The Art of Data Writing

I still vividly remember my first user research report presentation to my leads crew at Hewlett Packard. It was an exciting moment for me as a researcher.

In the report, I diligently included my SPSS workings, detailing correlations, regressions, and significance levels. I assumed that everyone in the room understood the intricacies of data analysis. I even explained why I chose the Mann-Whitney test instead of the t-Test for measurement . This was right after my grad school, and my enthusiasm was palpable. My geeky eyes were bright and shining. But everyone in the room was scratching their heads ?? After 45 mins, no one understood a thing from the findings. That rainy Singapore afternoon changed my data writing forever ??

I learnt the art of data writing from those mistakes. As designers and product managers, we rely heavily on data insights. Data writing is about conveying data insights in a compelling and accurate manner. It allows us to describe scientific findings and quantitative research data formally and informally, whether in public events or within our specialised teams.

To help you write better data stories that can influence product decisions based on solid data, here are some tips:

1/ Start by organizing the components of your data analysis. Your credibility depends on how you structure the study design, data collection methods, protocols, and data analysis methods. It's important to show how the data was cleaned and analyzed in the appendix.

2/ Share your hypothesis and related assumptions. Provide clear definitions of the tests used and explain them using concepts and empirical calculations. If you considered any alternative approaches, share those as well.

3/ Place the findings of your data analysis in context. Build a compelling argument and acknowledge the limitations of your analysis. Clearly identify where the data cannot be used or generalised. This will establish the context, methods, graphs, and impact of your findings. Guide your audience on how to interpret your findings.

4/ Organize your article into distinct sections: Abstract (providing context, aim, results, methods, and conclusions), Introduction (including motivation and literature review), Observations, Analysis, Discussion, Conclusion, and References. Include an appendix to showcase your data provenance – what data was analyzed and what was excluded.

5/ Enhance your report with data visualizations such as charts and figures. Prepare a one-minute revelation that highlights the central finding. However, a comprehensive discussion on data storytelling using charts is beyond the scope of this document.

6/ Create a storyboard to visually arrange the progression of your data story. A well-structured storyboard seamlessly bridges the knowledge gap between the reader and the writer.

7/ Write a blog version of your report using simple language, condensing it to less than 300 words. Adapt the narrative to fit the context appropriately. Additionally, prepare a 100-word summary of your data story to share with executives – a TL;DR version.

Building great products is challenging, but data can make it easier. Use data credibly and make a lasting impact.

I see a number of data scientists and user researchers, not structuring their data story and as a result they lose credibility. The organisation starts bucketing the research tribe into purists. Whereas, if you just know how to storytell using data, you would help your product immensely. The bridge between a great product decisions and a so-so product decision is your data story. Make it count.

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