Data Literacy

"Data literacy" is formally called out as a new core competency as part of a clear commitment to the organization and leadership valuing "information as a strategic asset." Training programs (online and/or in-person; internal and/or external) are available and supported across all required levels of proficiency. (Gartner, 2019, Toolkit)

Wolff, et.al. 2018. "Data literacy is the ability to ask and answer real-world questions from large and small data sets through an inquiry process, with consideration of ethical use of data. It is based on core practical and creative skills, with the ability to extend knowledge of specialist data handling skills according to goals. These include the abilities to select, clean, analyse, visualise, critique and interpret data, as well as to communicate stories from data and to use data as part of a design process. "

Data literacy is the “ability to derive meaningful information from data” (Sperry 2018). "To summarize, a data literate individual would, at minimum, be able to understand information extracted from data and summarized into simple statistics, make further calculations using those statistics, and use the statistics to inform decisions. However, this definition is context-dependent, which will be illustrated below." (Bonikowska, Sanmartin and Frenette, Statistics Canada, 2019)

Also cited in the same report:

A separate literature review conducted by an interdisciplinary team of researchers at Dalhousie University in Canada (Ridsdale et al. 2015) led to a more concise definition: “Data literacy is the ability to collect, manage, evaluate, and apply data, in a critical manner” (p. 2). The researchers note that the definition should be allowed to change and evolve with input from stakeholders.

Working from the review by Ridsdale et al., Data to the People put forth an even more concise definition of data literacy, where data literacy is “our ability to read, write and comprehend data, just as literacy is our ability to read, write and comprehend our native language” (Data to the People 2018).

There is a sense in some of the literature that “data literate” should not be a label reserved for data scientists or specialists. Data literacy should be thought of as “the ability of non-specialists to make use of data” (Frank et al. 2016, abstract) and measure “a person’s ability to read, work with, analyze and argue with data” (Qlik 2018, p. 3), presumably using simple statistics such as means and percentages.

(End of citation from the same report)

We define data literacy as “the desire and ability to constructively engage in society through and about data.” Five observations emerge from this definition:
1. “Desire and ability” highlights technology as a magnifier of human intent and capacity. 2. “Ability” underlines literacy as a continuum, moving away from the dichotomy of literate and illiterate. 3. “Data” is understood broadly as “individual facts, statistics, or items of information.” 4. “Constructively engage in society” suggests an active purpose driving the desire and ability. 5. And “through or about data” offers the possibility for individuals to engage as data literate individuals without being able to conduct advanced analytics.
This definition—as well as the nature of data itself—encompasses elements and principles from each of these sub-kinds of literacy (such as media, statistical, scientific computational, information and digital literacies), moving away from medium-centred definitions of literacy towards a more encompassing one. (Bhargava, et.al., 2015)

"Data literacy is the ability to understand, find meaning, interpret, and communicate using data. Just as language literacy is the set of knowledge and skills to communicate and inform with words, data literacy encompasses a similar set of knowledge and skills to communicate and inform with data. " (Wells, 2022, Part 1)

"A common misconception equates data literacy with the ability to understand and create charts and graphs, limiting the concept to literacy as it applies to data visualization. Full data literacy encompasses all of the skills to understand, find meaning, interpret, and communicate with data. A fully data-literate individual has a working knowledge of where data comes from, how it is processed, how it is organized, how it is managed, and how it is used." (Wells, 2022, Part 2)

Development of the concept of 'data literacy'. All currently directly quoted/

According to Qin and D'Ignazio (2010), data literacy – though named by them science data literacy – is the ability of understanding, using and managing (science) data. (Via Koltay, 2015)

Required skills and abilities: Carlson et al. (2011) name both general and specific skills. The first group contains drawing correct conclusions from data, and recognizing when data is being used in misleading or inappropriate ways. In the second group we see the ability of reading graphs and charts appropriately.

Johnson (2012) defined data literacy as the ability to process, sort, and filter vast quantities of information, which requires knowing how to search, how to filter and process, to produce and synthesize. It is clear that these are also the characteristics of information literacy (Via Koltay, 2015)

Data literacy can be simply defined as "the ability to understand and use data effectively to inform decisions" (Mandinach and Gummer (2013, p.30). On the other hand, this definition indicates that data literacy is a specific skill set and knowledge base that enables us to transform data into information and ultimately into actionable knowledge, which comprises developing hypotheses, identifying problems, interpreting the data, and determining, planning, implementing, as well as monitoring courses of action. (Via Koltay, 2015)

According to Calzada Prado and Marzal (2013), data literacy enables individuals to access, interpret, critically assess, manage, handle and ethically use data. Managing, that appears in this definition comprises preservation and curation, and this definition is much more comprehensive than the above ones.

