My son, Sam, has just written a post about systems and networks (link). I found the post really interesting in a paternal sense and an epistemological sense.
The paternal part of me is delighted to read a blog post by Sam and to learn about his observations and reflections as a member of the #INF537 (link) Masters of Education (Knowledge Networks and Digital Innovation) online at Charles Sturt University.
The epistemological delight is in my commitment to self organising networks hinted at in Sam’s post. I have written a lot about networks (link) and have been thinking about these issues a great deal since the distributed, open course CCK08 (link), and becoming an accidental connectivist (link).
I am keen to persuade Sam privately and publicly to explore self organising networks (link) and to read more about Stephen Downes’ (link) and Alan Levine’s (link) work. I appreciate Sam’s particular working environment constraints (systemic) but am determined to explore the action possibilities he can address as a community driver and facilitate network flourishing within those constraints (link).
I sense that with energy anything is possible even in constrained contexts.
Back in 2013, David Brooks wrote in the New York Times (link):
If you asked me to describe the rising philosophy of the day, I’d say it is data-ism. We now have the ability to gather huge amounts of data. This ability seems to carry with it certain cultural assumptions — that everything that can be measured should be measured; that data is a transparent and reliable lens that allows us to filter out emotionalism and ideology; that data will help us do remarkable things — like foretell the future.
David’s work appeared in an article by Oleksii Kharkovyna (link) in which he looked at ‘dataism’. In the article, Oleskii observed “dataism began as a neutral scientific theory but is now mutating into a religion that claims to determine right and wrong”.
Oleskii’s post led me to look more carefully at some data ideas. I have created a Citationsy list of my reading (link). The readings encouraged me to think about the volume, velocity and variety of data and how organisations, particularly in sport, are dealing with this.
Jim Harris makes some very important points in his 2012 post. I paid particular attention to:
Our organizations have been compulsively hoarding data for a long time. And with silos replicating data as well as new data, and new types of data being created and stored on a daily basis, managing all of the data is not only becoming impractical, but because we are too busy with the activity of trying to manage all of it, we are hoarding countless bytes of data without evaluating data usage, gathering data requirements, or planning for data archival
We do need to contemplate gathering, evaluating and storing as we become awash in data. David Slemen has discussed this in his look at the role of Director of Strategy and Analytics in football (link).
The first Ashes Test was completed at Edgebaston in Birmingham on 5 August (link). Australia won on the final day of the test by 251 runs.
Australia scored 487 runs in their second innings. England required 398 runs to win the game on the final day.
Throughout this cricket summer in England, I have wondered if we can predict the outcome of games early in their play after a team has set a target in an innings.
In this test match, I used Australia’s second innings total as a guide to what England needed to do to bat through the final day. I made the assumption that each partnership for England needed to be 49 runs. I was mindful that England was unlikely to score 496 runs in the day but I did have this linear relationship as a check:
The actual profile on Day 5 was:
These data left me thinking about training and competition and how both teams might prepare for the second test at the Lord’s Cricket Ground in a week’s time.
More generally, the result encouraged me to think about the importance of winning first in a series or a tournament.