6. Conclusion
The crisis has also highlighted continued challenges in terms of data access, data processing and IT infrastructures. NSIs still struggle with data access; a lot more output would have been possible if high value data sets were to be available from privately own data sources. Similarly, IT processes are not yet in place to automate statistical output using not only in-house data but also newly available data. A continuous investment in data acquisition and processing, the public image of the trustworthiness of an NSI, state of the art IT infrastructures and a more flexible statistical output programme are some of the challenges for the future. Finally, NSI should look for different approaches to collect data. A lot more sensors are currently on the market that can measure environmental or personal health information or economic activities more objectively than that surveys do. Citizens are through the General Data Protection Regulation (GDPR) the owners of their data which offers a lot of potential for data altruism (the sharing of own data that is collected by companies with public instances such as an NSI for public good).
When an NSI wants to produce a completely new statistic, the covid-crisis has taught us that the most successful approach is to use a new, readily available, data source that provides the necessary information and calibrate this with an already existing (traditional) statistic that measures the same or a very similar concept. The latter is needed as we found that creating a new statistic from scratch is nearly impossible in a limited time-frame. For new data, it just takes a lot of effort and time to understand the way the data is generated and the kind of errors it contains.