According to Calzada Prado and Marzal (2013), data literate persons have to know how to select and synthesize data and combine it with other information sources and prior knowledge. They have to recognize source data value, be familiar with data types and formats

(Via Koltay, 2015)

Mandinach and Gummer (2013), enumerate data literacy skills that include knowing how to identify, collect, organize, analyze, summarize, and prioritize data. The last two skills are especially worth of attention as they are the ones that do not appear in other lists. Developing hypotheses, identifying problems, interpreting the data, and determining, planning, implementing, as well as monitoring courses of action also pertain to required skills and add the need for tailoring data literacy to the specific uses.

Data literacy, as it is understood by the Association of College and Research Libraries, focuses on understanding how to find and evaluate data, giving emphasis to the version of the given dataset, the person responsible for it, and does not neglect the questions of citing and ethical use of data (ACRL, 2013).

The Association of College and Research Libraries (2013) focuses on understanding how to find and evaluate data, giving emphasis to the version of the given dataset, the person responsible for it, and does not neglect the questions of citing and ethical use of data. This literacy concentrates on ownership and rights issues, and cuts across disciplinary boundaries and the traditional structures of academic library organizations.

(Via Koltay, 2015)

When recommending unified terminology and voting for the use of the term data literacy, Koltay (2015) identified several differing concepts and terms that are used for denoting this concept. There is data information literacy (Carlson et al., 2011), science data literacy (Qin and D'Ignazio, 2010) and research data literacy (Schneider, 2013).

we can identify data literacy as a specific skill set and knowledge base, which empowers individuals to transform data into information and into actionable knowledge by enabling them to access, interpret, critically assess, manage, and ethically use data. Not forgetting that literacies are multiple, multimodal, and multifaceted (Koltay, 2015)

"Data literacy is the ability to collect, manage, evaluate, and apply data, in a critical manner" (Ridsdale et al. 2015)

We define data literacy as "the desire and ability to constructively engage in society through and about data." Five observations emerge from this definition: 1. "Desire and ability" highlights technology as a magnifier of human intent and capacity. 2. "Ability" underlines literacy as a continuum, moving away from the dichotomy of literate and illiterate. 3. "Data" is understood broadly as "individual facts, statistics, or items of information." 4. "Constructively engage in society" suggests an active purpose driving the desire and ability. 5. And "through or about data" offers the possibility for individuals to engage as data literate individuals without being able to conduct advanced analytics. This definition—as well as the nature of data itself—encompasses elements and principles from each of these sub-kinds of literacy (such as media, statistical, scientific computational, information and digital literacies), moving away from medium-centred definitions of literacy towards a more encompassing one. (Bhargava, et.al., 2015)

There is a sense in some of the literature that "data literate" should not be a label reserved for data scientists or specialists. Data literacy should be thought of as "the ability of non-specialists to make use of data" (Frank et al. 2016, abstract)

Measure "a person's ability to read, work with, analyze and argue with data" (Qlik 2018, p. 3),

Data literacy is the "ability to derive meaningful information from data" (Sperry 2018).

Working from the review by Ridsdale et al., Data to the People put forth an even more concise definition of data literacy, where data literacy is "our ability to read, write and comprehend data, just as literacy is our ability to read, write and comprehend our native language" (Data to the People 2018).

Literacy broadly means having competency in a particular area. Data literacy includes the skills necessary to discover and access data, manipulate data, evaluate data quality, conduct analysis using data, interpret results of analyses, and understand the ethics of using data. (Department of National Defence, 2019) "Data literacy" is formally called out as a new core competency as part of a clear commitment to the organization and leadership valuing "information as a strategic asset." Training programs (online and/or in-person; internal and/or external) are available and supported across all required levels of proficiency. (Gartner, 2019, Toolkit)

"To summarize, a data literate individual would, at minimum, be able to understand information extracted from data and summarized into simple statistics, make further calculations using those statistics, and use the statistics to inform decisions. However, this definition is context-dependent, which will be illustrated below." (Bonikowska, Sanmartin and Frenette, Statistics Canada, 2019) "Data literacy is the ability to understand, find meaning, interpret, and communicate using data. Just as language literacy is the set of knowledge and skills to communicate and inform with words, data literacy encompasses a similar set of knowledge and skills to communicate and inform with data. " (Wells, 2022, Part 1) "A common misconception equates data literacy with the ability to understand and create charts and graphs, limiting the concept to literacy as it applies to data visualization. Full data literacy encompasses all of the skills to understand, find meaning, interpret, and communicate with data. A fully data-literate individual has a working knowledge of where data comes from, how it is processed, how it is organized, how it is managed, and how it is used." (Wells, 2022, Part 2)