Melinda Roy
Business intelligence software providers often promote tags as way to help users find and filter content. Yet many data analysts and institutional research administrators, including myself, are quick to dismiss them, saying we’ve never seen it done well. But focusing on the messiness of end-user assigned tags ignores their function as a data literacy learning tool.
In higher education, traditional learning assessments evaluate how well a student can explicitly, externally describe and contextualize taught information through essays, tests, or verbal argument. But organizational knowledge management theory points to two types of knowing: implicit (internal, habitual) and explicit (able to be documented)—the first of which is individual, and the second of which can be communal.
Social-tag use can be both an externalized expression of implicit knowledge, and a community conversation about meaning, theme, and application of an item.
For individuals, the practice of tagging a dashboard with their own keyword is an expression of how they have internalized and contextualized the content into their own work. It represents a user’s skillfulness at data interpretation and classification. As other users tag the dashboard with different keywords, themes will emerge. When seeing their word in a list or wordcloud, the individual is given an opportunity to self-correct. Maybe their tag doesn’t fit with the others. They might ask, “Why does mine not fit, did I get something wrong, or is everyone else wrong?”. This engages them in critical thinking and self-initiated self-debate. Meanwhile, the other users might ask the same questions, or wonder if they’ve missed an opportunity for further application. Both users are practicing their data literacy skills without a teacher. When they decide to delete or update their tags, they further curate the tags for future users to perform the same skill-building and learning tasks.
Additionally, social tags build a user’s internal lexicon and data dictionary. They learn new jargon, identify frequently used institutional acronyms and terms, and add nuance for others by adding their own keywords.
Beyond self-assessment and correction, dashboard creators can analyze this data to identify areas of confusion, misinformation, or conflicting definitions to address with their end users. If institutional research, data analysts, and data creators refrain from adding tags themselves, attempting to correct tags, or guide users towards a specific set of descriptors, the collection of tags can represent end-user content understandings and associations. Then, this analysis can inform data governance conversations, identify topics, themes, and subjects, for future dashboard products or learning sessions. It is also useful for identifying how onboarding users to data dashboards and meaning is currently falling short. This can be used for process review and further instructional document development. For offices struggling to identify a best approach for organizing a large collection of data dashboards, analyzing tag use by user profile can inform a modifying the strategy to better suit how users actually engage with data products.
As a research analyst, with a background in library cataloguing and information management, I often prioritize categorization from a top-down, descriptive perspective. But bottom-up user taxonomies, developed through processes like social-tagging, offer more opportunities for understanding the state of data literacy, analyzing and developing repository organization. I have to remind myself there is a reason libraries have librarians, because highly-structured subject-based taxonomies are often not intuitive for end-users, and accessibility is always more important than the perfectly organized. Tags offer a hands-off, peer-guided way for users to navigate and discover the breadth of content available.
For organizations without a formal education or onboarding program for their data dashboard users, enabling and promoting social tag use is a quick win for developing data literacy.
References:
Kannan, P., & Nam, H. (2014). The Informational Value of Social Tagging Networks. Journal of Marketing, 21-40.
Kimmerle, J., Cress, U., & Held, C. (2010). The interplay between individual and collective knowledge: technologies for organisational learning and knowledge building. Knowledge Management Research & Practice, 33-44.