What is Data Literacy?
In short: understanding of and dealing with data.
Big Data
Large amounts of data are being generated by recording user behaviour in social media and apps, on search engines, shopping platforms or rating portals. Further data repositories result from research data, government data (Open Data), geo data and in the constantly growing databases.
Data literacy is the ability to transform data into information and information back into data.
Electronic Data Processing EDP
Initially there was EDP – electronic data processing. As such it used to be about electronic data. At that time one had to be able to operate data processing systems (practically any kind of software with input/output functions and the appropriate hardware) that could process hundreds of kilobytes (on floppy disks) of data. The output data were represented as texts and numbers which could be converted into information by human intelligence. It was only with larger storage capacities that images and sounds could be processed as well.
Data Literacy
Today we are talking about data volumes in the terabyte range. The data acquisition as well as the representation includes all kinds of visual and acoustic data. However, more and more data is generated by sensors and recording devices (such as video cameras and smartphones).
Information experts are no longer adequate to analyse the data deluge. Rather, the recognition of patterns (regularities), their evaluation and selection are supported by algorithms called “artificial intelligence”. The output options extend to machine-generated texts and even machine-generated spoken language. Thus, EDP sounds old-fashioned.
Data Analysts and Data Journalists
The rising stars in the field are data analysts and data journalists. They find information in big data that nobody actually expected, but which may promise a strategic competitive advantage.
From Data to Information
Whether “EDP skills” or “data literacy”, it is still about data processing. Even “artificial intelligence” cannot exploit the data unless a human intelligence imposes its own ends on it. But this human intelligence is not and never has been primarily interested in the data as such, its storage and backup, but rather in the information encoded in the data.
What is new, however, is the storage of data even before their information content becomes obvious. Their significance is evaluated later on (if ever). Thus, it is difficult to predict who can extract what from these data. This creates their potential value, but also their potential for abuse. The latter is why the algorithms used for data processing (“artificial intelligence”) are often regarded with suspicion. They are not transparent and their decision paths are no longer traceable for most of us.
Just as data are placed below information by North on his “Steps to Knowledge”, data literacy should be considered as part of information literacy, especially where the search for information is concerned.
Data literacy without information literacy opens the door to data abuse.