What Is the Best Advice You Have Ever Received? - Issue 95
What advice changed or transformed your career? Take some advice from analytics industry leaders.
Hello analysts! I’m back with a special topic for you in this week’s Data Analysis Journal, an advice column about data and product analytics.
If you’re not a paid subscriber, here’s what you missed this month:
Why People Don’t Buy Your Subscriptions - a recap of Appfigures Chat with Jake Mor, Founder & CEO of SuperWall. How to optimize your subscriptions and the onboarding flow, doubling trial-to-paid conversion rates, benchmarks to follow, design concepts to use, experimentation, and more.
What An Analysis of 150+ Onboarding Experiences Reveals - a recap and insights from onboarding research led by ProductLed experts. Read about how to set up analytics, analyze, and work with onboarding flow data.
Top 10 Python Pandas Tips For Data Analysis - where I share my favorite list of Pandas tips and functions that I almost always use when working with Python for data processing, transformations, and analysis.
I just had a birthday this past week, and in order to process my grief about getting older, I decided to make this newsletter a little special. Inspired by a recent article with Deb Liu, a Silicon Valley tech executive, I reached out to my network with one question - what is the best advice you received that changed your life or career? I received many invaluable and insightful responses that resonated with me.
Today I wanted to share a few of them, and I hope it will inspire more readers to learn analytics, get empowered with data, and grow their careers.
To start it off, I’ll share advice that I received some time ago that I often reflect on and want to pass to other analysts:
Many people pick a generalist side and do a little bit of each. The problem with that is it’s difficult to go deep, and as a result, you end up looking like all other analysts. Career management is Economics 101 - supply-demand. How would you put yourself in a situation where you are very low supply, and there is very large demand. And that’s how you get the roles and the job opportunities faster than others do.
If you are stuck writing code, and can’t come up with a solution that works - take a step back and reduce complexity. Break down your problem into smaller sub-problems, and work with each one until it's solved. Once done, combine them into the final solution. Test every iteration of the code to ensure it returns your expected output.
- from my partner, Alexander.
There are many folks out there who work a role without an eye on the
horizon, unsure of what their next step might be. Know what you want,
and tell your manager what you want to do.
- from my ex-manager and coach, Jared Horney.
And here is some advice, stories, and comments that I received from my network and analytics industry leaders:
“Every cutting-edge thing you learned in college will be stale in three years. Hence, take jobs where gaining new knowledge is a key part of being successful.”
Looking back, it has been proven to be true again and again and again. I've done my best in every role to stay close to the real work, and that has forced me to keep learning new skills. The alternative fate is I become yet another director/VP, whose primary success is driven by an ability to suck up or play company politics well.”
Chu-Cheng Hsieh, Chief Data Officer at Etsy, Advisory Board Member @Google. Ex @Amazon, @Intuit:
The best advice is to always ask oneself: "Do you want to be effective or do you want to be right?" This applies to both data and leadership.
Let's start with data.
This advice reminds me to take calculated risks because speed matters in business. Often the cost of having comprehensive data to make the right decision requires lots of work. For example, one can set the p-value threshold at 0.01 but it means that you need to run A/B tests for an extended period of time – sometimes you probably never hit 0.01 because when you extend the experiment period, you have to also consider seasonality, model drifting, etc. And that's why most companies set p=0.05. Even if such a threshold naturally introduces false positive treatment, the choice is much more effective.
Let's talk about leadership. This question can be applied in different contexts. I'm providing one example here. Often we are facing multi-choice options. Say, there's almost no right answer in designing an organization. When I have to introduce an org change, I often favor consulting only key people (senior leaders, my manager, HRBP, etc.) While it's absolutely possible to consult every manager in the org to gather more information, keeping the group small comes with benefits like speed and alignment. It would take months if dozens of managers are involved in the process. On the contrary, it would be a blind decision to make an org decision without considering different perspectives. The question reminds me to seek a compromise between gathering inputs, achieving alignment, and making timely decisions.
Adam Kinney, VP of Analytics at Mixpanel, ex @Twitter, @Google:
The best advice I got was to always ask questions about any request for data, metrics, analysis, etc, until I understand the ultimate decision that the requester is trying to make. All requests like these should ultimately be informing some decision, whether that it is a big one-off decision for the company or an ongoing decision, like metrics that inform a manager on whether their team's execution is on track. When you understand the decisions driving requests, you have an opportunity to figure out how best to inform the decision beyond the specific request and how to formulate it into the format that would be most effective for the decision makers. It also helps with prioritization since the underlying decisions can usually be sorted for importance and urgency better than specific data requests.
The impact of doing this in my career has been that I moved from a tactical bit player with limited impact to someone who is driving strategy and has huge impact. It has also helped me coach my teams on how to have a bigger impact, which is generally a difficult thing for even pretty experienced data analysts to figure out.
For me, the best advice I ever got was to find real problems that I cared about answering.
There are lots of tutorials out there that teach technical skills on toy problems and sample datasets. These problems might teach you a few techniques, but they won't make you a better analyst, because learning to be an analyst requires asking questions, seeing a result, being curious about what that result tells you, asking more questions, and continuing to dig until you uncover something truly interesting and useful. With real data, on real problems that you care about you'll do this naturally; your curiosity will draw you further in. On sample problems, you'll often stop when you get to the answer in the back of the book—which, of course, doesn't exist for most questions you'll want to answer.
Alex Justman, Vice President, Digital Resiliency at First Republic Bank:
Best lesson I got from possibly one of the worst managers I ever had “Never be victimized by your circumstances”
You have to own your situation. If things turn against you you need to figure out how to assess the position you are in, and identify who you need to work with to provide a solution to your customers!
35 years ago when I was in my first job as a high school language teacher (in Latin), my first mentor urged me to learn more about computers and programming because he thought it would become important and getting in on it in the early days would be both interesting and good for my career. He was more right that I think he could imagine, since that start everything for me.
The second advice was from my boss was to accept a posting London to help start a new company about 10 years ago. While there were various hassles for someone older to take a position like that, for me it was a great opportunity to expand my network and diversify my skills, which was valuable in later positions.
Matt Brattin, Founder TMB Analytics, ex VP of Analytics @Aplos Software
I have two pieces of advice that came to me at different times which I've never forgotten:
1) Before I got started, I asked an alumni of my graduate program something he wished he'd known before getting started and he said to me "there is no such thing as perfect data". It didn't mean a lot to me at the time, but definitely as I got out into the wild it made much more sense in that the best you can do is understand your data and "respect your data", so that you can handle it with care knowing there is always going to be some nuance to it.
2) Immediately after getting promoted to my first manager role I was told by the head of my vertical "people are unpredictable". This was meant in a way to prepare me for people management and knowing that you can't know everything about the way people work all the time, especially when the team gets large. What I took from this, though, is the importance of communication and creating an environment for openness where you can reduce the unpredictability, but you can never eliminate it fully.
Thanks for reading, and thank you to everyone who shared their learnings. Until next Wednesday!