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Data Literacy: Concepts

Data Literacy: Concepts Home




The representation of facts as text, numbers, graphics, images, sound, or video (The Department of National Defence and Canadian Armed Forces Data Strategy, 2019) [More]

Data Hub
Ingesting, integrating and provisioning data between and among a range of producing and consuming applications, data stores. A balance of collecting and connecting to data. (Gartner, 2029, Toolkit) [More]

Data Journey

Data Lake
Storing data in its native format for exploration, offering an unrefined view of data for highly skilled data scientists and analysts. (Gartner, 2019, Toolkit) [More]

Data Literacy
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)

Wolff, 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)


Data Literacy Assessment

Data Mart
A subset of a data warehouse oriented to a specific business function, group or purpose. (Gartner, 2019, Toolkit) [More]

Data maturity
Data maturity is a measurement of the extent to which an organization is utilizing their data. To achieve a high level of data maturity, data must be deeply ingrained in the organization, and be fully incorporated into all decision making and practices. Data maturity is often measured in stages. (Palmer, 2021) [More]

Data Quality
A discipline ensuring that data is "fit for use" in business processes, includes cleansing, matching, profiling and enrichment. (Gartner, 2019, Toolkit) [More]

Data skills or competencies
Data literacy competencies are the knowledge and skills you need to effectively work with data. ... The knowledge required to know what data is and what are different types of data. This includes understanding the use of data concepts and definitions. (Statistics Canada, 2020) [More]

Data Warehouse
An architecture that stores data extracted from transaction systems, operational data stores and external sources, with aggregates for enterprise-wide reporting and analysis for predefined business needs. (Gartner, 2019, Toolkit) [More]

Digital literacy
Digital literacy is the ability to access, manage, understand, integrate, communicate, evaluate and create information safely and appropriately through digital technologies for employment, decent jobs and entrepreneurship. It includes competences that are variously referred to as computer literacy, ICT literacy, information literacy and media literacy. (Law, 2018) [More]

Digital self-tracking

Gestalt Principles

Information is defined as data in context. Data and information are intertwined, and the policies and processes that govern and manage them should be aligned. (Department of National Defence, 2019) [More]

Literacy is the ability to identify, understand, interpret, create, communicate and compute, using printed and written materials associated with varying contexts. Literacy involves a continuum of learning in enabling individuals to achieve their goals, to develop their knowledge and potential, and to participate fully in their community and wider society. (Robinson 2005, p. 13) [More]


Open Data
Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike. (Open Knowledge Foundation, 2022) [More]

Predictive Analytics
Addresses the question of “what is likely to happen?" Relying on techniques such as predictive modeling, regression analysis, forecasting, multivariate statistics and pattern matching. Example: Demand forecasts of products by SKU. (Gartner 2019) [More]

Prescriptive Analytics
Addresses the question of “what should be done?“ Relying on techniques such as graph analysis, simulation, complex-event processing, recommendation engines, heuristics, neural networks and machine learning. Example: Proposed offerings based on propensity-to-buy analytics. (Gartner, 2019) [More]

Teaching Data Literacy
This concept includes:

  • Teaching frameworks or approaches
  • Examples of data literacy teaching approaches and program
  • [More]