This chapter covers how  data visualisations help overcome data literacy challenges. 

Being able to understand and work with data is called data literacy, and it is a critical factor for driving trust in democratic and experimental decision making. But, according to the Data Literacy project a whopping “76% of key decision-makers aren't confident in their ability to read, work with, analyze and argue with data”.  

Aware of this bottleneck, PoliVisu investigated the issue of data literacy in the public sector through its own survey and project experiences. From the gathered results it was clear that policy makers are ready to work with data; they show a growing appetite for it, also a good understanding of the advantages that data might bring to their daily activities. However, they also highlighted barriers that prevent them from making the most of their (unprocessed) data, chief among which is the lack of experienced in-house data analysts and data scientists. The absence of data-related roles means administrations are struggling to analyse and interpret data in meaningful ways.

Based on this feedback, PoliVisu understood that for public administrations to leverage experimental decision making, data literacy must be addressed on two levels:

• Creating policy visualisations: This level involves collecting and turning raw data into smart policy visualisations, thus providing a common view of the situation to support policy makers in their decision-making process

• Extracting intelligence from visualisations: This level involves ensuring anyone, no matter their background, can view and explore the data visualisation to better understand policy context

Video 3: Six tips for effective data management

Overcoming data challenges by enabling everyone to be a data analyst

Most visualisation platforms still require users to possess a degree of data science expertise to extract useful intelligence from data. PoliVisu turns this around by focusing on user experience. PoliVisu offers simple visualisation interfaces with understandable analytical tools to unleash data driven experimentation. The focus on user experience lowers the need for high levels of data literacy, and ensures complex policy is open for all stakeholders to easily explore and understand the situation that needs to be improved. Stakeholders can experiment with ideas, co-develop potential solutions, and contribute to policy formalisation. This levelling of the field means that policy makers, administrative workers, emergency services, entrepreneurs, businesses, and citizens can all participate in co-creation and consultation exercises as part of the policy making process.

What do you think the biggest barrier to data literacy is?

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