Data Literacy ~ Competency Model or Framework

A competencies framework is a list of skills, competencies or capacities associated with Site Title. It may be organized according to: specific skills, content areas, or levels or degrees of maturity.

Data literacy competencies are organized into Themes

Change Management

Existing definitions may become inadequate over time as the types of data available change to become bigger and more complex (Wolff et al. 2016), and emerging technologies such as artificial intelligence change how we think of and use data (Statistics Canada, 2020).

Some types of data – master data and reference data – should have tightly controlled sets of valid values. These values appear in thousands and millions of transactions; without change control, different repositories storing master and reference data get out of sync (Data Governance Institute, 2020).

, Open Data Institute, 2020;, Alan D. Duncan, Donna Medeiros, Aron Clarke, Sally Parker, Gartner, 2021;, Gartner, 2019;, Dave Wells, Eckerson Group, 2022;, Data Governance Institute, 2022;

Citation and Sharing

Knowledge of widely-accepted data citation methods, creates correct citations for secondary data sets (Ridsdale, et.al., 2015).

Although you are likely creating your own, original data visualizations, they are based on external data sources. Any reader who is looking at your data visualization should be able to find its original source. Don’t forget to cite your data source that you used to create your visualization. Review some tips on citing data from the Ryerson University library (Mulvaney, et.al., 2022)

Chantel Ridsdale, et.al., Dalhousie University, 2015;, Nora Mulvaney, Audrey Wubbenhorst, Amtoj Kaur, Ryerson University, 2022;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;, Australian Public Service Commission, Government of Australia, 2021;

Critical Thinking

Conceptually, data literacy requires critical thinking, gaining knowledge from abstraction, and application of results. This critical, and often abstract reasoning is similar to computational thinking. Computational thinking involves “defining abstractions, working with multiple layers of abstraction and understanding the relationships among the different layers” (Ridsdale, et.al., 2015).

Chantel Ridsdale, et.al., Dalhousie University, 2015;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;, Hugo Bowne-Anderson, Mike Loukides, O'Reilly, 2022;

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. (Statistics Canada, 2020). Eg. predictive and prescriptive analytics.

, Gartner, 2019;

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 (Statistics Canada, 2020).

, Canada.ca, 2021;, Statistics Canada, Canada.ca, 2020;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;, Dave Wells, Eckerson Group, 2022;

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 (Statistics Canada, 2020).

Statistics Canada, Canada.ca, 2020;, Open Data Institute, 2020;, Apolitical, 2021;, Learn2Analyze, 2017;

Data Communities

Data communities - networks of engaged data users within an organization - represent a way for businesses to create conditions where people can immerse themselves in the language of data, encouraging data literacy and fueling excitement around data and analytics... to generate data insights that are truly valuable, people need to become fluent in data—to understand the data they see and participate in conversations where data is the lingua franca. Just as a professional who takes a job abroad needs to immerse herself in the native tongue, businesses who value data literacy need ways to immerse their people in the language of data (Compton, 2020).

, Open Data Institute, 2020;, Jason Compton, Forbes, 2020;

Data Conversion and Interoperability

Data processing methodology: understanding and/or applying statistical procedures used to deal with intermediate data and statistical outputs, e.g., weighting schemes, statistical adjustment, or methods for imputing missing values or source data (APS, 2021)

Knowledge of different data types and conversion methods, converts data from one format or file type to another (Ridsdale, et.al., 2015)

, Canada.ca, 2021;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Databilities, Data to the People, 2020;, Helena Sternkopf, Roland M. Mueller, University of Hawai'i at Manoa, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018;, Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;, Australian Public Service Commission, Government of Australia, 2021;, Dave Wells, Eckerson Group, 2022;

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).

You need to find data that is well-matched with analysis goals. Data seeking is not practical without first stating the goals and understanding the business, information, and data requirements. With known requirements, you can search for data. The datasets that you find should be evaluated for quality and trustworthiness, and sometimes to select the best fit among multiple datasets that are available (Wells, 2022).

