Data literacy competencies

Data literacy competencies are the knowledge and skills you need to effectively work with data.

Data analysis

The knowledge and skills required to ask and answer a range of questions by analyzing data including developing an analytical plan; selecting and using appropriate statistical techniques and tools; and interpreting, evaluating and comparing results with other findings.

Data awareness

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.

Data cleaning

The knowledge and skills to determine if data are 'clean' and use the best method and tools to take necessary actions to resolve any problems to ensure data are in a suitable form for analysis.

Data discovery

The knowledge and skills to search, identify, locate and access data from a range of sources related to the needs of an organization.

Data ethics

The knowledge that allows a person to acquire, use, interpret and share data in an ethical manner including recognizing legal and ethical issues (e.g., biases, privacy).

Data exploration

The knowledge and skills required to use a range of methods and tools to learn what is in the data. The methods include: summary statistics; frequency tables; outlier detection; and visualization to explore patterns and relationships in the data.

Data gathering

The knowledge and skills to gather data in simple and more complex forms to support the gatherer's needs. This could involve the planning, development and execution of surveys or gathering data from other sources such as administrative data, satellite or social media data.

Data interpretation

The knowledge and skills required to read and understand tables, charts and graphs and identify points of interest. Interpretation of data also involves synthesizing information from related sources.

Data management and organization

The knowledge and skills required to navigate internal and external systems to locate, access, organize, protect and store data related to the organization's needs.

Data modeling

The knowledge and skills required to apply advanced statistical and analytic techniques and tools (e.g. regression, machine learning, data mining) to perform data exploration and build accurate, valid and efficient modelling solutions that can be used to find relationships between data and make predictions about data.

Data stewardship

Knowledge and skills required to effectively manage data assets. This includes the oversight of data to ensure fitness for use, the accessibility of the data, and compliance with polices, directives and regulations.

Data tools

The knowledge and skills required to use appropriate software, tools, and processes to gather, organize, analyze, visualize and manage data.

Data visualization

The knowledge and skills required to create meaningful tables, charts and graphics to visually present data. This also includes evaluating the effectiveness of the visual representation (i.e., using the right chart) while ensuring accuracy to avoid misrepresentation.

Evaluating data quality

The knowledge and skills required to critically assess data sources to ensure they meet the needs of an organization. This includes identifying errors or problems and taking action to correct them. This also includes awareness of organizational policies, procedures and standards to ensure good quality data.

Evaluating decisions based on data

The knowledge and skills required to evaluate a range of data sources and evidence in order to make decisions and take actions. This can include monitoring and evaluating the effectiveness of policies and programs.

Evidence based decision-making

The knowledge and skills required to use data to help in the decision-making and policy making process. This includes thinking critically when working with data; formulating appropriate business questions; identifying appropriate datasets; deciding on measurement priorities; prioritizing information garnered from data; converting data into actionable information; and weighing the merit and impact of possible solutions and decisions.

Metadata creation and use

The knowledge and skills required to extract and create meaningful documentation that will enable the correct usage and interpretation of the data. This includes the documentation of metadata which is the underlying definitions and descriptions about the data.

Storytelling

The knowledge and skills required to describe key points of interest in statistical information (i.e., data that has been analyzed). This includes identifying the desired outcome of the presentation; identifying the audience's needs and level of familiarity with the subject; establishing the context; and selecting effective visualizations.