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Towards efficient data collection at Statistics Sweden

This document outlines the strategies and processes implemented at Statistics Sweden to optimize data collection across various sectors, including households and enterprises. Led by Johan Erikson, the focus is on establishing common routines, utilizing innovative tools like web data collection and interview collection systems, and addressing the challenges of declining response rates and resource planning. The success of centralized data collection is evaluated, emphasizing the importance of collaboration, technical expertise, and learning from experiences to improve future data integration and sharing among government agencies.

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Towards efficient data collection at Statistics Sweden

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  1. Towardsefficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

  2. Data collection today • Twomain data collectiondepartments • Individuals and households • Interviewsurveys • Questionnairesurveys • Enterprises and public sector • Enterprises and Enterprise relations – Örebro • Enterprises – Stockholm • Public sector • Coordination and Largeenterprise management • Process owner at process department • Register data collection at subjectmatterdepartments

  3. Roles in data collection • Process owner • Establishcommonroutines • Build and maintaincommontools • Process users • Runcollection on a daily basis • Demands on commonroutines and tools

  4. Commontools in use • Web data collectiontool • Interviewcollectiontool • Hand-heldcomputers for CPI collection • Scanning system • ”Funnel” for administrative data • Triton – commonproduction system

  5. Effects of centralised data collection • Expert functions • Resourcepooling • Learning by experience • Implementation of new tools and routines • Addressingnon-survey-specificissues • Internal tension, ”us and them” • Resourceplanning in largeunits

  6. Ongoinginitiatives • Triton project – expectedgains • Planning and metadata haveeffect on IT tools • Built-inqualityassuranceactivities • Easier to pool resources and work on manysurveys • Moreefficientinterview data collection • Contact strategies • Common set of cases • Data warehousing • EDI initiatives (XBRL)

  7. Futurechallenges • Decliningresponse rates • Difficult to reach respondents • Pressure to reduceburden • Combiningsurvey data and administrative data • Data sharingbetweengovernmentagencies • More on EDI • New technologicaladvances • Social media? • Optimisingresourceplanning with new contactstrategies

  8. Reflections / conclusions • Centralisation of data collection has beensuccessful so far • Internal tension tends to decline over time • Some of the futurechallenges that face us are meteasier with a centralised data collection • Demand for expertise on collection-specificissues – collection is a general knowledge, not onlysurvey-specific, e.g. contactstrategies • Demand for technicalexpertise • Demand for central roles (persons) to negotiate with data providers and othergovernmentagencies

  9. Reflections / conclusions (2) • Some of the futurechallengesprobablyrequireevenmore of centralisation, and new thinking • Manyissues are the same for householdsurveys and business surveys – combinedexpertisenecessary (mixed mode, decliningresponse rates) • Technicalchallengessuch as direct data feeds • Combining administrative data and survey data • Administrative data are used for both registers and surveys • Are todayssurveys and their limits the mosteffectiveway to collect data? • A single ”data capture” departmentcould be an effectiveway to deal with data sharingissues

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