Data is everywhere, but how do you know which data you can trust? Using poor quality data can lead to erroneous results and even reputational damage. Before deciding to trust a dataset, assess data quality by considering both the source and the data itself.
Consider the Source
Where did you get the data files? From a source that can ensure the files haven’t been changed or corrupted? Are you using the latest, most complete version of the data?
What do you know about the research methodology? Is there an associated article you can read to assess the research design? Do the data collection and processing methods meet the standards for high quality research in your field?
6 Dimensions of Data Quality
Once you’re assured the data comes from a quality source, assess the data itself. Although assessment tools for specific types of data might include additional measures, most data scientists agree that these six dimensions are essential for assessing data quality.
Accuracy
data correctly represents the source material
Completeness
expected values are fully present and known nulls are clearly marked
Consistency
data is recorded uniformly both within variables and across the dataset
Timeliness
data represents a time period appropriate for research purposes
Uniqueness
data records are not unnecessarily duplicated
Validity
data values fit within defined ranges or categories
See this past Data Nudge for more tips on using data you didn’t collect yourself.
Talking to Students about Data Quality
Try this Data Quality Dimensions Cheat Sheet from datacamp or check out the Data Literacy video series from Arizona State University, which has 10-minute instructional videos that teach students how to recognize quality data in a variety of fields and contexts.
All Data Nudges are licensed under CC BY 4.0. You are free to share, adopt, or adapt them and cite the Illinois Research Data Service.