Continuing our posts on how to achieve success at work, let’s turn to the power of empathy.
To help bring to life how greater empathy could breakthrough barriers for your Data Science projects, I am delighted to welcome back Ryan. Guest blogger Ryan Den Rooijen has tremendous experience from his work leading Data Science teams in Google & Dyson.
So, let’s hear what he has learnt about the need for analysts to have empathy & what this has to do with his outdoor activities…
A lesson from running on camaraderie
I love running. Not only is it a great way of exploring places while travelling, managing stress levels, and in general staying fit. It is also an activity that can engender a certain… conviviality?
When you have been pushing yourself for a good thirty minutes and you pass another runner going flat out, you momentarily sense this primal connection between you two.
While the team-building effects of shared suffering are well known — think of The Crucible — what is interesting about meeting runners out in the wild is that you are of course by no means a team. You are two strangers passing at 25km/h or more. Yet those few seconds are all it takes to communicate a kinship, an acknowledgement that you are, in some way, in it together.
What has this got to do with Analytics?
Many of you will be well acquainted with the adage that you build analytical capabilities by focusing on people, platforms, and processes.
Yet, we know that many data projects fail, despite at first glance the aforementioned boxed being checked. There are many different factors we can identify, such as faulty strategies, erroneous architectures, and inadequate investment. But, perhaps, we should consider another culprit: a lack of empathy.
Whether it is…
- a business leader discounting the rigorous analyses conducted by a data scientist;
- a network engineer stubbornly refusing to consider opening a firewall port for a data engineer;
- a data product manager blithely dismissing the complexity of an enterprise application;
…empathy is sorely lacking in all of these situations.
In each scenario, people are not empathising with the other party. They are not stopping to consider how the other person is feeling, leading to frustration and a lack of progress.
Why empathy is relevant for Analytics & Data Science teams
These disconnects can occur within teams as well, such as in cases where a non-technical manager fails to acknowledge the legitimate complaints of their developers, or a data scientist dismisses a business partner role as a job “anyone can do.”
This lack of empathy can introduce friction at best and cause a stalemate at worst. Instead of growing closer as people solve a problem together, they become more dismissive of the other’s efforts.
How does empathy help in these situations?
The great thing about empathy is that “why should I help address their concerns” tends to become “how can I help address our concerns?” As cliche as it is to talk about “one team, one dream,” those who are able to empathise effectively with another are able to mentally “join teams.”
From this perspective, problem
Empathy binds runners, particularly those who run longer distances, through a familiarity with the curious mix of anaerobic pain and endorphin pleasure.
In a similar manner, spending time to understand stakeholders’ and partners’ roles in enough detail that you can empathise with their hopes and fears, is a worthwhile investment when trying to accomplish a challenging project.
After all, empathy is instinctively recognisable and one of the most powerful ways to build trust. You do not even need to run.
Empathy for you & your team?
Thanks to Ryan for those thoughts. I have to say that I agree with him, from my own experience of leading & developing Analytics teams. I have seen barriers removed and strategic alliances formed through empathy. It can often be the key to overcoming lack of buy-in elsewhere in a business.
Do you agree? If so, what are you going to do differently as a result? How could you help your team take a step towards being more empathic?