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:
- Topic. Addresses a domain question or domain challenge in an application domain specific to a data set.
- Aim. Data-driven answers to some domain question.
- Audience. Decision-makers or domain experts interested in ‘evidence’ to inform decision-making.
Franz suggest five principles that inform good reporting:
- Correctness and veracity
- Clarity in writing
- Reproducibility and transparency
- Method and process
- 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.