Use the tabs to navigate to resources on data literacy.

Data Literacy is complex and covers a lot of ground, as you can see in this relatively comprehensive definition from The Oceans of Data Institute: “The data literate individual understands, explains and documents the utility and limitations of data by becoming a critical consumer of data, controlling [one’s] personal data trail, finding meaning and taking action based on data. [One] can identify, collect, evaluate, analyze, interpret, present and protect data."

Data literacy include the abilities to:

Identify data

• What objective requires and justifies data?
• What data is already ethically collected?
• Is that data complete, relevant & valid?
• What other data is needed?
  • What would be the potentials risks of collecting & holding this data? 
  • What are the costs (employee time, student time, creating barriers, etc.)?
  • What authorization do you need to collect this data?
  • What are the potential benefits of this data?

Collect data 

• What is the best method collecting the best data consistent with ethics?
  • What is the most practical method of collecting sufficient data consistent with ethics?

Protect data, protect privacy

• How will the data be secured?
• Does the data need to be identified with individuals?
• What is the schedule for retaining and deleting data?

Organize & clean data

• What  norms and ethics are established to guide data cleaning and organizing?
  • Does everyone understand the risk of bias and the plan to reduce/eliminate it?
  • How will duplicate or erroneous entries be eliminated or averaged out?
  • What is the plan for addressing outliers or structural errors?
• Are there multiple data sources that need merged?
• Is there consistency, conformity, completeness and currency of the data?
• Are there file naming conventions and secured shared file locations?

Evaluate & analyze data

• Is this data useful?
  • Some data indicates a problem with the data process that must be addressed before the data can provide insight regarding objectives
  • Was any data collected that was not needed?
  • What any data needed that was not collected?
• Is the data consistent and high quality?
• Can patterns be detected in valid data that relate to your objective?
• Can you support claims of correlation or causation with the data?
• How does your data compare to benchmarks or averages?
• What models can define relationships in your data?

Explain & present data

• How can data be accurately visualized for ease of understanding?
• How can data be contextualized for your audience?
  • What is explicit in the data?
  • What is inferred in the data?
• What action toward the stated objective is indicated? 

Appy data

• Based on this data, what changes/improvements in learning, decision-making, or problem-solving are needed?
• Based on this data, what improvements are needed to the data plan?

Delete or archive data

• Do any state/federal mandates determine how long the data must be kept?
• What institutional policies govern how, how long, and where data is maintained?
• How long can this data provide useful insights?
• How will the data be safely deleted or archived?