The following questions have to be answered by data curators.
• Who owns the data?
• What requirements are imposed by others (such as funding agencies or
publishers)?
• Which data should be retained?
• For how long should data be maintained?
• How should it be preserved?
• What are the ethical considerations, related to it?
• What sort of risk management is needed?
• How is data accessed?
• How open should it be?
• How should the costs be borne?
• What alternatives to local data management exist? (Erway, 2013). (Cited in Koltay, 2015)

, Learn2Analyze, 2017;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Dave Wells, Eckerson Group, 2022;, Tibor Koltay, Journal of Librarianship and Information Science, 2015;

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).

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 (Statistics Canada, 2020).

, Gartner, 2019;

Data Discovery and Exploration

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).

The knowledge and skills to search, identify, locate and access data from a range of sources related to the needs of an organization (Statistics Canada, 2020). The methods include: summary statistics; frequency tables; outlier detection; and visualization to explore patterns and relationships in the data (Statistics Canada, 2020).

, Gartner, 2019;

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) (Statistics Canada, 2020).

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).

, Canada.ca, 2021;, Statistics Canada, Canada.ca, 2020;, Open Data Institute, 2020;, Apolitical, 2021;, Learn2Analyze, 2017;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Helena Sternkopf, Roland M. Mueller, University of Hawai'i at Manoa, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;

Data Evaluation or Assessment

To analyse, compare and critically evaluate the credibility and reliability of sources of data, information and digital content. To analyse, interpret and critically evaluate the data, information and digital content (Law, et.al., 2018).

Nancy Law, David Woo, Jimmy de la Torre, Gary Wong, UNESCO Institute for Statistics, 2018;, Open Data Institute, 2020;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;, Dave Wells, Eckerson Group, 2022;

Data Gathering or Data Collection

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 (Statistics Canada, 2020).

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).

Data collection, manipulation, and analysis processes: These behaviours that can be qualified mainly as motivational can be supplemented by distinct steps that are needed for data sharing. Some of them are described by Buckland (2011) as follows:
• Discovering if the suitable data set exists;
• Identifying its location;
• Examining if the copy is usable or not;
• Clearing if it is permissible to use;
• Ascertaining its interoperability, i.e. if it is standardized enough to be
usable with acceptable effort;
• Judging if its description is clear enough to indicate what the given data
set represents;
• Ascertaining trust;
• Deciding if the given dataset is usable for someone's purpose. (Cited in Koltay, 2015)

Statistics Canada, Canada.ca, 2020;, Korri Palmer, Safegraph, 2021;, Apolitical, 2021;, Learn2Analyze, 2017;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Databilities, Data to the People, 2020;, Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;, Australian Public Service Commission, Government of Australia, 2021;, Dave Wells, Eckerson Group, 2022;, Tibor Koltay, Journal of Librarianship and Information Science, 2015;

Data Governance or 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 policies, directives and regulations (Statistics Canada, 2020).

, Canada.ca, 2021;, L. Heeley, S. Wilkinson, Joint Nature Conservation Committee, Gov.uk, 2018;, Statistics Canada, Canada.ca, 2020;, Korri Palmer, Safegraph, 2021;, Open Data Institute, 2020;, Alan D. Duncan, Donna Medeiros, Aron Clarke, Sally Parker, Gartner, 2021;, Apolitical, 2021;, Australian Public Service Commission, Government of Australia, 2021;, Dave Wells, Eckerson Group, 2022;, elearning Curve, ;, elearning Curve, ;

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 (Statistics Canada, 2020).

Chantel Ridsdale, et.al., Dalhousie University, 2015;

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).

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 (Statistics Canada, 2020).

VAULTIS, short for data that is visible, accessible, understandable, linked, trustworthy, interoperable and secure. (Houston, AMCOM, 2021)

, Gartner, 2019;

Data Manipulation

Manipulating, processing, cleansing and combining data for further analysis or use. Automation of complex manipulation on large data volumes... covers reformatting, cleansing and combining of data from different sources for further analysis, storage or use. This includes simple processes to check and transform data, through to automating complex manipulation of large data volumes. This excludes analysis, in-depth domain knowledge and ongoing management of datasets (Heeley & Wilkinson, 2018).

L. Heeley, S. Wilkinson, Joint Nature Conservation Committee, Gov.uk, 2018;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Databilities, Data to the People, 2020;, Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Helena Sternkopf, Roland M. Mueller, University of Hawai'i at Manoa, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;, Australian Public Service Commission, Government of Australia, 2021;, Dave Wells, Eckerson Group, 2022;

Data Modeling or Architecture

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 (Statistics Canada, 2020).

