Dancing with data

Tirthajyoti Sarkar observed in a recent post (link):

a data-driven analytics process is much like a complex and intricate dance performance, where a high degree of harmony between the participants — data extraction, wrangling, statistical modeling, business logic, etc.— is absolutely necessary for any measure of success.

His thoughts about a data analytics process resonated strongly with my view of the analysis of performance as an interdisciplinary activity and one that seeks to make sense of “a heady cocktail of multiple tools and techniques”.

This making sense will be increasingly important in a fourth industrial revolution that H K Brar proposes will be powered by a third (electrified) data rail (link). She notes:

The data tracks the have been laid in the previous wave of digitization (Third Industrial Revolution) still remain, but the Fourth Industrial Revolution will be electrified and truly come to fruition by the proverbial ‘third rail’: turning data into a powerful commodity — as powerful as steam, electricity, coal, and oil in the traditional economy.

In the choreography and performance of dancing with data, HK suggests we address six stages of data lifecycles:

  • Creation, Capture
  • Storage, Organization, Processing
  • Use, Analysis
  • Publishing, Sharing
  • Back-Up, Maintenance
  • Destruction or Re-Use

HK concludes her discussion with these questions:

  • Is the data valuable?
  • Is there a willingness-to-pay for the value extracted from the data?
  • Is the data rare, precious, niche, or expensive to obtain?
  • Is there an intrinsic value that is extracted by ensuring the data’s wholeness or accuracy?
  • Is there a requirement for data permanency?
  • Is real-time access to data important?
  • Is there a requirement to check and validate the data, and by whom?
  • Is sharing of data amongst business ecosystem participants important?
  • Is there a requirement of how data can be creation, and by whom?

I think these kinds of questions are an excellent way of starting whole of organisation conversations about the dancing we will do together with data … that are being created at a present rate of nearing 3 quintillion bytes each day (link).

Photo Credit

Dancing in Sofia, Byron Stumman on Unsplash

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.