ROMA 23 GIUGNO 2016 MODERNISATION LAB - FOCUSSING ON MODERNISATION STRATEGIES IN EUROPE: SOME NSIs’ EXPERIENCES Giulio Barcaroli – Lessons learnt and conclusion.

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ROMA 23 GIUGNO 2016 MODERNISATION LAB - FOCUSSING ON MODERNISATION STRATEGIES IN EUROPE: SOME NSIs’ EXPERIENCES Giulio Barcaroli – Lessons learnt and conclusion remarks Modernisation Lab – Focussing on Modernisation Strategies in Europe: some NSIs’ experiences Lessons learnt and conclusion remarks Giulio Barcaroli| Italian National Institute of Statistics - Istat

ROMA 23 GIUGNO 2016 MODERNISATION LAB - FOCUSSING ON MODERNISATION STRATEGIES IN EUROPE: SOME NSIs’ EXPERIENCES Giulio Barcaroli – Lessons learnt and conclusion remarks  Standardisation of internal production processes: no more pipelines with individual solutions, but an integrated and industrialised system  Paradigm shift in data collection: use those already available before asking for costly and burdensome new ones  Paradigm shift in competencies: from a methodological expertise able to handle the classic survey production process, to the new capabilities required to build a registers based production process  Paradigm shift in methodology: from the traditional (design based / model assisted) estimates produced by sampling surveys, use of model based estimation systems applicable in a multi-sources environment 2 Different (but complementary) concepts in this session presentations What is “modernisation” in official statistics?

ROMA 23 GIUGNO 2016 MODERNISATION LAB - FOCUSSING ON MODERNISATION STRATEGIES IN EUROPE: SOME NSIs’ EXPERIENCES Giulio Barcaroli – Lessons learnt and conclusion remarks  Official statistics production processes are often characterised by non optimality in terms of both effectiveness and efficiency  Standardisation can greatly help to increase optimality, whenever optimal methods and instruments are adopted as standards, and processes are designed following a well-defined Business Architecture  Yes, BUT: standardisation decreases the degrees of freedom in a system  A certain degree of freedom should be granted to explore new solutions  National Statistical Institutes are not only producers, but also researchers: creativity is vital 3 One major issue: standardise, yes, but to what extent? Standardisation: yes, but …

ROMA 23 GIUGNO 2016 MODERNISATION LAB - FOCUSSING ON MODERNISATION STRATEGIES IN EUROPE: SOME NSIs’ EXPERIENCES Giulio Barcaroli – Lessons learnt and conclusion remarks  Surveys are characterised by high costs and decreasing response rates  Increasing availability of administrative data and new data sources (Big Data), less costly and not burdensome  Yes, BUT: this sources are characterised by the fact that they are not under the control of statisticians  Problems: representativeness, different definitions and classifications, always-changing mechanisms of generation 4 They seem to refer to a common view: the classic (sampling) survey is out of date. In some cases, to be abandoned. Paradigm shifts: no more surveys?

ROMA 23 GIUGNO 2016 MODERNISATION LAB - FOCUSSING ON MODERNISATION STRATEGIES IN EUROPE: SOME NSIs’ EXPERIENCES Giulio Barcaroli – Lessons learnt and conclusion remarks  We need a bridge between what is under control and well known (the survey process and the survey data) and what is neither one nor the other  Survey data to build models…  … to be applied to data from other sources…  … to produce more statistical information, less costly, more timely and possibly of higher quality 5 The way to go: a combination of sources and processes, in which the (sampling) survey plays a fundamental role A combination of survey data and new sources

ROMA 23 GIUGNO 2016 MODERNISATION LAB - FOCUSSING ON MODERNISATION STRATEGIES IN EUROPE: SOME NSIs’ EXPERIENCES Giulio Barcaroli – Lessons learnt and conclusion remarks  In any case, survey process and survey data will be only a component of the whole system  New sources and different processes ask for new competencies  Not only sampling methods and statistical modelling, but also: machine learning (statistical learning), text mining, network analysis, … 6 Not only statisticians, not only IT experts: data scientists Data science? Yes, without “but”