Writing a report

Earlier this week, Avinash Kaushik wrote about Responses to Negative Data (link). Shortly after his post was published, I found a link to a Turing Institute blog post, written by Franz Kiraly, What is a data scientific report? (link).

Both posts have helped me to think about the why, what and how of sharing observations, analyses and insights.

Franz, the author of the Turing blog post suggest that a stylised data report is characterised by:

  1. Topic. Addresses a domain question or domain challenge in an application domain specific to a data set.
  2. Aim. Data-driven answers to some domain question.
  3. Audience. Decision-makers or domain experts interested in ‘evidence’ to inform decision-making.

Franz suggest five principles that inform good reporting:

  1. Correctness and veracity
  2. Clarity in writing
  3. Reproducibility and transparency
  4. Method and process
  5. Application and context

Whilst there are some issues I take with Avinash’s and Franz’s posts, I do think they both raise some fundamental issues for us as we contemplate sharing our data-informed stories. I am particularly interested in how the curiosity and openness Avinash describes meets Franz’s five principles.

As I was concluding this post, up popped a link to Samuel Flender’s post How to be less wrong (link). This will be an excellent companion to the two posts discussed here. It also gives me an opportunity to extend my interest in Bayesian perspectives.

Photo Credit

Photo by Sandis Helvigs on Unsplash

In plain sight

white open book on brown wooden table in front of clear glass window in dim room

The serendipity of finding Thomas Grisold and Alexander Kaiser’s (2017) paper (link), whilst looking for recent discussions about feedforward (link) prompted me to think about personal learning journeys.

Thomas and Alexander ask ‘How can unlearning initiate a deep learning process leading to the best version of our self?‘. The volume and quality of resources available to us makes this a very important question.

Notwithstanding the debate about the concept of ‘unlearning’, two excellent links made me think about my ongoing quest to explore visualisation and a better version of my self as a data sharer and storyteller.

The first was Claus Wilke’s (2018) Fundamentals of Data Visualization (link). His welcome message notes that the book “is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional”.

I found it through an alert to chapter 16 of the book, Visualizing Uncertainty (link). The alert came from Matthew Kay (link) whose own work on uncertainty visualisation has also been nudging me to a better version of my self.

The second resource was Amy Cesal’s Sunlight Foundation Data Visualization Style Guidelines (link). Amy worked with Zander Furnas to develop the guidelines. There is a copy of these guidelines on GitHub (link).

Amy reflects on her use of a style guide:

Since having a style guide, I have to do less work on the majority of data visuals, because they are already 90% done when they are handed off, if they are handed off at all. I also spend less time testing for colorblindness and text readability, because I’m using pre-tested options. This way, I have more time to focus on larger projects that push the boundaries of our style guidelines, and really make the visuals exceptional.


Amy’s mention of boundaries is where my reading of Thomas and Alexander meets Claus and Amy.

Access to such outstanding visualisation resources disturbs a learned aesthetic. Thomas and Alexander note that:

Feedforward self-modelling involves constructing a desirable image of the self that represents achievements beyond the individual’s current capability. It yields the potential for improvement and rapid changes of behaviour”.

Just as I was drafting this post an alert to Cole Nussbaumer Knaflic’s (2018) accessible data viz is better data viz (link) appeared in my in box. Cole observes “Often, when we are creating charts and graphs, we think of ourselves as the ideal user. This is not only a problem because we know more about the data than the target user, but because other users might have a different set of constraints than we do.”

My hope is that from the inspiration of these great resources, I can start a process of deep learning about how to share in plain sight … not as a New Year resolution but as an everyday practice.

Photo Credit

Photo by Ilya Ilford on Unsplash