Statistics Canada, Canada.ca, 2020;, Korri Palmer, Safegraph, 2021;, Alan D. Duncan, Donna Medeiros, Aron Clarke, Sally Parker, Gartner, 2021;, Apolitical, 2021;, Dave Wells, Eckerson Group, 2022;

Data Policy

A data policy contains a set of rules, principles, and guidelines that provide a framework for different areas of data management throughout the enterprise, including but not limited to data governance, data quality, and data architecture. Osthus

A data policy enables an organization to consistently address the broad range of potential developments and scenarios that may arise related to its creation, processing, use, and sharing of digital data.

, Open Data Institute, 2020;, Chartered Professional Accountants (CPA), 2020;

Data Preservation and Reuse

Assesses requirements for preservation, asseses methods and tools for data preservation, preserves data (Ridsdale, et.al., 2015).

Data preservation is the act of conserving and maintaining both the safety and integrity of data. Preservation is done through formal activities that are governed by policies, regulations and strategies directed towards protecting and prolonging the existence and authenticity of data and its metadata. The main goal of data preservation is to protect data from being lost or destroyed and to contribute to the reuse and progression of the data. (Wikipedia)

Databilities, Data to the People, 2020;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;

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 (Statistics Canada, 2020).

Koltay, 2015:  data quality is one of the cornerstones of the data-intensive paradigm of scientific research that is determined by multiple factors. The first one is trust, which is complex in itself. The elements of trust include the lineage, version and error rate of data and the fact that they are understood and acceptable (Buckland, 2011).

By reviewing quality attributes extensively, Giarlo (2013) argues that trust depends on subjective judgements on authenticity, acceptability or applicability of the data; and is also influenced by the given subject discipline, the reputation of those responsible for the creation of the data, and the biases of the persons who are evaluating the data. It is also related to cognitive authority, which has two levels. At an operational level, cognitive authority is the extent to which users think that they can trust the information.

On a more general level, cognitive authority refers to influences that a user would recognize as proper because the information therein is thought to be credible and worthy of belief (Rieh, 2002). The next factor of data quality is authenticity, which measures the extent to which the data is judged to represent the proper ways of conducting scientific research, including the reliability of the instruments used to gather the data, the soundness of underlying theoretical frameworks, the completeness, accuracy, and validity of the data. In order to evaluate authenticity, the data must be understandable."

---

 

, Canada.ca, 2021;, Apolitical, 2021;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Nora Mulvaney, Audrey Wubbenhorst, Amtoj Kaur, Ryerson University, 2022;, Databilities, Data to the People, 2020;, Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Helena Sternkopf, Roland M. Mueller, University of Hawai'i at Manoa, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;, Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;, Australian Public Service Commission, Government of Australia, 2021;, elearning Curve, 2022;, Tibor Koltay, Journal of Librarianship and Information Science, 2015;, Statistics Canada, Government of Canada, ;

Data Requirements

The ability to understand and prioritise user needs; and identify how data can be efficiently integrated into processes... By understanding the processes and data with which they work, a data specialist can effectively improve the efficiency and effectiveness of their work. To do this they need to understand the dependencies of their parts of the business and the importance of clarity in understanding data and other problems. (Heeley & Wilkinson, 2018)

L. Heeley, S. Wilkinson, Joint Nature Conservation Committee, Gov.uk, 2018;, Apolitical, 2021;

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.

, Gartner, 2019;

Data Security

Individual is aware of the main policies around data security, sharing and licensing. This includes awareness of the Data Protection Act and the appropriate action to take in the instance of a suspected or actual breach (Heeley & Wilkinson, 2018).

Processes are in place to ensure confidentiality, integrity, and availability of data. Only data that is necessary is collected/used. Consistent, companywide policies for secure and ethically sound data handling are constantly redefined and updated (Statistics Canada, 2020).

, Canada.ca, 2021;, L. Heeley, S. Wilkinson, Joint Nature Conservation Committee, Gov.uk, 2018;, Alan D. Duncan, Donna Medeiros, Aron Clarke, Sally Parker, Gartner, 2021;, Learn2Analyze, 2017;

Data Standards

Generic procedures and standards on how to handle data are formalized and widespread, and benefits are understood at all levels of the organization (Statistics Canada, 2020).

