What story is data sharing? What story is it not showing? What attributes, facets and interactions are highlighted and why? What is the intention of unveiling the story? When considering data, one is reminded of how perspective shapes what story we want to share and how it is received.

When diving into perspective with my learners, I always start with Brendan Wenzel’s, They All Saw a Cat. Read below by @simicrane.

Data, when curated and worked within a space such as Palladio is three-dimensional and non-linear. I was so intrigued by the interconnections the data shared and how the facets worked together. How were the smaller communities decided? Were they random? When decomposing the interconnections, I looked for the nodes with less connectivity and wondered why that was?

I wondered if the data would look different with data collection done elsewhere in the world in a non-university cohort. The nodes were the most visually telling to me because they drew my eye in. When I pulled the nodes apart with my curser, I recognized the visual connections with more ease. The other questions that I had in looking at this data were based on what criteria people used to shape their opinions. Were their choices based on a memory or connection to a song or a genre of music? An extension of that, when an individual is indifferent to music, is choosing it an accurate reflection of their perception?

Perception tells this data story. As individuals, we choose the experiences, biases and data to form our decisions, and the data isn’t capable of depicting an entire narrative.

To explore this more within a context I was more familiar, I used an existing Excel Sheet from our team of educational consultants and put it into Palladio, to see if I could better understand what a different set of data might share.

Looking at the depicted data, I can see the interconnectivity of our team’s work. I can see the number of people we are working with and the areas of learning we are focusing on. However, the data is still black and white, lacking the depth, real world context. It doesn’t portray the nuances of each school, the schools’ goal, or the intentionality of all those involved. One may look at one node and draw a conclusion, but without the back story, and a deeper awareness of the bigger picture, misguided conclusions could easily be drawn.

One needs to be cognizant that a data set is the merely the beginning of a story. Within that there are biases, perspectives, and variables that may not be accounted for. The way data is presented shapes a narrative. The story unfolds between the each node and with every edge. The obstacle is for one to continually ask, what isn’t being told in a narrative, what is missing? Are there better questions we can ask?

Palladio is a valuable tool to help one start asking bigger questions. It’s a captivating opening paragraph to a really great story with many different interpretations.