Making sense of data practices

Laura Ellis has been writing this week about solving business problems with data (link). The alert to her post came shortly after another link had taken me back to a presentation by Dan Weaving in 2017 on load monitoring in sport (link). A separate alert had drawn my attention to two Cassie Kozyrkov articles, one on hypotheses (link) and the second on what great data analysts do (link).

I have all these as tabs in my browser at the moment. They joined the tab holding David Snowden and Mary Boone’s (2007) discussion of a leader’s framework for decision-making (link).

These five connections make for fascinating reading. A good starting point, I think, is David and Mary’s visualisation that forms the reference point for the application of the Cynefin framework:

They observe “the Cynefin framework helps leaders determine the prevailing operative context so that they can make appropriate choices”.

The 2007 visualisation was modified in 2014 when ‘simple‘ became ‘obvious‘ (link). Disorder is in the centre of the diagram wherein there is no clarity about which of the other domains apply:

In a book chapter published in the year 2000 (link), David notes “the Cynefin model focuses on the location of knowledge in an organization using cultural and sense making …”. Laura, Dan and Cassie provide excellent examples of this sense-making in their own cultural contexts.

Many of my colleagues in sport will appreciate this slide from Dan’s presentation that exhorts us “to adopt a systematic process to reduce data by understanding the similarity and uniqueness of the multiple measures we collect”:

… whilst being very clear about the time constraints to share the outcomes of this process with coaches.

Photo Credit

Arboretum – Bonsai (Meg Rutherford, CC BY 2.0)

A Fra Mauro kind of week

Fra Mauro was a cartographer. He lived in the Republic of Venice in the fifteenth century.

I found out about him in James Cowan’s (1997) A Mapmaker’s Dream. In that account, Fra Mauro welcomed visitors from all over the world in his monastery and used their news to develop his map of the world.

I loved the idea that he could be in Venice and yet be connected with voyages of discovery and established trade routes.

I had a Fra Mauro feeling this week in rural New South Wales. Social media, particularly Twitter, brought me news of adventures elsewhere.

Jacquie Tran was on her way to a Sports Performance Research Institute New Zealand conference:

Javier Fernandez was at a conference:

Mark Upton was writing about returning ‘home’ in South Australia after all his travels. In his discussion of living in fellowship he wrote “We DO need to balance and share power by exploring the dynamic interaction of leadership and followship” (original emphasis).

By serendipity, I met Jo Gibson, who lives just 50 kms away. Jo is researching leadership and followership in the dynamic way that Mark advocates. I have the good fortune to be her PhD supervisor.

I ended my week, delighted in reading a quote from Albert Mundet far away in Spain: “We compete in the short term, but we may cooperate at longer term”.

From a Fra Mauro perspective, this sharing is immensely powerful.

For many years, I have hoped that open sharing is the new competitive edge and that through sharing we transform sport in the ways that about which Mark Upton and his colleagues write so eloquently and has been demonstrated so well in New Zealand and Spain this week.

Photo Credit

Venezia (Roberto Defilipi, CC BY-NC-ND 2.0)

#EL30 Graphing

Week 3 of Stephen Downes’ E-Learning 3.0 course is looking at Graphs.

Stephen recommended some resources for this topic. These included:

Vaidehi Joshi’s (2017) gentle introduction to graph theory. In her discussion of graphs, Vaidehi observes “in mathematics, graphs are a way to formally represent a network, which is basically just a collection of objects that are all interconnected”.  She distinguishes between directed graphs and undirected graphs and explains the ways edges connect nodes in these kind of graphs. An example of the former is Twitter (each edge created represents a one-way relationship), and of the latter Facebook (its edges are unordered pairs).

Vaidehi suggests a number of resources to provide details about graphs, one of them is Jonathan Cohen’s slide show Graph Traversal. He defines a graph as a “general structure for representing positions with an arbitrary connectivity structure” that has a collection of vertices (nodes) and edges (arcs). An edge connects two vertices and makes them adjacent.

A second resource shared by Stephen is Fjodor Van Veen’s (2016) Neural Network Zoo. In his post Fjodor shares a “mostly complete chart of Neural Networks’ and includes a detailed list of references to support his visualisation of the networks.

A third resource continues the visualisation theme. Vishakha Jha (2017) uses this diagram to inform the discussion of machine learning:

A fourth resource recommendation is Graph Data Structure and Algorithms (2017). This article aggregates a large number of links to graph topics. It includes this explanation:

One of the E-Learning 3.0 course members, Aras Bozkurt, exemplified this theme in this tweet and in doing so underscored the skills available within self-organising networks :

It was a great way to end and start conversations about graphs.

Photo Credits

Title image is from Gonçalves B, Coutinho D, Santos S, Lago-Penas C, Jiménez S, Sampaio J (2017) Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football. PLoS ONE 12(1): e0171156. https://doi.org/10.1371/journal.pone.0171156

Other images are frame grabs for the resources cited in this post.