Why teach data divergence?

Data divergence occurs when different data are used to characterize problems or there are different interpretations of data – to explain what happened, why, the results and possible remedies.

• EcoGovLab research foregrounds the increasing importance of data in environmental issues. Conventional approaches to data literacy are not sufficient to address the multiple layers of information that students and researchers are confronted with today. While we consider "nefarious" cases such as misinformation and greenwashing as types of data divergence, our typology extends further than that. Often, scientists and other experts have genuine disagreement about what should be considered as useful and important data. Many environmental issues present data experts with different measurement priorities. For example, should air pollution be measured at the neighborhood level or the district level? This question could have implications for distribution of funding for scientific work.
• Conventional data literacy initiatives over-emphasize a separation between fact and opinion to evaluate information. This approach not only disregards the usefulness of qualitative research (disguised as opinion), it also undermines the various communities of data practitioners who work hard to give data value and meaning in our world. 
• To address these discrepancies, researchers at EcoGovLab have produced a typology of data divergence that can help researchers and students navigate many layers of data and information. It can help form their own interpretations of the issue at hand. Learning a typology of data divergence extends our vocabulary of data problems and provide us with a metacognitive skill to discern information.