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Session 1 – Communication of Statistics

Lessons Learned:

  • Complicated and fancy designs of communication material may not improve things (3)
  • Communication is an essential part of survey design
  • We should have a marketing strategy on the collection side
  • Better communication makes data collection more efficient and effective (higher response)
  • Automated/interactive FAQ for online surveys improves response rate
  • Be pro-active and stay in contact with respondents
  • Ask respondents if they “think what you think they think”
  • Interaction with customers and good and active user support is important
  • Use mail sort exercise for testing design of mailing packages
  • Business registers could be good communication tools
  • Importance of thorough testing of data collection instruments and dissemination tools
  • Use in-house data for targeted marketing

Future work:

  • Develop tools to measure the (cost) effectiveness of our interventions made to improve response (2)
  • Best practices in how we communicate and reach respondents
  • - Best practices for communication with respondents (especially for initial contact)
  • - Research/testing different ways of reaching different respondents
  • - Use of social media to reach-out to respondents
  • - Find out more about personalized customer portal
  • - What incentives improve response rates
  • - Direct marketing to respondents who did not finish survey (‘re-marketing’)
  • How to transition from traditional surveys to web based approaches (best practices)
  • More examples of linking online collection with dissemination sites
  • Thorough testing standard tools and business processes for data collection
  • With change from fixed to mobile phones, do we need to rethink sample frameworks (no geographic location, household vs individual)?
  • Guidelines on how to create e-mail databases and safeguard privacy

Session 2 – New Tools and Methods

Lessons Learned:

  • By focusing on a particular type of Big Data, we can be very practical about what needs to be solved
  • Use open source algorithms, specialist companies and not internal IT, standard query system for mobile phone data, work together with providers
  • Big Data provides big opportunities but need mixed sources to validate algorithms and results (not the solution to everything)
  • Big Data doesn’t solve all problems and might raise new ones
  • New technologies and data sources need new skills (and new staff?)
  • Outsourcing or partnerships can fill gaps in in-house knowledge
  • Paperless is the way to go
  • Greater use of tablets is worth to follow-up on
  • Use open source and share algorithms
  • Harmonization and standardization of tools, taxonomies and software needed
  • Phone data and other electronic sources concern have a technical component and a legal/regulatory component

Future work

  • Work on international level MOU with mobile phone companies and partner with them using political support (data important for the public good)
  • Evaluate whether to make contracts with private sector or make them provide access by law
  • Explore how to partner with data suppliers and get aggregate instead of micro-data
  • Cost benefit of using Big Data sources
  • How to overcome public concerns about privacy and Big Data from NSOs
  • How to make data from new sources quality ensured and how to build partnerships
  • How to team up IT staff and statisticians to improve and enable innovation
  • More work on data integration of new sources
  • Organize sharing of algorithms and software demos as workshop session

Session 3 – Data Source Management and Risk Monitoring

Lessons Learned

  • Advantages and disadvantages of the different models (subject vs process) are still not clear
  • As we are still at an early stage with Big Data, collaboration is important
  • Planning is important but monitoring is key
  • Follow the whole production system to see where most gains can be made
  • Data collection needs a good management process and system
  • Potential uses of new data sources, including web-based interviewing
  • CAWI needs a different approach than traditional collection methods
  • A Big Data framework already exist (Eurostat)
  • In order to harness new data sources statistical offices have to be agile to incorporate new IT and software
  • Reduce burden by using administrative and tax reporting through common taxonomy to get statistics

Future Work

  • Continue cooperation and coordination with all actors on Big Data activities and keep sharing experiences
  • Good practices in systematic approaches to risk management (not just for Big Data)
  • How to tailor traditional methods to CAWI and what is the optimal mix
  • How to incorporate virtual interviewing in web surveys
  • Share experiences and ideas on standardized business reporting
  • Have standardized ledgers and business reporting based on existing taxonomies.
  • How to modify administrative sources to be more useful for statistics and reduce reporting burden
  • What are the advantages and disadvantages of subject/silo versus process oriented approaches and how do you manage change from one to another or combine them into a matrix approach

 

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