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