State of the art of CSPA and sharing tools projects Marco Silipo THE CONTRACTOR IS ACTING UNDER A FRAMEWORK CONTRACT CONCLUDED WITH THE COMMISSION

Introduction What’s happening with… …and how can CSPA help ? Statistical Organizations ? World of statistics ? Technologies ? …and how can CSPA help ?

Statistical Organizations – Trends Agency transformation Agility, Flexibility, Quality, Efficiency, Relevance New modes of engagement and delivery – products, services, partnerships, collaboration, leadership, education Provider Partnerships, Administrative and Big Data Digital platforms and shared services Cost optimization Advanced methods and tools Cross-agency, domain, levels of government, statistics to support new policy, service delivery initiatives Locally relevant statistics (small area) for local government service delivery Sustainable development goals and world collaboration Internal innovation programs Open data platforms

World of Statistics – Trends Data Science and Scientists Collaborative statistics Collaborative methods and tools Visualization and visual analytics Data prospecting, exploration, integration Open data and “open by default” Artificial Intelligence and Machine Learning “Oceans of data” Event-stream statistics, near real-time, now-casting, predictive analytics Public sector leverage of statistics Open training – e.g. MOOCS, universities, web-based “Citizen statisticians”

Technology – Trends Collaborative development, sharing of tools and methods, “experts in a box” starter kits Cloud-based statistics Cognitive Solutions, Artificial intelligence, Machine Learning API’s and microservices High-performance processing Data lakes Advanced search, discovery, use of knowledge, data COTS – OpenSource integrations Advanced geo-informatics Internet of Things – a world of sensors and event streams …and more Cloud

How will you meet the challenges and harness the opportunities this new world creates? As an Enterprise As a Designer As an Investor As a User As an Assembler

Wiesbaden Workshop 2017 Investor Designer “Mon – Tue- Wed” “Future” Assembler “You”

More and more data is available to public sector, science and business! Drivers: Cloud Computing Internet of Services Internet of Things Cyberphysical Systems sensor data web archives video streams Underlying Trends: Connectivity Collaboration Computer generated data simulation data audio streams RFID data

Skills Best-Practices The 5 Dimensions of Data Ownership Copyright/IPR Liability Insolvancy Privacy Data-driven Decision Making Risk Management Competitive Intelligence Digital Humanities Personalised services Industry 4.0 Legal Dimension Social Dimension Economic Dimension Technology Dimension Application Dimension User Behaviour Societal Impact Collaboration Data Ecosystem Infrastructures Skills Best-Practices Entrepreneurship Business Models Benchmarking Deployment Models Data Pricing Scalable Data Processing HPC Statistics/ML Multilinguality Visualization

Policy context: the European Digital Single Market 1.Better access for consumers and businesses to digital goods and services across Europe Unlocking the e-commerce potential Enable SMEs and start-ups to scale-up End to geoblocking of goods and services 2. Shaping the right environment for digital networks and services to flourish Establish role of online platforms Telecoms rules fit for the digital age Improved data protection rules Combat illegal content online 3. Building a European Digital Economy and society with growth potential Big data and Cloud Strategy Digitalising European Industry e-Government Action Plan Standardisation e-Skills Access to data

The data economy

Data-driven applications … home automation health lifecycle management water management information marketplaces market research traffic management energy management … will revolutionise decision making! … have great economic potential!

We need efficient statistical data ecosystem/s

Transformation of organisations Organisations evolving towards more service oriented approach. Focus on the service and the customer and not in the product itself Organisations developing shared services that can scale-up and be reused

Create Services for reuse Services should be created once and then reused many times How the statistical community can benefit from this approach?

CSPA as enabler for building shared-services (current situation) NSI 1 Collect Process Analyse Disseminate country 2 Output Different tools and process for the same requirements. COSTLY NSI 2 country 1 CSPA is a reference architecture for the statistical industry build by the international statistical community. It includes technology architecture and principles but does not prescribe technology environments

CSPA as enabler for building shared-services (targeted model) Collect Process Analyse Disseminate ? Sharing Services where economical “Plug and Play” architecture ? CSPA is a reference architecture for the statistical industry build by the international statistical community. It includes technology architecture and principles but does not prescribe technology environments

Building the future of European statistics Political: DSM (Digital Single Market) Statistical: European Statistical System (ESS) members are committed to sharing and reuse. Members and Eurostat participate in standards building and maintenance (CSPA, GSBPM…)

WHAT? Five priority areas better understand and address user needs be a global partner and leader for innovation build strategic alliances with public and private partners Users abide with the European Statistics Code of Practice apply fit-for-purpose tools to enhance quality assurance assess the usability and quality of source data Quality exploit potential of new data sources invest in new IT tools and methodology continue to improve existing data collection methods New data sources adopt a new dissemination and communication strategy create a data pool of European statistics optimise ESS portfolio of products and services promote European statistics as a brand Dissemination further intensify the collaborative partnership of the ESS further identify and implement standards for statistical production adopt enterprise architecture as a common reference framework advance in sharing IT services and infrastructure benefit from exchange of (micro)data, while fully respecting statistical confidentiality Production process

Shared SERVices Goal: Developing and sharing IT services in the ESS by implementing UNECE's steered Common Statistical Production Architecture (CSPA) Achievements: Infrastructure for centrally hosting services set up Service catalogue for ESS and CSPA community (soon with integrated lifecycle of services) Interaction and advice to Validation and ESBRs National Accounts started deploying SERV ESSnet building example services in NSIs and guidelines Phase 2 launched by the ESSC

First finalised ESS project: Validation Project successfully finalised: methodological handbook, validation language (VTL), architecture, service prototypes Now – following a roadmap for implementation at the national level Start with 7 pilot domains and 8 pilot countries Even on this code list, most codes can be agreed. Even on this code list, most codes can be agreed.