Knowledge and skills.  Data is a complex mystery to the average civil servant.

In my opinion, one major barrier to data literacy is the unwillingness of the policy makers to invest in data analytics abilities. These people especially in my country are older persons and as the saying goes, you cannot teach a old dog new tricks. This has become a major barrier to data literacy and has also directly hampered our chances of taking advantage of the enormous data available concerning almost everything.

Lack of motivation, ability or willingness

Additionally to what has been suggested before, I think a lack of understanding its usefulness might form a barrier.

Lack of concepts and information regarding the use of data and as a consequence of the policy, performers do not understand why this is needed and what benefits can be obtained from data analysis.

In my opinion, the costs of data collection and analysis are one of the main hurdles to an increased data literacy. Also software licences are often expensive for public authorities. Furthermore, data experts are often unwilling to work in the public sector, since their skills are much sought after and the pay is much higher in the private sector. The competition for talent is hence also to be seen as a barrier. On a personal level, I am intrigued by data analysis and love their visualisations, but I do not get my head around how to visualize them, 

It's sometimes difficult to understand the data without extra information. 
One visualization of data can lead to different conclusions, depending your own background information. 

Lack of understanding of concept, interest in subject, etc. It's relatively complex type of science.

A problem is nice visualisation, indeed. Politicians / decision maker sometimes just don't know what can be done with data. And giving then boring excels might not help. Therefore they must be triggered with nice visualisations (from other cities e.g.) that might give them the trigger to invest in people that can make the nice datareports / visualisations they need.

The mindset of people, I think is the biggest problem. If a big collection of data is shown and not visualised properly people give up easily. If they put some time in understanding the data they would become more literate. This could be realised by visualising simple data that anyone can understand.

1) Working with big datasets can be intimidating.

2) you need to have some basic knowledge of software to deal with data.

3) You need to have some basic knowledge of the source of the data, a lot of people can't distinguish between the nuances that different information sources can (or can't) deliver.

4) A lot of basic 'traps' in data interpretation that people who are 'data illiterate' fall into (confirmation bias, correlation/causation, etc.) are barriers that they even can't see until it is to late.

Hi,

I think the lack of data skills to process large datasets is a big barrier

Knowledge and skill

New data sets and data analytics capabilities advance in a rapid rate policy makers can't keep up with the innovation. 

I would say the main barrier is the lack of coding/programming skills that are often necessary to make datasets useful and visualise any results. 

In my opinion, the main barriers to data literacy are:

• Lack of competent software/programming skills. In addition, the fact that there are a lot of data mining/visualisation tools, makes it harder to enter the field, since so much options can discourage user.
• Basic knowledge of the source of the data. Depending on how it was collected or what information does it include, the analysis process can differ a lot, and this can discourage the user as well.

The skills needed to understand and interpret data. The complex technology makes it even more difficult for people to understand whats going on. 

1. lack of openness in terms of sharing data and tools 

2. collaborations at different levels of decision makers

There's no awareness of the importance of data literacy.

Given the background I came from, it is nearly impossible to get the real/trustworthy data in the first place. This is because we are not care much about documentation, data collection and preservation. No matter how skilled one is in data visualization and analysis, if the data itself is not trustworthy the policymakers cannot draw a conclusion from that data. Hence I think trustworthiness of raw data is main barrier in data literacy. 

I think a really big barrier to data literacy is public concern that not all data out there is from reputable sources, but not always being certain of how to verify it. If you don't feel you can trust it you have no desire to learn to understand it.

the data itself. we need to ensure the data is correct

I would say fear - in terms of lacking confidence and skills, especially if the data is going to be used by senior leaders, with important decisions or in a public proposal or argument

• Skills, knowledge, and confidence. 
• In some cases, a culture set by senior leadership on the value of data.
• Misconceptions that data will tell you the answer, rather than indicating what questions or conditions to be explored further.