1.1. Innovation in times of a pandemic
In 2020 the world was struck by a pandemic and peoples’ lives were dominated by health concerns, broken routines, reduced social contacts and crisis management. National statistical Institutes (NSIs) are there to provide statistical output and information to allow policy makers and government to develop policy guidelines and decide quickly, for example, on health interventions. As for many governmental organizations, at Statistics Netherlands this goal became very prominent in view of the covid-19 crisis. Working from home became the new normal and all activities were set up digitally; output based on surveys had to be delivered without the Computer Assisted Personal Interview (CAPI) observation and solely based on Computer Assisted Telephone Interview (CATI) and Computer Assisted Web Interview (CAWI); new output in the form of dashboards was requested to address the urgency for timely output. Mortality and morbidity statistics were delivered faster and more frequent because new procedures came in place and more recent statistical information (using register data and data sources such as public transport data) on mobility, the labour market, the economy and social consequences was produced to address the need for rapid information and trends in the year 2020. Data access to new data sources such as mobile phone data and transport data (OV chip card data) was prioritized in almost all EU countries in order to make new outputs possible on contact tracing or the spread of the virus. These developments, accompanied by intensified collaboration with other governmental and private institutes, brainstorming on the additional information that could be available in our in-house data sources, gave a new impetus for innovation not only in timeliness, but also in new and detailed statistical output.
1.2. Pre-pandemic statistical innovation
During the 1990’s and 2000’s, major innovations were implemented at Statistics Netherlands. Government registers replaced surveys as the primary input in many areas of official statistics. Data collection, editing, processing and dissemination were digitalized and automated to a great extent. Important drivers behind this process were the availability of massive, digital governmental data sources, new technological opportunities, political pressure to decrease the administrative burden on businesses – as well as budget cuts and retrenchments.
In recent years, Statistics Netherlands has put a lot of effort in further stimulating innovation, with new (big) data sources, Data Science and methods such as Machine Learning and Artificial Intelligence as main drivers. A lot was learned by developing innovation as a part of a pipeline process from Proof of Concept (PoC) and experimental (beta) statistics to the implementation of an official statistical product. However, the final step, implementing an experimental product into the official statistical process turned out to be challenging. The corona crisis brought an urgency for output which meant that a lot of the previously perceived barriers to implementation and publication were partially overcome. A lot of innovative output was developed and published within months – sometimes even weeks. The answers to what made this possible and which lessons can be drawn from this for future innovation and improving response to policy issues are the focus of this paper. Chapter 2 describes the innovation process that has been developed at Statistics Netherlands; chapter 3 provides some examples of innovative output at Statistics Netherlands following the start of the corona crisis. Chapters 4 and 5 discuss these developments by looking into what made speedy innovation possible and which lessons can be drawn for future innovation. Finally, chapter 6 draws some final conclusions.