Data and coherent narratives

Peter Killeen (2018), in a paper that discusses the futures of experimental analysis of behavior, observes “we must learn that data have little value until embedded in a coherent narrative”.

The construction of this narrative has been a hot topic this week in conversations about data science activities.

One example is Evan Hansleigh’s discussion of sharing data used in Economist articles:

Releasing data can give our readers extra confidence in our work, and allows researchers and other journalists to check — and to build upon — our work. So we’re looking to change this, and publish more of our data on GitHub.

He adds:

Years ago, “data” generally meant a table in Excel, or possibly even a line or bar chart to trace in a graphics program. Today, data often take the form of large CSV files, and we frequently do analysis, transformation, and plotting in R or Python to produce our stories. We assemble more data ourselves, by compiling publicly available datasets or scraping data from websites, than we used to. We are also making more use of statistical modelling. All this means we have a lot more data that we can share — and a lot more data worth sharing.

Evan’s article concludes:

We plan to publish more of our data on GitHub in the coming months—and, where it’s appropriate, the analysis and code behind them as well. We look forward to seeing how our readers use and build upon the data reporting we do.

The availability of such shared resources, in Uzma Barlaskar’s terms, will enable us to be data-informed rather than data-driven. Uzma suggests:

In data driven decision making, data is at the center of the decision making. It’s the primary (and sometimes, the only) input. You rely on data alone to decide the best path forward. In data informed decision making, data is a key input among many other variables. You use the data to build a deeper understanding of what value you are providing to your users. (Original emphases)

Alejandro Díaz, Kayvaun Rowshankish, and Tamim Saleh share insights from McKinsey research on the use of artificial intelligence in business and note “the emergence of data analytics as an omnipresent reality of modern organizational life” and the consideration that might be given to “a healthy data culture”.

Alejandro, Kayvaun and Tamim suggest that such a culture:

  • Is a decision culture
  • Has ongoing commitment to and conversations about data initiatives
  • Stimulates bottom up demand for data
  • Manages risk as a ‘smart accelerator’ for analytics processes
  • Supports change agents
  • Balances recruitment of specialists with retention of existing staff

Chris Lidner has looked at the profiles of data scientists that become part of an organisational data culture. He reports “data scientists come from a wide variety of fields of study, levels of education, and prior jobs”. They have a range of job descriptions too: data engineer, data analyst, software engineer, machine learning engineer, and data scientist.

The combination of these posts sent me back to re-read Chris Moran’s What Makes a Good Metric? published in August. I think Chris helps us think about our data narratives in the context of “audience, metrics, culture, and journalism”. He points us to Deepnews.ai Project as an example of valuing the impact of journalism to the information ecosystem.

This leads Chris to identify the characteristics of robust metrics that help us understand quality and impact:

  • Relevant
  • Measurable
  • Actionable
  • Reliable
  • Readable

He reminded us also that we should be conscious of Goodhart’s Law: any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.

As a result of reflecting on these aggregated ideas and discussions, I returned to this diagram presented by Hadley Wickham and Garrett Grolmund‘s data exploration visualisation:

I wondered how this process might change if we start, as Peter Killeen suggested, with an awareness of how we might embed our narrative for a range of audiences in data intensive contexts.

Photo Credits

Basketball photo by William Topa on Unsplash
Person holding four photos photo by Josh Hild on Unsplash

Coaching Ideas

Two fragments came together yesterday and sent me off thinking about coaching.

The first came in an email message from Jo Gibson. She is writing up her PhD at the moment and we have been discussing narrative forms. In her email, Jo shared a description of a short story as:

something glimpsed from the corner of the eye, in passing. An illuminated moment … a glimpse of truth, about which you have forgotten to ask.

When I read that I thought that it was a powerful description of coaches’ experiences as they try to extend their practice. I particularly like the “forgotten to ask” part.

In my own coaching, the forgotten parts emerge through reflection and become part of the next short story, sometimes made explicit, but often left unsaid, embedded in the guided discovery I have planned.

The second fragment also came in the form an email. A friend had seen the first episode of Monty Don’s Paradise Gardens programs. In that program, Monty visits Isfahan, Kashnan, Shiraz and Pasargadae in Iran. There is archeological evidence of a garden at the heart of Cyrus’s 6th century palace at Pasargadae. The program note observes:

When the Arabs invaded Persia in the 6th century, it was these Zoroastrian gardens that influenced their ideas not only of what a garden should be, but of paradise itself.

What struck me about this was that our gardens today are connected to this garden. Our practices have their roots (literally and metaphorically) in Persia.

These two fragments came together in my thinking about how we learn to be coaches and develop our own sense of coaching.

In our coaching, I believe we glimpse the coaching of others who preceded us. On some days, the way a coaching session evolves gives us a taste of ‘paradise’ … in Iranian, a word that describes an enclosed space.

