Data Literacy is one of the five components of AI literacy in the ALiF Standard. Every conforming implementation addresses all five, with emphasis that varies by professional role.
Key elements
- Data quality assessment
- Data management practices
- Analysis methodologies
- Communication approaches
Example competencies
- Evaluates quality and limitations of datasets
- Manages data collection and processing appropriately
- Analyzes and interprets data for decision-making
- Communicates data insights through effective visualization
How it relates to the other components
Data Literacy (DAT) as foundational substrate. Data literacy provides the conceptual foundation that the other four components draw upon. Technical Understanding requires grasping how data shapes AI behavior. Critical Evaluation requires assessing data quality to judge output reliability. Practical Application requires working with data inputs and outputs. Ethical Considerations requires understanding data privacy, bias, and representation. While DAT is not a prerequisite that must be completed before other components, weaknesses in data literacy constrain development across the framework.
Sub-competencies (4)
DAT.1 Data Understanding
The ability to comprehend the nature, types, quality, and limitations of data that underlie AI systems. This sub-competency encompasses recognizing different data types and formats, evaluating data quality and identifying biases within datasets, understanding how data characteristics shape AI system behavior and outputs, and appreciating the constraints that data limitations place on AI reliability.
| Foundation (L1) | Intermediate (L2) | Advanced (L3) |
|---|---|---|
| Recognizes common data types and formats. Evaluates basic data quality (completeness, obvious errors). Understands that AI outputs depend on training data. Identifies straightforward biases in datasets. | Assesses data quality using multiple criteria. Evaluates how data characteristics shape AI behavior. Identifies subtle biases and their potential consequences. Understands the relationship between data provenance and output reliability. | Develops data evaluation frameworks that others adopt. Creates data quality standards for organizational use. Leads initiatives to improve data understanding across teams. Designs approaches for identifying data limitations in novel AI applications. |
DAT.2 Data Collection and Management
The ability to handle data responsibly throughout its lifecycle in AI-related contexts. This sub-competency encompasses collecting data using appropriate and ethical methods, organizing and storing data in accessible and secure formats, maintaining data quality and integrity over time, and adhering to privacy and security standards in data management.
| Foundation (L1) | Intermediate (L2) | Advanced (L3) |
|---|---|---|
| Collects data using standard, ethical methods. Organizes data for accessibility and basic security. Follows established data management protocols. Maintains data integrity in routine tasks. | Implements structured data management procedures. Designs data collection approaches for specific purposes. Creates systems for maintaining data quality over time. Ensures compliance with privacy and security standards. | Develops data governance strategies that embed quality and ethics into organizational data handling. Creates data management methodologies that others adopt. Leads institutional data stewardship initiatives. |
DAT.3 Data Analysis
The ability to analyze and interpret data in the context of AI use and AI-generated outputs. This sub-competency encompasses applying appropriate analytical methods to extract meaning from data, interpreting results with attention to uncertainty and limitations, using data analysis to evaluate AI system performance, and drawing defensible conclusions from data-driven evidence.
| Foundation (L1) | Intermediate (L2) | Advanced (L3) |
|---|---|---|
| Performs basic data analyses using standard tools. Interprets simple analytical outputs. Identifies obvious patterns and anomalies. Recognizes when analysis results are unreliable. | Applies appropriate analytical methods for different data types. Interprets results with attention to uncertainty and limitations. Uses analysis to evaluate AI system performance. Draws defensible conclusions from evidence. | Develops analytical frameworks that connect data analysis to strategic decision-making. Creates methodologies for evaluating AI performance across complex, multi-variable contexts. Leads analytical initiatives that inform organizational AI strategy. |
DAT.4 Data Visualization and Communication
The ability to represent and communicate data-driven insights effectively to relevant audiences. This sub-competency encompasses creating clear and accurate data visualizations, selecting appropriate visualization methods for different data types and audiences, interpreting and critically evaluating others’ data presentations, and communicating data-informed conclusions in ways that support decision-making.
| Foundation (L1) | Intermediate (L2) | Advanced (L3) |
|---|---|---|
| Creates simple, accurate data visualizations. Interprets basic charts and graphs correctly. Communicates data findings in plain language. Recognizes misleading visual representations. | Designs effective visualizations for different audiences. Selects appropriate visualization methods for data types. Develops data communication strategies for professional contexts. Creates narratives that make data insights accessible. | Creates data communication systems that connect insights to decision-making at organizational scale. Develops visualization approaches that reveal patterns in complex AI-related data. Mentors others in effective data storytelling. |
Definitions and level descriptors are quoted from ALiF Standard v1.0r1. The level descriptors are informative; role-specific descriptors are in the Competency Framework.