modernization of European Official Statistics ESS Rome, 31 March & 1 April 2014

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SUMMARY OF SESSIONS

Session 1A + 1B : Methodology, quality issues and accreditation of Big Data sets Session 2A + 2B : Learning & Development Session 3A + 3B : Strategy, programming and planning for Big Data in Statistical Programmes Session 4A + 4B : IT & Security

High Medium Low

• Representativeness and selectivity • Exploring new techniques • Use cases and Roles of Big Data

Source • Data processing • Timeliness • Privacy and volatility in access to Big

Data sources • Quality framework and certification

• Integration with traditional data sources

Methodology, quality issues and accreditation of Big Data sets. Session 1A: ISSUES

Methodology, quality issues and accreditation of Big Data sets. Session 1A: ACTIONS

• Representativeness and selectivity : Use other sources (survey-based and admin)

• Exploring new techniques : adapting existing ones establish standard practices in Big Data inference.

• Use cases and Roles of Big Data Source: mixed approach, Common Use cases • Integration with traditional data sources: Linking with privacy constraints

country dependant • Big Data Quality Framework: source –specific dimensions, subject-specific

dimension, look at admin data experience • Timeliness : IT infrastructure (computational time, but also real time

processing • Data Access: privacy can «block» the usage of some sources , beyond

regulation privacy by-design

High Medium Low

• Identify which scientific areas can contribute to methodological and quality issues on Big Data

• How to involve Academia • How to set up joint research and

application projects with Academia

• How to set stable relationships with Academia

Relationships with Academia Session 1B: ISSUES

Relationships with Academia Session 1 B: SOLUTIONS

• Identify which scientific areas can contribute to methodological and quality issues on Big Data: • Contact groups from several fields that are doing research projects on Big data

• How to involve Academia • We can provide also other kinds of data (survey-based, admin) and simplify

procedures for acamic access, Training, Public competitions involving them directly, Work on Real cases

• How to set stable relationships Relationships with Academia • Joint labs, Temporary recruitment of people (e.g., post-docs) supervised by

universities, NSOs sponsored Ph.Ds, Regular lectures by both sides, Research protocols and facilities to access NSOs’ data, Steering Committee on the Big Data topic

• how to set up joint research and application projects with Academia • Government to asks partnerships btw NSOs and Univ for funded research

programmes (both national and international), Eurostat funded projects (like ESSnets): jointly univ and NSOs

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• No Big Data-related IT skills in NSIs • Lack of soft skills necessary for team work: communication and Data Science team

mgt • Lack of statistical skills/legal barriers for data linking/matching • Need for concretely identifying the skills needed • Rapid technology evolution makes it difficult to plan for stable curricula • Lack of innovative/creative culture in NSIs, few change management skills • No turnover in some Nsis: no generational renovation

• Better salaries in the industry for data scientists • Legal barriers to use of Big Data may hamper creativity/innovative uses • Training not always alligned to business plan • Difficulty to connect with the “traditional academic mathematical statistician” and

machine learning experts • Low/no recognition of self-training and online training in NSIs • Demographics of NSI staff (ageing)

Learning & development Session 2A/B: ISSUES

Session 2A/B: SOLUTIONS

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• Proposals for Horizon 2020 (Marie Sklodowska-Curie)

• Adapt ESTP courses’ format to allow for blended (face-to-face + distance e.g. e-summer school) learning (next ESTP call?)

• Study the possibility of including Big Data in the “Statistical week” of Eurostat

• Identify staff who can start with selected online, free course on Big Data

• Develop statistical courses based on Big Data

• Give time to explore and get trained during work schedule

• Study the replication of ESTP2014 BIG DATA course at national levels

• Study to opening up of ESTP2014 BIG DATA course sessions by videoconferences/webinars

• Introductory (not detailed) courses on specific software (what can the software offer: R, Hadoop, etc.) and how to get the data (cf. UNECE’s sandbox/lab)

• ESS WG on Learning and Devt. to identify NSIs’ Big Data training needs

• Organize/attend Big Data meet-ups at the national level

• Obligation of participants to courses on Big Data to share knowledge (if possible, replicate) at NSS level

• Establish joint working groups of academics and NSIs on Big Data

• Identifying/mapping opportunities in Official Statistics and outside (by EMOS)

• Use ERASMUS PLUS to develop EMOS-labelled Master with Big Data profile

• Establish a Centre of Competence on Big Data

• Expert career tracks in NSIs

• Design instruments for the exchange of Big Data experts within NSS

• Offer traineeships in NSIs to graduate students on a variety of subjects (stats, math, IT but also marketing, art & design)

• Offer low-budget research positions in NSIs

Learning & development

Difficult Medium Easy

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• Share experiences within Europe and abroad also beyond NSIs - distill best practices

• Define a number of concrete areas to collaborate at EU level

• Communication strategy for BD towards society at large

• Within NSI define the strategical importance of BD

• Structured common

approach for the way of using BD for statistical production

• Establishment of Private Public Partnership for BD from private sector.