At such moments, our coaching is connected with the ideas that have been explored in other places and are realised in our own design.

Photo Credits

Grenada in 2D (Alexander Savin, CC BY-NC 2.0)

Wrestler and his coach (Michael Heiniger, CC BY-NC-SA 2.0)

A four decade journey in performance analysis and analytics

The end of a calendar year is a good time to reflect on learning journeys. This December, I have been thinking back over four decades.

My fascination with performance analysis and analytics started with my role as a teacher of physical education and as a young coach in the 1970s. In 1977, I started to take responsibility for coaching club rugby union. My role models were Tony Gray, Jim Greenwood, Ray Williams and John Dawes. I think my roots in applied performance analysis were set then. Thereafter, whatever work I did in analysis was focussed on supporting coaches and athletes.

A decade later, in 1987, I was starting the write up of my part-time PhD at the University of Surrey. I had spent three years observing the teaching of physical education in two schools and was immersed in the ethnographic literature. My supervisor introduced me to the work of Miller Mair and from that time I have been keen to explore performance analysis and analytics as storytelling and story sharing.

These are the kinds of things I learned from Miller:

Our worlds are structured in metaphor and images. We can only tell stories from conjured images of what we suppose we are and what we suppose we know, within the language and assumptions of our place and time.

Every telling (whether in psychotherapy, science, the market place or the lovers bed) is a composition with personal intentions. Every telling is partial, suffused with personal interest.

Every telling has to be in some manner and style. Even when we seek to be plain and blunt we are using stylistic devices for signifying plainness and bluntness.

Science has tried to be ‘the story to end all stories’, or a story trying its hardest not to seem like a story at all, but the way things are. Every group has its own sanctioned ways of telling for different purposes and contexts, its ways of listening, ways of evaluating. The ‘hard’ approaches to science have their own ways of telling set up in such a way as to seem and claim to be above and separate from mere telling, beyond any contamination in the telling itself.

But stories are partial and political. We all have vested interests in our psychological and other tellings.

My thesis ended up being a collection of stories. Two of them are:

Do people who have lost their voice have to do it?

Anush and basketball fever

I had started doing some hand notations of rugby and lacrosse whilst I was at St Mary’s College at Strawberry Hill (1978-1986) and had been using VHS video recordings of performance. By the time my PhD was submitted, I had written a book about the use of video in sport.

The next decade took me into the digital era but strongly connected to storytelling (Are we all performance analysts? (1998)). I was fortunate to spend this decade at the Cardiff Institute of Higher Education (later UWIC and subsequently Cardiff Met).

I was a guest at the Sports Coach conference in Melbourne in November 1998.

I took with me  one of the first digital cameras and an early example of a portable analysis system that had been developed by Tony Kirkbride. My presentation to the conference is archived on Slideshare.

These are two of slides I shared at the conference:

and

I provided an example of my own use of digital stills in coaching as I tried out the new technology.

A decade later, I was at the Australian Institute of Sport (AIS) in Canberra. I rode the wave of digital technologies and was part of a decade that saw the full-fledged software as a service era and the emergence of a video repository for online and near line access on demand. In 2005, I had the opportunity to develop a proposal for funding for three analytics positions. We were also using Australia’s supercomputers and high speed infrastructure to share large amounts of swimming video and data (up to 1tb). By 2007, we had started a data analytics project for the Beijing Olympics that contributed to a gold medal performance and had developed a stable machine learning approach to support cricket coaches in the Ashes Series.

The opportunities I had at the AIS gave me wonderful freedom to explore and champion disruption in the support an institute could offer to the daily training and competition environments.  I was fortunate that my line managers were able to see beyond the narrow limits of videography and coding and embrace a digital age that celebrated knowledge discovery in databases.

Much of my work in the decade following the AIS innovations has been spent exploring open access sharing. In 2017, my focus has been to use the plethora of online education platforms to continue my own learning and to share resources with others in a world that now sees the sharing, aggregating and curating of digital artifacts as a normal activity.

My most recent learning experiences have been with R and ggplot 2.

Reflecting on this four decade journey from analogue to digital experiences has been a fascinating experience. It was helped by a presentation I gave in Ireland earlier this year, Are We There Yet?

What excites me is how present day researchers are using technologies and creating innovative communities of practice that are accelerating our understanding of performance.

When I started my learning journey in the 1970s, it was possible to aspire to be a polymath in performance analysis and analytics. Four decades on the flourishing of all forms of enquiry make that much more difficult.

I am relishing the next decade and am wondering what my experience will be when I look back from 2027.

I am hopeful it will be more like:

than

Photo Credits

Interesting sidelines (scsmith4, CC BY-NC-ND 2.0)

Miller Mair (Constructivist Psychology)

2008 McLaren Park CX Tilt Shift (Steven Woo, CC BY-NC-SA 2.0)

Geen hulp voor Giusto Cerutti (Nationaal Archief, no known copyright restrictions)