20 Years of Data, 10 Conclusions
I got my first “real” job in 1999, working for a power company in Copenhagen (shout out to Lars) creating electricity pricing reports in Excel. Since then I’ve worked for small companies, start-ups and large companies across a range of industries. I’ve worked with passionate founders as well as hired guns and I’ve sat at both sides of the table (in-house and consulting).
I celebrated my 20 year “professional anniversary” reflecting and writing down the first ten things that came to mind.
→ There’s no crazy data science bubble going on. What’s crazy is this: 15 years ago it was common for large companies to have only a handful of data scientists (or “data miners” as we used to say), yet hundreds of marketing people (nothing against marketers!). Today, having 100 data scientists on staff is not considered an outlier.
→ Real hustle motivates and inspires. Fake hustle does the exact opposite.
→ Being busy isn’t a goal or an accomplishment. Getting the right stuff done is. Not letting yourself get overwhelmed is. Finding balance is.
→ The workplace is a microcosm where small issues get magnified and blown out of proportion. As a leader it is your job to put things into perspective — not fuel the fire.
→ Although it can feel bureaucratic, process is really important. Sprints and OKRs generate focus and transparency. But don’t forget that the goal of a process is not the process itself.
→ Start-ups are more fun. Building from scratch without endless management of constituents is one of the most rewarding professional experiences I’ve had.
→ For consumer companies, funnel conversion will decline over time and customer acquisition cost will go up. It’s gravity. It’s expected. The key is to create a counter strategy early so you avoid thrashing when it (inevitably) happens.
→ If you’re building a team, hiring must be the first priority — all the time. No meeting, code, analysis or slide deck is more important than getting key talent on your team. Play the long game.
→ No matter what your role is — technical or non-technical — learn how to communicate. If you can’t communicate your ideas effectively, you won’t realize your full potential. It’s that simple.
→ The business world has gone from being skeptical of data science (see my first point) to embracing it full on. But let’s not completely lose the skepticism. The recent surge in AI snake-oil is a good example — putting the AI label on a flier does not automatically make the product useful or credible.
