I’m Lucky

2020 has forced me, like many others, to reflect on my own privilege and why I’ve been able to accomplish what I have.

I want to show gratitude towards the folks who helped propel my career where it is today. I had/have so many things going for me, and these people played a bigger part than most. If you find yourself stuck in your career, maybe you can find some inspiration here from a lesson that someone taught me. Who in your life can play a part like these?

Coach Condon – Making things happen for so many of us.
John, Tristan, Wade, Tom, Emma – Thanks for taking a chance on me!
Konrad, Vicky, Lev, Muness – You have taught me so much.
Richard – Starting our first business together.
Alyson and Sam – Everyone needs family friends like you in their life.
Emilie, Ally, Stephanie, Simon – Thanks for the continuing amazing peer-learning.
Lindsey – Thanks for the love, support, and health!

If you narrow in on the people who took a chance and influenced a step-change in my career arc, it’s sobering how many times there are personal relationships in addition to my own hustle. While I created some of this momentum on my own, I had the confidence to do so because of all of the rest of the times where people like this helped make sure that things worked out for me.

I’m offering free coaching for underrepresented folks in tech

Even if I take for granted that I’m good at what I do, without these people (and so many more) willing to believe in me at various points in my career, I wouldn’t be where I am. I’m certain that there are great people out there who haven’t been given those chances, and that’s why I want to try to pay it forward by making some of my coaching free.

Who helped you get where you are today? Who’s the best person you know who hasn’t been given that chance yet? Should you recommend them to work with me for free?

Appendix – More detailed gratitude – Who does these things for you?

1. Jim (Coach) Condon – there’s likely no businessperson with a bigger impact on my career arc than Coach Condon. I still remember him giving a career day presentation about CyberCash (think some pre-alpha version of PayPal crossed with Bitcoin) and just the general knowledge of business that I picked up spending time at their house. Even as a kid, I had a sense that I might want a career like his one day: rarely a founder, but serial executive. It gave an interesting target for thinking about what I could accomplish and drove me to consider what a path like that might look like. Obviously someone doesn’t just “become an executive” so I had to learn to think backwards from there. Plus, he personally got me my first two amazing jobs in high school, and directly influenced a job or two later than that. I’m confident in saying that without Coach Condon, my career would look completely different.
2. John Bracken – When I decided that I wanted to get back into tech/startups, I knew that I had the skills and passion for it, but getting that first transition job is always a challenge. By encouraging me to start as a side project (he didn’t even know that I was going to suggest that to him!) he gave me the chance to prove it.
3. Konrad Waliszewski – While John gave me my shot at Speek, Konrad showed me the type of leader that I want to be. To put it simply, I know that I’m not alone as a former coworker who hopes to get a chance to work with Konrad again.
4. Vicky Volvovski – I’ve never seen anything like Vicky’s career arc at Zapier. While we bonded over both having to apply 3 times before getting hired at Zapier, it is our working relationship that has taught me so much. Knowing that someone is good enough at “getting stuff done” to go from blog freelancer to the Zapier exec team is an inspiration. Her approach to solving problems and thinking about the business is something that I’ll continue to apply regularly. And if we could all just get stuff done like she can, there’s no limit to what a team can accomplish.
5. Lev Volftsun – I knew from high school how successful of a businessperson Lev was, so I wanted to work for him just to learn how he thought about running a company. Working for him taught me the power of expectations in business: both for yourself, and also for others.
6. Tristan Handy – In Fishtown’s very early days, Tristan was turning down work because his folks were too busy. Dbt was brand new and in one of the only cases I can remember, I was completely uncertain whether or not I could pick up some of that work. He convinced me to take on a short contract stint with them anyway, and said that I’d be fine. I know that the data modeling skills I worked on during my time there influenced my ability to get later jobs.
7. Wade Foster – While I work for Wade now, that wasn’t how my time at Zapier started. Wade took a phone call off of a cold email and stayed in communication over a number of months before we found the right fit for me at Zapier on the data team. It only took 3 deep interview processes! 😂 Even after I was working on the data team, it was not necessarily obvious to create the role for me working for him that he did, and I still remember his quote the day we decided, “I’m still not sure this is going to work, but I’d like to give it a shot.” I hope that choice continues to pay dividends for both of us!
8. Muness Castle – My first Zapier manager, helped ensure that I was able to work on projects that helped me grow and show off the skills I’d need later. Without some of those assignments and opportunities, it’s not certain that I’d have gotten the later roles that I did.
9. Emma Candelier – I wasn’t completely sure how I was going to wrap up my last year at UVA; I was considering a 5 year MA in Math, but I knew I liked business, too. Talking through my options for MS Commerce with Emma set me up exactly where I wanted to be coming out of school.
10. Richard Kim (and his parents) – The best man at my wedding and also my first business partner. Without our tiptoes into tutoring businesses before and after college, I’m not sure when I would have incorporated my first company. His parents’ company was also a success story that guided both of us towards business, but also served as general inspiration for what success can look like.
11. Sam and Alyson Rod – In addition to being our closest family friends and a core part of my personal support system growing up, knowing that they “ran their business from the basement” made owner-entrepreneurship a completely normal part of my childhood. Plus, Alyson embodied the type of adult and parent that I still aspire to be. I wish everyone had someone like her in their life, and I’m so grateful that I do.
12. Emilie Schario, Ally Hendrickson, Stephanie Couzin, Simon Ouderkirk – Haven’t had a chance to work together with you all in the same depth that I have with most of the folks on this list, but after all of our conversations, I come away motivated to do great. We all have high expectations for ourselves and others and I appreciate being able to bounce ideas and learn from all of you!
13. Tom Becher – Data Analytics was still not-well-defined when I first reached out about a job. Thanks for taking a chance on first year college student. My time at MITRE helped be build the core of my technical data analytics skills that continue to serve me well today.
14. Lindsey and Jean – It might be cliche to include my wife and daughter here, but it’s my post so I’m doing it anyway! 😆 Without Lindsey I wouldn’t have learned to reflect to the degree that enables a post like this to happen. If I want to tie it more directly to career, she’s a big part of why I eat healthy and develop healthy habits that continue to allow me to perform at a high level.