Make use of open data standards to build technology that’s easier to expand, upgrade and use with other technologies; meet API technical and data standards to help deliver better services (DSA, 2022)

, Canada.ca, 2021;, Open Data Institute, 2020;, Apolitical, 2021;, Data Standards Authority (DSA), 2022;

Data Storytelling

A combination of data visualization, narrative (the plotline) and context (the surrounding situation/scenario). (Gartner, 2019, Toolkit).

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." (Statistics Canada, 2020).

, Gartner, 2019;

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)

A data culture is a "learning environment within a school or district that includes attitudes, values, goals, norms of behavior and practices, accompanied by an explicit vision for data use by leadership for the importance and power that data can bring to the decision-making process. (Hamilton et al., 2009, p. 46)" (Mandinach, 2012)

The rules of using and caring for data, offered by Goodman (2014), can guide researchers in their effort to ensure that their data and analyzes continue to be of value. The recommended behaviours include:
• Conducting research with a particular level of reuse in mind;
• Linking data to someone's own publications as often as possible;
• Stating if someone wants to get credit for data and describing how it may
happen,
• Rewarding colleagues for sharing data;
• Publishing a description of processing steps in order to enable
interpreting and reusing data;
• Fostering and using data repositories;
• Sharing data with a permanent identifier (e.g. the Digital Object Identifier,
DOI). (Cited by Koltay, 2015)

 

, Gartner, 2019;

Data Systems and Tools

The knowledge and skills required to use appropriate software, tools, and processes to gather, organize, analyze, visualize and manage data (Statistics Canada, 2020).

Many different types of databases exist. The most common types in use today include flat files, spreadsheets, relational databases, multi-dimensional databases, and NoSQL databases. Data literate individuals need to be capable of working with databases of many different kinds (Wells, 2020)

• Spreadsheets (such as Excel and Open Office) – essential tools for quickly scanning and checking data as well as providing simple data manipulation and combination.
• Geographic Information Systems (such as QGIS and ESRI’s Arc toolset) – tools for holding, managing and integrating spatial information.
• Databases (such as Access, SQL Server, Oracle, PostGres, PostGIS) – powerful tools for manipulating and querying larger volumes of data.
• Database management tools (such as Toad, SQL Developer) – provides clear access to database structure for exploring and querying databases (Heeley & Wilkinson, 2018).

, Canada.ca, 2021;, Statistics Canada, Canada.ca, 2020;, Open Data Institute, 2020;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;, Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;, Australian Public Service Commission, Government of Australia, 2021;, Dave Wells, Eckerson Group, 2022;, ;

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)

, Gartner, 2019;

Data Visualization

Use of dashboards (e.g., dials, gauges, charts and maps), infographics, flow charts, decision trees, slide show/series (Gartner, 2019).

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 (Statistics Canada, 2020).

, Canada.ca, 2021;, L. Heeley, S. Wilkinson, Joint Nature Conservation Committee, Gov.uk, 2018;, Statistics Canada, Canada.ca, 2020;, Open Data Institute, 2020;, Apolitical, 2021;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Nora Mulvaney, Audrey Wubbenhorst, Amtoj Kaur, Ryerson University, 2022;, Databilities, Data to the People, 2020;, Helena Sternkopf, Roland M. Mueller, University of Hawai'i at Manoa, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018;, Andreas Grillenberger, Ralf Romeike, Proceedings of the 13th Workshop in Primary and Secondary Computing Education, 2018;, Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;, Australian Public Service Commission, Government of Australia, 2021;, Dave Wells, Eckerson Group, 2022;, Ferdio, ;, Statistics Canada, Government of Canada, 2020;

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. (Gartner, 2019, Toolkit)

Cramer, Little & McHatton (2015) describe a recursive five-step data-based decision-making model: goal identification, data collection, data reflection, identifying areas for improvement, and dissemination.

"DDDM is not just about the numbers or the data. It is about making actionable the data by transforming them into usable knowledge " (Mandinach, 2012)

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 (Statistics Canada, 2020). Also 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 (Statistics Canada, 2020).

