Data Literacy: Themes
Change Management
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Citation and Sharing
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Critical Thinking
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Data Analysis
Eg. predictive and prescriptive analytics. [More]
Data Awareness
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Data Cleaning
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Data Communities
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Data Conversion and Interoperability
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Data Curation
Ensure that data is reliably retrievable for future reuse, and to determine what data is worth saving and for how long) (Learn2Analyze, 2017) [More]
Data Description or Metadata
Metadata management: The discipline of managing information that describes various facets of a data asset to improve its usability. Shorthand: "Data about data." (Gartner, 2019, Toolkit)
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Data Discovery and Exploration
Data Discovery: automatically finding, visualizing and narrating important findings within datasets (such as correlations, exceptions, clusters, links and predictions) that are relevant to users without requiring them to build models or write algorithms. (Gartner, 2019, Toolkit)
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Data Ethics
Be able to use the informed consent, be able to protect individuals’ data privacy, confidentiality, integrity and security, be able to apply authorship, ownership, data access (governance), re-negotiation and data-sharing. (Learn2Analyze, 2017) [More]
Data Evaluation or Assessment
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Data Gathering or Data Collection
be able to obtain, access and gather the appropriate data and/or data sources, and be able to apply data limitations and quality measures (e.g., validity, reliability, biases in the data, difficulty in collection, accuracy, completeness). (Learn2Analyze, 2017) [More]
Data Governance or Stewardship
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Data Interpretation
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Data Management
Consistently describing the core entities of an organization across different views/users of the same data, including: customers, prospects, citizens, suppliers, sites, hierarchies, chart of accounts etc. (Gartner, 2019, Toolkit)
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Data Manipulation
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Data Modeling or Architecture
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Data Policy
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Data Preservation and Reuse
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Data Quality
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Data Requirements
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Data Science and Machine Learning
Includes the following:
Natural language processing: NLP is a way for computers to analyze, understand and derive meaning from human language in a smart and useful way. NLP is a subset of artificial intelligence (AI).
Examples: Sentiment analysis, speech-to-text recognition, automatic summarization and language translation.
Natural language generation: NLG automates the creation of language or content from data inputs.
Examples: Weather reports, form letters and financial reports.
Artificial intelligence: AI is a set of related technologies that seems to emulate human thinking and action by learning, coming to its own conclusions and enhancing human cognitive performance (also known as cognitive computing) or replacing people on execution of nonroutine tasks.
Machine learning: ML algorithms are composed of many technologies (such as deep learning, neural networks and natural-language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information inputs.
Example use cases: Autonomous vehicles; automatic speech recognition and generation; detecting novel concepts and abstractions.
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Data Security
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Data Standards
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Data Storytelling
A combination of data visualization, narrative (the plotline) and context (the surrounding situation/scenario). (Gartner, 2019, Toolkit)
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Data Strategy or Culture
Example: "Meetings are data-driven and analytically rich. Metrics and analytics are at the forefront of business decision-making, not an afterthought to validate an opinion. Data is trusted, and context of data is understood and appreciated. We discuss outcomes and moments powered by data and insight." (Gartner, 2019, Toolkit) [More]
Data Systems and Tools
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Data Valuation
Understanding the business value of data scientists, data engineers and business analysts and the importance of meeting them frequently and productively. ... understanding how data adds value to business decisions.
(Gartner, 2019, Toolkit)
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Data Visualization
Use of dashboards (e.g., dials, gauges, charts and maps), infographics, flow charts, decision trees, slide show/series. (Gartner, 2019) [More]
Data-Informed Decision-Making
Examples: Leadership presentations include: key performance metrics; related analysis, visualization and storytelling; roles and moments affected are described and data-driven actions taken; explanation of results, business impact and outcomes achieved.
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Identifying Problems With Data
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Inquiry Process
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Plan, Implement and Monitor
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Present Data Verbally
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Statistics and Critical Reasoning
Examples: Mean: The average (like the average grades of a set of students' scores in a classroom).
Median: The middle number of a set of numbers arranged in order. If no middle, add the two central numbers and divide by two.
Mode: The number that occurs most often in a set of numbers.
Standard deviation: A measure of how spread out the numbers are from the center of a set of numbers.
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Using or Innovating With Data
Example: "Data is a prevalent element of ideation and how we explore new business ideas. In our meetings, it is common to hear: "What if we had access to that data? Could others leverage this data? Can we blend this data with that data? Who else might benefit from this data? What insights does this data provide? What if we share this data, and are we allowed to? What data is available from our partners?"" (Gartner, 2019, Toolkit) [More]