Data Literacy Competencies

Data Literacy Competencies

Data Literacy Competencies
Picture: Volker Schwartze

The term Data Literacy describes a wide variety of individual competencies, and different approaches for a definition of the competency framework exist.

Data Literacy does not only include competencies concerning the collection and generation of data but also concerning their reception.1 During the coding, an awareness for the removed or added context is required. This happens e.g., due to the aggregation of data in the form of mean values or the choice of a specific way to visualise the data. The decoding process, on the other hand, requires among others an understanding of the actual information content of a statistic or visualisation.1

Data Literacy competencies during the work with data (modified after Schüller et al., 2019) Data Literacy competencies during the work with data (modified after Schüller et al., 2019) Picture: Volker Schwartze (CC-BY-4.0, modified after Schüller et al. 2019)

Data Literacy should not be understood as a synonym for Data Science. While Data Science represents an independent research field and includes mainly technical competencies regarding Statistics and Computer Science, Data Literacy is a basic competency that is relevant for every person at a certain level of expertise.

The following overview is based on the Data Literacy competency matrix from Ridsdale et al. (2015)2, which is one of the most widely used competence frameworks to date. The 21 individual competencies from 5 knowledge areas are categorised into conceptual, basic and advanced competencies. Further descriptions and examples for the competencies are given.

Conceptual Framework Show content

Introduction to Data (conceptual)

  • Basic knowledge and understanding of data
  • Understanding of relevance and applications of data
  • Knowledge about different data types and formats
Data Discovery and Collection Show content

Data Collection (basic)

  • Identification of relevant data
  • Collection of data using appropriate methods (e.g. observations, measurements, simulations)
  • Search for and collection of existing data
  • Example: use of search engines and databases

Evaluating and Ensuring Quality of Data and Sources (basic)

  • Critical assessment of data sources for trustworthiness
  • Critical evaluation of the quality of datasets (e.g. errors, completeness, relevance, representativity)
Data Management Show content

Data Organisation (basic)

  • Knowledge of basic data organisation methods and tools
  • Assessment of data organisation requirements
  • Example: meaningful naming of files, development of logical folder structures, structure of spreadsheets

Data Manipulation (basic)

  • Assessment of methods to clean data
  • Identification of outliers and anomalies
  • Processing of data (e.g. filter, sort, merge)

Data Conversion (advanced)

  • Knowledge of different data types and conversion methods
  • Conversion of data from one format or file type to another

Metadata Creation and Use (advanced)

  • Awareness for the importance of data documentation
  • Creation of metadata descriptors
  • Assignment of appropriate metadata descriptors to original data sets
  • Application of relevant methods and standards

Data Curation, Security, and Re-Use (advanced)

  • Assessment of data curation requirements (e.g. retention schedule, storage, accessibility, sharing requirements, etc.)
  • Assessment of data security requirements (e.g. restricted access, protected drives, etc.)

Data Preservation (advanced)

  • Assessment of requirements for preservation and application of appropriate methods / tools
Data Evaluation Show content

Data Tools (conceptual)

  • Knowledge of data analysis tools and techniques
  • Selection of appropriate data analysis tool or technique
  • Application of data analysis tools and techniques

Basic Data Analysis (basic)

  • Development of analysis plans
  • Application of analysis methods and tools
  • Implementation of exploratory analyses
  • Evaluation of results of analysis and comparison to results of other analyses

Data Interpretation (Understanding Data) (basic)

  • Ability to read and understand charts, tables and graphs
  • Identification of key messages and integration with other important information
  • Identification of discrepancies within the data

Identifying Problems Using Data (basic)

  • Use of data to identify problems in practical situations (e.g. workplace efficiency)
  • Use of data to identify higher level problems (e.g. politics, environment, scientific experimentation, marketing, economics, etc.) 

Data Visualisation (basic)

  • Creation of meaningful tables to organise and visually present data
  • Creation of meaningful graphical representations of data
  • Evaluation of the effectiveness of graphical representations
  • Critical assessment of graphical representations in terms of accuracy and misrepresentation of data

Presenting Data (basic)

  • Assessment of the desired outcome(s) for the data presentation
  • Assessment of audience needs and familiarity with the subject(s)
  • Utilisation of meaningful tables and visualisations
  • Clear and coherent presentation of arguments and/or outcomes

Data Driven Decisions (basic)

  • Prioritisation of information garnered from data
  • Conversion of data into actionable information
  • Weighing the merit and impacts of possible solutions / decisions
  • Implementation of decisions / solutions
Data Application Show content

Critical Thinking (conceptual)

  • Awareness of high-level / complex issues and challenges associated with data
  • Critical thinking with respect to working with data

Data Culture (conceptual)

  • Recognition of the importance of data
  • Support of an environment that fosters critical use of data in education, research, and decision-making

Data Ethics (conceptual)

  • Awareness of legal and ethical issues associated with data
  • Application of and working with data in an ethical manner

Data Citation (basic)

  • Knowledge of widely accepted data citation methods
  • Creation of correct citations for secondary data sets

Data Sharing (basic)

  • Assessment of methods and platforms for data sharing
  • Sharing of data in a legal and ethical manner

Evaluation of Decisions Based on Data (advanced)

  • Collection of follow-up data to assess effectiveness of decisions or solutions based on data
  • Analysis of follow-up data and comparison of the results with other findings
  • Evaluation of decisions or solutions based on data
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