If we treated careers more like dating, nobody would settle down so quickly.
David Epstein
Range: Why Generalists Triumph in a Specialized World
I consistently receive the same questions from people seeking an Analytics career: What classes should I take? What certifications should I get? Should I learn SQL, Python or R?
Behind those questions there’s a consistent assumption: “There must be a clear path to an analytics career.”
I’m here to challenge that assumption. There isn’t one clear path to work in analytics – most of us got there through a winding, wandering series of career moves. My story is one of many – ask someone in Analytics and you’ll hear something similar.
Typical Wandering Path
(1) Get a college degree or other training – not super relevant
(2) Work for a while in some job non-analytics related
(3) Recognize interest in analytics
(4) Start doing basic analytics at work (ideally) or on own time
(5) Leverage that experience into first analytics job
I’ll call out each step as it happened in my career journey.
My Wandering Path
Initial Career (Years 1-4)
Coming out of college I shared the assumption that careers were linear. After all, life to that point was linear, so why wouldn’t careers be the same?
Except, my linear plans fell apart two days before my wedding in 2011. I’d studied International Economic Development (Step 1), interned in Latin America, become fluent in Spanish and was planning to move with my soon-to-be wife to Bolivia. In one phone call and several subsequent conversations, that potential life and career ended. I was sitting in a dead-end job I thought I’d be leaving and had to figure out Plan B.
At first it wasn’t obvious – what else should I do? I was a Customer Success Manager (Step 2) but didn’t really want to do that as a career. I’d worked in sales departments, but didn’t really want to be a salesperson. But then I had an epiphany – there was a part of each of my first few roles that I loved that never was part of my job description.
I was consistently making little analytics & reports (Step 4 – which ironically for me came before step 3!). I’d turn 2,000 customer emails into a digestible summary for the product team. I’d make Salesforce forecasts & dashboards for the executive team. I made a Google Sheet for my Rosetta Stone team to help management track & manage renewal rates for their teams. This stuff was fun! I liked it! (Step 3) But what now?
The Great Filter: Landing First Data Job
Have you heard of the concept of the ‘Great Filter’? It’s part of the Fermi Paradox, which ponders why there is no extraterrestrial life given the seeming high probability it should exist in the universe. Within the Fermi Paradox, it’s the step getting from non-living matter to living matter (abiogenesis). The Great Filter is a catchall for “it’s hard to get past this point.”
I argue there is a Great Filter for those trying to get into Analytics – getting your first job. In fact, I’m devoting my next blog to this topic, so consider this a lead in to next week.
Passing Through the Great Filter
I realized I had an uphill climb ahead. Perhaps this is where many of you are – how do you get a company to take a chance on you?
I asked lots of current analysts via informational interviews at local coffee shops. They all said “I got here via a pretty random series of events.” Sound familiar?
They gave me a breadcrumb trail, though: “You have to get enough experience together and communicate about it well enough to get someone to try you out.” Easier said than done, but I did have some experience already in my current role.
I applied everywhere. I was told by recruiters/HR multiple times “Hey, I guess you could be an analyst but I think your future is in sales based on your resume.” Leads fizzled out until one day I got a call.
The Meeting That Changed Everything
“Can you show up at the office in an hour? The CFO and SVP of Sales want to talk.”
I got that call from Jacob — the same Jacob here at DataDuel. He was working at Funko, a quickly-growing collectibles company north of Seattle. They didn’t have a position open yet, but there was interest in getting analytics going. Before going into the meeting, here is all I knew:
- The position was planned to be part-time Analytics and part-time something else until analytics skills were proven
- The position was planned to be a contract position (not great – I’d just bought my first house and wasn’t looking for a contract spot)
- There would be minimal support since no data team existed, so a self-starter attitude was needed
Given those three bullet points, I had three goals going in:
- Communicate my potential to be great at analytics if given the chance
- Sell them that I was worth a shot as an employee & not a contractor
- Demonstrate self-starter attitude to analytics from previous roles
I quickly threw on a dress shirt, re-learned how to tie a tie on YouTube and flew out to the car.
The rest is history and full of the content I’ll fill this weekly series with. The conversation went really well and they decided to take a chance on me a couple weeks later (Step 5!). While I got a full-time position, I took a 10% pay cut because I needed to prove myself. I knew the temporary sacrifice would be worth it – I just needed my first position to get past the Great Filter.
In Conclusion
There’s no one path to analytics – there are many. I’ve used my path as an anecdote for the infinite options out there.
The general path, though, is to start doing some analytics in any fashion you can, and leverage that experience to get your first position. It isn’t easy – there’s a Great Filter out there which prevents many from getting in.
Come back next week and I’ll dive into the Analytics Great Filter in more detail, and provide some practical options of how to overcome it.