Data Literacy

If you can count it, track it, describe it, or compare it—it’s probably data. Common types of data include:

• Quantitative
  • Measured or counted values—can be analyzed with math or statistics.
  • (e.g., coffees consumed per semester, damages awarded in civil litigation, quarterly revenue figures, white blood cell counts, survey scores, likes on a social media post, etc.)
• Qualitative
  • Descriptive, open-ended, or observational—rich in detail.
  • (e.g., verbatim quotes from interviews, open-ended survey responses, customer feedback from online reviews, clinical notes in a patient's record, legal opinions from judges, etc.)
• Categorical
  • Groupings, labels, or categories—not numerical, but sortable.
  • (e.g., academic major, type of device used to access a website, method of payment, blood type, legal case status, etc.)
• Structured Textual
  • Text in consistent, organized formats such as in lists and databases.
  • (e.g., references section formatted in APA style, nutrition facts label, metadata for digital assets, vital signs chart entries, court filing documents with docket numbers, etc.)
• Geospatial
  • Information tied to physical location.
  • (e.g., social media posts tagged with NY neighborhoods, foot traffic analytics / heatmaps for retail stores, geographic distribution of measles outbreaks, mapping crisis response zones for mobile units, crime scene locations, etc.)
• Time-Series
  • Collected or tracked over time.
  • (e.g., stock prices, sleep patterns, class performance over a semester, blood glucose trends throughout the day, timeline of events in a criminal investigation, etc.)
• Behavioral
  • Captured actions, typically from tech use or observation.
  • (e.g., YouTube watch history, step counts, online order patterns, time spent on different sections of an app, web browsing history in digital forensics, etc.)
• Sentiment/Emotional
  • Data capturing tone, mood, or opinion.
  • (e.g., tone of social media comments, emotional responses during product testing, mood tracking data from wellness apps, courtroom demeanor and its influence on jury perception, facial expression tracking in affective computing research, etc.)
• Transactional
  • Records of exchanges or interactions—often financial or system-based.
  • (e.g., returns and reimbursements, student worker hours logged and paid, procedure codes billed during a medical visit, bank transactions in fraud cases, frequency of service usage in employee wellness plans, etc.)
• Demographic
  • Information describing who people are (individual or group-level).
  • (e.g., age and gender, household income bracket, geographic location of users, employment status, immigration status in asylum applications, disability status and accommodation needs, etc.)

• Reading Data
  Understand what a dataset, graph, or statistic is saying.
• Questioning Data
  Ask who made it, how it was gathered, and what it might leave out.
• Interpreting Data
  Put numbers into context. A 5% increase—of what? Over how long? Compared to what?
• Using Data Ethically
  Do not cherry-pick. Don’t manipulate graphs to mislead. Do not erase people or communities in your analysis.
• Communicating with Data
  Use visuals and language that are clear, fair, and honest. Be transparent about your sources.

Every dataset is a result of choices.

Imagine a university claims, “Student satisfaction is up 30%.”

• Who was surveyed? Only freshmen? Only business majors?
• What questions were asked? “Are you satisfied?” or “How satisfied are you with X, Y, and Z?” and were the potential answers dichotomous, numerous, or free form?
• What didn’t they ask? What was left out?
• Who benefits from this data being shared? Who does not?

Data is created by humans. Humans decide what to count, what to ignore, how to ask, how to visualize, and how to share. Even the most impressive-looking graphs are shaped by human choices.

That doesn’t mean data is bad—it means you need to be curious and critical.

Try starting with these questions any time you encounter or engage with data, not only in your studies or in your workplace, but in everyday life:

1. Who collected the data? (Credibility depends on the source.)
2. How was the data collected? (Methodology affects accuracy.)
3. For what purpose was it collected? (Context matters.)
4. What might be missing? (Incomplete / misrepresentation.)
5. How is it being presented, and why that way? (Watch for bias or attempts of persuasion.)