The core competency of a data analyst is “Speed to Insight”.

A data team often consists of many people, with many skills, using potentially overlapping techniques. This focus on speed distinguishes this role from data scientists or statisticians. Today I’m focused on answering questions about the business or about how users behave. I’ll refer to these types of questions as mostly in the realm of data analysts, though some orgs call these folks data scientists, too. A good data analyst should be able to interface directly with folks in the business unit that they’re working with. They need to have a solid understanding of business fundamentals in addition to data chops. A junior analyst may rely on business people asking smart questions, and answering the questions that they’re asked, quickly. While this is clearly helpful, it’s not the highest-leverage opportunity for an analyst. The best analysts don’t only answer the questions that they’re asked. Actually doing analysis is often the easy part. It’s other skills that separate an average analyst from the best.

Help teammates rephrase their question.

Ask the “next 3 questions” that they should know

When you’re asked a particular question, it can be tempting to think “sure, I can answer that”. While that might be the first step, it’s important to get at the root reason for the question. If someone asks for the signup conversion rate across a section of your website, it’s the analyst’s job to dig in. Why are you wondering about signup conversion? Would we rather measure conversion to active users? Are you interested in a particular segment? Does the signup rate vary across paid, direct, organic, and social traffic? It’s unusual that a PM wants a metric for the sake of a metric. They’re really trying to learn something about the nature of your product or your audience. It’s your job to know enough about your data sources and about the business itself to answer the next three questions that they didn’t ask. Short circuiting the back and forth will help your team move faster.

