Reflecting on growth mindset
Why fostering a growth mindset is critical to building effective data-driven cultures
This post is a continuation of my Learning from Metrics Review series for data leaders, where I discuss using signal from Metrics Reviews to drive your organization's data culture and data-driven decision-making forward.
The first post is here, and will be updated with links out to all other posts as they are published. Be sure to subscribe to stay up to date with future installments!
When a review meeting is on the horizon, I like to check in with presenters -- especially if it's been a while since I've chatted with them. It can go a long way to fostering data curiosity, and can help catch obvious questions ahead of time.
One time I was doing my rounds, and I came across a Product Manager who was visibly distraught, working in Paint with a screenshot from one of the product's metric dashboards. I asked what was wrong, and if there was something I could help with. The response:
I don't know what to do. No matter how I stretch or squish this graph, I can't make it look like it's going up and to the right.
I'll admit, this one caught me off guard. I wasn't immediately sure how to respond! While the PM definitely needed some coaching, it turns out that was only part of the way forward. To explain, I need to first set some context around growth mindset.
What is growth mindset?
The term growth mindset was coined by Dr. Carol Dweck in her book Mindset: The New Psychology of Success. It's one of two mindsets she presents in the book, that shape how we show up, and respond, to challenging situations. A fixed mindset stems from a belief that one's intelligence and abilities are fixed, or set in stone — you're either born a genius or you're not. In contrast, a growth mindset stems from a belief that one's intelligence and abilities can be cultivated and grown through effort, dedication, and deliberate practice.
Throughout the book, Dr. Dweck highlights how these mindsets manifest in all facets of life. For example, someone with a fixed mindset tends to avoid challenges, and ignore or shy away from negative feedback. Someone with a growth mindset, on the other hand, embraces challenges and the opportunity to learn from negative feedback.
So we're starting to see some reflection of the example above — the PM in question is operating from a fixed mindset, and it was leading to some suboptimal decisions around data sharing. But Dr. Dweck has more to share around mindset in professional contexts, that can paint a more vivid picture for this example.
It turns out, organizations can also have mindsets that manifest through their culture. A fixed organizational mindset ("culture of geniuses") treats employees based on perceived, intrinsic abilities, whereas a growth organizational mindset ("culture of development") focuses on supporting employee growth through focused effort, support, and mentorship.
Dr. Dweck's research finds that an organization with a fixed mindset begets employees with fixed mindsets, and similarly for growth mindset. The findings around fixed vs. growth organizational mindsets are a fascinating read. One compelling finding: Employees in a fixed mindset company are more likely to agree to the statement, "In this company, people often hide information and keep secrets."
Again, we see the PM's actions reflected, but now the bigger picture starts to emerge. While there is room for development on the PM's part, the broader culture was contributing to this moment as well. It turns out, the PM's product was in a tenuous position, and the leadership team was keen to portray that everything was on track and under control. This brought a level of tension to review meetings: Negative data points were rejected outright, and met with a multitude of follow-ups to make sure the data was “properly understood” before it would be accepted. No wonder the PM was reluctant to bring bad news to the meeting — it likely meant a mountain of extra work with little to no payoff.
Growth mindset and data-driven decision-making
While this case may feel extreme, it's one example of a common pattern I've observed when business leaders struggle in their quest to be data-driven: They conflate being data-driven with being certain. When they feel uncertain, they defer action and ask for more data.1
Any business of at least moderate complexity will have a never-ending supply of questions that could be answered. But answering all of them is an unreasonable goal; and even if it were feasible, it wouldn’t create absolute certainty. Findings will always center on trends, probabilities, and likelihoods. Being data-driven means accepting that our knowledge will grow and evolve over time, and simultaneously using what's presently known to move the business forward.
This aligns with Dr. Dweck's findings around growth mindset within organizations. In particular, she finds "Employees in growth-mindset companies... say their organization supports (reasonable) risk-taking, innovation, and creativity" (emphasis mine). Making decisions as our knowledge is evolving is a risk. It's the data leader's responsibility to make that risk as reasonable as possible. It's the business leader's responsibility to lean into the risk that remains and make a decision.2
Hopefully this growth mindset overview has you thinking about where it overlaps with the data culture you want to build in your organization. I've only just scratched the surface. Next week, I'll share a simple test anyone can do to assess whether your data culture is centered on a growth mindset, or a fixed mindset, along with some tips if it's the latter. If you haven't subscribed yet, you can sign up to receive that update in your inbox!
Post photo by Jeremy Bishop on Unsplash Nature | Growth
I enjoy this article from Katie Bauer, where she coins the initialism SLOTH to describe this kind of business leader, and discusses how data leaders can still make the most of their partnership.
A great resource for business leaders is Annie Duke's Thinking in Bets: Making smarter decisions when you don't have all the facts. Making decisions with incomplete data is a skill, and as with all skills, it needs practice, reflection, and a trusted support system to improve.