While a ‘narrative’ attached to data analysis can be powerful and persuasive, it can also turn into a runaway freight train that can be counterproductive. This article resonated strongly with me, and considers both sides of the argument on when to use a storyline to get across results. While it covers the analytics field as a broad whole, I see it as being especially relevant in the context of people analytics.
In short, it considers storylines and narratives that provide an intuitive explanation of the data. These can be critical to understanding, providing a memorable mental model to a client or other information consumer that drives home the point of the analysis.
While this phenomenon is broadly understood, the article also covers the other side of the equation: the idea that a convincing storyline may be very difficult to let go of, even if the analysis subsequently proves it to be incorrect. Crucially, this risk is not limited to the analyst: the audience or intermediate managers are also at risk of fixating too early on a narrative.
A Particular Vulnerability for People Analytics
To bring the conversation to people analytics specifically: I am especially wary of the danger of premature narratives in this space. There are two characteristics of people analytics that I see as increasing the risk.
- The nature of the data: Given the challenges around collection and interpretation, people analytics datasets are likely to be subject to even more caveats and limitations than usual. Due to both these limitations and the inherent complexity of the data, analyses will rarely neatly resolve to a simple, clean, interpretable solution. Analysts therefore tasked with connecting a lot of different dots, which will typically require a clean narrative to be created.
- Intuitive conclusions: I see people analytics being more at risk of bias driven by intuitions. In some fields, analysts are unlikely to have clear hypotheses about what might be driving the changes (e.g., handwritten digit recognition). Conversely, often people analytics deals with very human issues and challenges, that both the analyst and the audience will likely have some experience of. The temptation to use personal anecdotal evidence to construct a unifying narrative Is extremely strong, even compared to other data analytics fields.
These factors put together make this risk especially notable in people analytics. The possible risks clearly do not obviate the need for storylines and narratives to drive impact and change. However, I believe it is a tool that should be used with caution, and without undue haste.
Implications for Consulting and the “10%” Answer
One of the reasons this article hit home for me was because of my pre-HBS experience in consulting. One of the hallmarks of consulting is an evolving storyline: a Day 1 theory of the case that, at least in theory, evolves over time with new data and analysis. While this can be a valuable tool to orient clients around, it is also possible for internal or external stakeholders to fixate on a particular narrative, irrespective of what new information might show.
In my experience, this risk holds double for analytics-centric cases. Given the norms of the industry, clients often seek a “10%” answer very close to the start of the project, when the analyst may still be early stages of the extract/transform/load and data validation process. Despite the push to deliver something, the preferable approach may be to avoid wrapping extremely early data analysis in a convenient narrative.
The article provides a cogent example of this, where the author provided a clear and compelling narrative that generated a huge amount of interest and passed untouched through multiple inspections. Ultimately, it turned out to be based on entirely false assumptions. While this happened in a harmless weekend long hackathon, care should be taken to avoid this problem in higher stakes arenas.