In this section, I would introduce in which context is this design situated, and what potential of this design I see to solve problems.
There are lots of data from government have been released, but still few common standards agreed on how to assess its usabilities. For example, one of the most basic and oldest types of information collected by regimes is statistics. The mastery of various types statistical data reflects the degree of rationality and modernization of governance of a regime. In general, most of the common measurement methods focus merely on the quality of statistical data, such as the indicators (e.g. Relevance of Concept, Accuracy of Estimates, Timeliness and Punctuality in Disseminating Results, etc) used by Eurostat and by the OECD.
In contrast, the usability for transforming data into information has been long neglected. It could also be seen as decentralisation of the power. How "data" can be used as valuable "information" is equally important as the the accessibility of data (Kenett & Shmueli, 2016). A good quality of official statistical data doesn’t guarantee qualified use/transformation of data. The current measurement for assessment may be just beneficial for the professionals who are familiar with how to utilise raw data for further analysis.
As we all know the power of design in demonstrating the data, design could play a more active role — not only visualise the data, but provide tools for transforming data to information. I see a potential of design intervention for empowering transformation.
Design could lower the thresholds for citizens to transform the data into forms comprehensive to them, and thus decentralise the power of data. In the age that data is power, the citizens should be able access, understand, and use the original data on their own, instead of merely being fed with post-truth within their social bubbles.
The back-stage structures of all the official statistical systems are essentially somehow decentralised in order to collect, process, and publish data, therefore, they inevitably give its users an perception of silos. This perception, which is especially common in services provided by great enterprises and other government sectors, is constructed through every gaps happening within the service journey.
Design has a great potential to dissolve the perception by strengthening the collaboration and providing a more integrated experience on service journey.
The redesign of the official statistical database interface, which was built with IBM's Carbon Design System (released with apache 2.0 open-source license), is an attempt to solve the problem mentioned.
This design project was the term assignment of a 3-week intense course at Aalto University. Given the limitation of time, the research is mainly based on my pervious using experience and desktop research. While working on this, I had several times of tutoring with the teachers of the Information Design course, Juuso Koponen and Jonatan Hildén. They gave me many enlightening insights from their perspectives as professional users of Statfin.
Another source of information was a guest lecture given during the Information Design course by Anne-Mari Kiviniemi, the spokesperson and communication manager Terveyden ja hyvinvoinnin laitos (THL). She shared her experience of facilitating abilities in data visualisation in her department. They made a huge effort on learning how to use online infographic makers and Microsoft Excel to make their statistical data looks more comprehensive and attractive. It made me realize that not only the citizens need to transform data, but also many government employees use the official statistical data frequently to make reports and slides for communication.
The main research finding is threefold: Firstly, I establish an overall understanding of the national statistical system of Finland, therefore, some organisational factors lay behind the current service gaps could be identify; secondly, I studied the common criteria to assess the quality of the official statistics, and thus have a glimpse on how statistical authorities evaluate the performance of themselves; thirdly, I acquired a basic understanding of the stakeholders in this field and established the benchmarks for this design project.
The finding is shown in the following section, Redesign Statfin.
Eurostat. (2003). Assessment of quality in statistics — Definition of Quality in Statistics. Kenett, R. S., & Shmueli, G. (2016). From quality to information quality in official statistics. Journal of Official Statistics, 32(4), 867–885. OECD. (2003). Quality Framework for OECD Statistics.