Answer faster by proposing a reframed question that’s close enough

Stakeholders may have a rudimentary understanding of what data is available, but they may not understand that a simple alteration of a question might reduce the time to answer from a day to an hour. No data team ever has all of the data that they want, modeled to answer every question. When someone asks a question that would require a new data model or custom work, see if you can reframe their question into one that you can answer quickly with existing data models. This most often to happen when they ask something extremely specific. They might even be asking a better question, but they may not care that much about the specifics.

Which of these 10 customer segments has the highest Lifetime Value (LTV)?

This is an astute question, and likely means that you’re dealing with a data savvy stakeholder. Say that “everyone knows” at your company that these are your top customer personas. But let’s say that you’re a young company and haven’t done segmented LTV calculations yet and have no easy path to getting this answer. You probably have “total dollars collected” easily accessible, though! In that case I would propose this counter, “What about if I give you Total Dollars Collected per user in segment?” Assuming that none of these user groups are systematically newer or older than others, that’s probably close enough! If the stakeholder is looking to prioritize marketing campaigns over the next couple of weeks, they’re not going to want to wait for a new LTV calculation. By reframing the question to one that was close-enough, you unblocked them a week early! That’s leverage.

Reframe to question that they should have been asking

For quite some time at Zapier, we had better data modeling around the number of Zaps that were “turned on” versus the number of Zaps that “had activity”. This lead to a tendency to rank various important KPIs and concepts using zaps-turned-on instead of actual activity. In most cases, this was fine (see reframing to be close enough, above), but once we got large enough to start noticing differences between turn on and activity, the data team made a concerted effort to help folks reframe their questions (and KPIs, and dashboards) in terms of activity! This is a better measurement in most cases, and when folks would ask for a turn-on count, we would ask them if that was really what they wanted.

Build Self-Service Tools (I consider Business Intelligence to live under the Analyst Umbrella at most organizations )

Building V1 is the quickest way to build stakeholder investment in data

The introduction to data driven decision making at many companies will come via an analyst working with the team to turn their goals into measurable outcomes. In the first iteration this often means building a dashboard or similar interface for folks to have access to answer those same first questions without you. Next, you have to build out data structures that are intuitive enough and well documented enough that you can advance to letting people explore the data on their own. Tableau, Looker, and similar tools are designed with the idea of letting savvy business users discover the answers to their own questions. Analysts who empower that will excel. How do we ensure that an analyst isn’t the only person diving into data? Well…

Teaching your team to answer their own questions makes everything go faster

In general, folks aren’t going to be comfortable mucking around in your data without some training. There are definitely good intros to [Looker|Tableau|Etc] out there, but they won’t explain your data sets. Good data teams will create intro material using real data, and actual questions that folks might ask in order to get them more comfortable. Success here doesn’t mean that everyone becomes a power user. Ideally, most people have enough familiarity to look around and feel good. Then, the few folks in various places around the organization that get really good are a massive force multiplier for the data team. At Zapier it’s our head of platform, a senior PM, a person in partner ops, and a writer. As good as an analyst might be at “understanding the business needs”, you’ll never be in it as deep as they are, and for the 80% of questions that are self-serveable, everyone wins here.

Do

Sometimes, your stakeholders are never going to DIY with BI tools. Sometimes, they ask questions that are too difficult to self serve, or require too much nuance. These are the best use of your analytical time. In those cases, you should still be willing and able to help build-it-for-them. The PM and head of platform that I mentioned earlier? They always have the most up-to-date and fastest time-to-insight of any teams in the org because they get so much of the way there themselves. In the best cases, you end up with a new data model that will let those types of questions be easier the next time. Analysts who follow these steps are likely to become the go-to folks on the data team. That’s good for the business, since you’re empowering people around the org. Plus it’s great for your career.

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Anything else that you see good analysts do regularly? Hit me up on twitter @smlevin11.

These opinions are mine, and don’t represent Zapier’s official stance on associate, staff, and senior positions.