, Gartner, 2019;

Dispositions

Mandinach & Gummer (2016) identify six dispositions: belief that all students can learn; belief in data/think critically; belief that improvement in education requires a continuous inquiry cycle; ethical use of data, including the protection of privacy and confidentiality of data; collaboration (vertically and horizontally); and communication skills with multiple audiences.

Bocala & Boudett () identify three 'habits of mind': shared commitment to action, assessment, & adjustment; intentional collaboration; and relentless focus on the evidence.

Edith Gummer, Ellen B. Mandinach, Teaching and Teacher Education, 2016;, Candice Bocala, Kathryn Parker Boudett, Teachers College Record, ;, Elizabeth H. Schultheis, Melissa K. Kjelvik, The American Biology Teacher, 2020;

Generate Data

"Generate data is another subcomponent of the 'use data' component, and it includes 'understand assessment', which expands into understand statistics and psychometrics, and 'use formative and summative assessments.'" (Gummer & Mandinach, 2015)

Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;

Identifying Problems With Data

Meta-skills include adaptability, deep thinking, and being able to critically assess problems (as opposed to simply regurgitating information), and are essential to engaging in the 21st century knowledge economy (Ridsdale, et.al., 2015).

The importance of doing diligence upfront, such as: scoping the problem correctly, gathering information, understanding the true goals of the analysis, defining what needs to get done by when, and putting this all together in a clear and concise written problem statement that gets signed off on by all stakeholders (Crowe, 2020).

Databilities, Data to the People, 2020;, Chantel Ridsdale, et.al., Dalhousie University, 2015;, Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Helena Sternkopf, Roland M. Mueller, University of Hawai'i at Manoa, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018;, Melissa Crowe, Tyler Technologies, 2020;

Inquiry Process

In the Inquiry Process "the components identify problems and frame questions lack subcomponents and elements... subelements (include) planning, guiding, designing, adjusting, differentiating, and individualizing instruction associated with the element." (Gummer & Mandinach, 2015).

Identifying and implementing change to create efficiencies and new opportunities by making existing processes, systems, tools and products better and/or creating new ones. (e.g.) Can identify deficiencies in current processes/systems and tools/ products, gain the required approval to make changes, and lead the implementation of those changes (APOS, 2021).

Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Edith Gummer, Ellen B. Mandinach, Teachers College Record, 2015;, Australian Public Service Commission, Government of Australia, 2021;, Dave Wells, Eckerson Group, 2022;

Plan, Implement and Monitor

In order to identify their own knowledge gap in the first place, they must have an understanding of how to plan for data collection, know how data can be identified and obtained and conceive how this data might eventually provide an answer to their initial question (Wolff, et.al., 2016).

Annika Wolff, Daniel Gooch, Jose J. Cavero Montaner, Umar Rashid, Gerd Kortuem, 2016;, Australian Public Service Commission, Government of Australia, 2021;

Present Data Verbally

Asssess the desired outcome(s) for presenting the data, assesses audience needs and familiarity with subject(s), plans the appropriate meeting or presentation type, utilizes meaningful tables and visualizations to communicate data, presents arguments and/or outcomes clearly and coherently (Ridsdale, 2015).

Databilities, Data to the People, 2020;, Helena Sternkopf, Roland M. Mueller, University of Hawai'i at Manoa, Proceedings of the 51st Hawaii International Conference on System Sciences, 2018;, Chantel Ridsdale, et.al., Dalhousie University, 2015;

Statistics and Critical Reasoning

Become critical consumers of statistically-based results reported in popular media, recognizing whether reported results reasonably follow from the study and analysis conducted; recognize questions for which the investigative process in statistics would be useful and should be able to answer questions using the investigative; produce graphical displays and numerical summaries and interpret what graphs do and do not reveal; recognize and be able to explain the central role of variability in the field; recognize and be able to explain the central role of randomness in designing studies and drawing conclusions; gain experience with how statistical models, including multivariable (GAISE, 2020).

Descriptive analysis develops statistics to illustrate the shape of the data, describing characteristics such as the distribution of values (Wells, 2022).

Know – understand – be able to interpret statistics commonly used with educational data (e.g., randomness, central tendencies, mean, standard deviation, significance) (Learn2Analyze, 2017).

, Gartner, 2019;

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)

, Gartner, 2019;