Data literacy is not merely, as Gartner puts it, "the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value." Data literacy is part of information literacy where one is able to access, interpret, assess, evaluate, manage, handle, and ethically use data. Data literacy comprise critically consuming data, statistics, and visualisations. Individuals must also understand the context of the data they encounter in mass media, social media, scholarly outputs, and others of the ilk both on and off the internet. Recognising that there is inherent bias in data is also a crucial part of data literacy. Data points can be distributed in different ways and this in turn influences decision-making. Individuals must also be aware that devices such as computers, handphones, sensors, and exercise trackers consume and produce data.
In assessing and evaluating whether the data you have on hand is usable and can be trusted, understanding the origins, assumptions, and methods involved in the creation of the data can help. Here are some guiding questions to ask before making any decisions with data:
Are there potential sources of bias in this data?
What is the method of data collection within the research study?
What is the strongest argument for using this data?
What is the strongest argument against using this data?
This library guide was inspired by the Critical Digital Literacy library guide from Pratt Institute's School of Information.