• Costs or free of charge? • Few providers of BD-

strong companies

Strategy, programming and planning for BD in Statistical Programmes

Session 3A/B: ISSUES

• Put pressure at EU political level for funding BD usage for OS explaining the value added of BD

• Combine BD with other sources (surveys)

• Risk assesment of the

use of BD (HR- stability of sources,...) • How can NSIs can be

useful for private providers

Session 3A/B: ACTIONS

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• Preparation of a Handbook on experiences and best practices

• Set up in each NSI of an

interdisciplinary “Big Data Structure”

• Proactively use cross-portal and other platforms

• Collaboration with academia and stakeholders

• Set up of a frame:

Guidelines, Rules, Principles, Glossary (for the different topics)

• Global collaboration (HLG)

• ESS collaboration for a common practical approach

• National collaboration for domestical issues

• Intentional Agreement at International level. Specific National agreements.

• Legal obligation to provide for free as BD are of public utility

• Communicate to society at large how BD can be useful for them (real-

time data) • Dialoque with private

data providers on the potenciality of the statistics based on their BD

Strategy, programming and planning for BD in Statistical Programmes

High Medium Low

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• How to overcome legal issues with ‘sandboxes’ for Big Data experimentation (in some jurisdictions)?

• Not all parties are free to share data with NSOs (e.g. telcos) in some jurisdictions

• How to manage access rights to (combining) unstructured Big Data sets?

• How to deal with commercially sensitive information from private parties?

• How to solve copyright/IPR issues regarding Big Data sets/analyses?

• Who has ownership over which combinations of Big Data sets?

• How to prevent misuse of Big Data sets?

• How to incentivize third parties to share data with NSO?

Data protection & security Session 4A: ISSUES

• What kind of infrastructure should we build?

• How to deal with auditing requirements in Big Data context?

• How to anonymize Big Data sets?

Session 4A: SOLUTIONS

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• Realise process for structuring/anonymizing Big Data that goes in/out

• Create sandbox to test usability of data sets before obtaining the full (huge) data set

• Differentiate between use of Big Data from external sources vs. internal/own sources

• Ask data owners to run statistical procedures (defined by NSO) on their data, and return the results to the NSO (create PPP)

• Use current policies as starting point

• Create a shared legal framework for the use of private data by NSOs in ESS

• Develop classification/categorisation of big data in order to solve integrity issues

• Create infrastructure for transferring data between parties

• Develop a methodology to assess sensitivity of a given Big Data source/application (w.r.t privacy, confidentiality, etc.)

• Look into opportunities for governmental (national) clouds

Difficult Medium Easy

Data protection & security

High Medium Low

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IT Session 4B: ISSUES

• Need info on quality and availability of Big Data sets/repositories

• Privacy/security features not an integral part of currently available tools

• How to securely distribute / collaborate on Big Data sets?

• How to encrypt exabytes of data (quickly)?

• What is the place of Big Data analysis tools in the NSI data lifecycle?

• Issues with usage of a private-sector cloud

• How to share tools and best practices? • Improving mid-size data set analysis

performance w/ Big Data processing • How to find existing sources of Big

Data inside NSI? (‘dark data’)

• Lack of user-friendly tools for analysis of Big Data

• Personnel is scarce • Integration of Big Data

architecture with existing architecture

• Need architecture, not (just) tools

• Validity of Big Data

• Statistical burden needs to be reduced

• Quality of statistics needs to be improved: trade-off between new methods and continuity/auditability

• Training/knowledge building • Sourcing of staff

• Changes to Hadoop for other purposes might influence usage by NSIs

• Too much focus on NoSQL databases while many Big Data sets are highly structured

• Too much focus on Facebook etc., should be on internet of things-type sources.

• Real-time data analysis • Scenario planning

Session 4B: SOLUTIONS

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Difficult Medium Easy

IT

• Develop methodologies for Big Data analysis, then determine software needs (not vice versa)?

• Invent an incentive for third parties to register potentially interesting Big Data sets

• Create multi-disciplinary teams that create Big Data proof-of-concepts: learn by doing

• Agree on a tool set with the NSIs to facilitate collaboration (<> existing software sharing policy!)

• Determine position of Big Data analysis in the statistical process (when do we use it?)

• Create architecture for metadata on Big Data

• Share knowledge of existing user-friendly tools for analysis

• Stick to traditional security methods whenever possible

• Explore tools. Give NoSQL databases a ‘test run’, may be suitable for particular kinds of data sets

• Generate ideas on possible analyses assuming unlimited access to data

• Use small sets of Big Data to test processes

• Define architecture for handling Big Data: don’t choose single tools, but focus on process / integration issues

• Offer traineeships to master students • More shared, new kinds of training

programmes (specifically on Big Data skills) e.g. on NoSQL

• Experiment with unconventional tools (e.g. Splunk)

• Participate in practical, international activities like the HLG sandbox

Some Statistics… 107 Participants

27 Countries

21 EU Member States + 1 Candidate country (Turkey) 5 Other countries (USA, México, Switzerland, Australia,

Canada)

Other

ECB, UNSD, UNECE, OECD, The World Bank Univ. Berkeley, Sapienza, Napoli, Pisa, Durham, London,

GENES

Grazie mille!!

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