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Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner [email protected]

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Page 1: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Towards efficient data collection at Statistics Sweden

Johan Erikson

Data collection, process owner

[email protected]

Page 2: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Data collection today

• Two main data collection departments• Individuals and households

• Interview surveys• Questionnaire surveys

• Enterprises and public sector• Enterprises and Enterprise relations – Örebro• Enterprises – Stockholm• Public sector• Coordination and Large enterprise management

• Process owner at process department• Register data collection at subject matter

departments

Page 3: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se
Page 4: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Roles in data collection

• Process owner• Establish common routines• Build and maintain common tools

• Process users• Run collection on a daily basis• Demands on common routines and tools

Page 5: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Common tools in use

• Web data collection tool• Interview collection tool• Hand-held computers for CPI collection• Scanning system• ”Funnel” for administrative data• Triton – common production system

Page 6: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Effects of centralised data collection

• Expert functions• Resource pooling• Learning by experience• Implementation of new tools and routines• Addressing non-survey-specific issues

• Internal tension, ”us and them”• Resource planning in large units

Page 7: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Ongoing initiatives

• Triton project – expected gains• Planning and metadata have effect on IT tools• Built-in quality assurance activities• Easier to pool resources and work on many surveys

• More efficient interview data collection• Contact strategies• Common set of cases

• Data warehousing• EDI initiatives (XBRL)

Page 8: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Future challenges

• Declining response rates• Difficult to reach respondents• Pressure to reduce burden• Combining survey data and administrative data• Data sharing between government agencies• More on EDI• New technological advances• Social media?• Optimising resource planning with new contact

strategies

Page 9: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Reflections / conclusions

• Centralisation of data collection has been successful so far

• Internal tension tends to decline over time• Some of the future challenges that face us are met

easier with a centralised data collection• Demand for expertise on collection-specific issues –

collection is a general knowledge, not only survey-specific, e.g. contact strategies

• Demand for technical expertise• Demand for central roles (persons) to negotiate with

data providers and other government agencies

Page 10: Towards efficient data collection at Statistics Sweden Johan Erikson Data collection, process owner johan.erikson@scb.se

Reflections / conclusions (2)

• Some of the future challenges probably require even more of centralisation, and new thinking• Many issues are the same for household surveys

and business surveys – combined expertise necessary (mixed mode, declining response rates)

• Technical challenges such as direct data feeds• Combining administrative data and survey data

• Administrative data are used for both registers and surveys• Are todays surveys and their limits the most effective way to

collect data?

• A single ”data capture” department could be an effective way to deal with data sharing issues