The Looking Glass: Back on the Saddle
How to think of our careers during layoffs + why the promise of data is better than its reality.
Dear readers, has it been a year, a WHOLE YEAR, since my last post?!
I’m rusty. I’m a stranger returning to a changed hometown, the tap-tapping of the keyboard like music from a new era, the sentences awkward on the bright blank screen. Substack itself has new storefronts now; its mall has recommended some of you to me from other shops (hi! welcome!)
My writing has been in little fragments here and there. I’ve spruced up a pied-à-terre on LinkedIn (something I once thought I’d never do), but hey, it’s cozy; less intimidating than running a country manor. I’ve written novels of notes in the confines of our internal company Slack. But now I’m ready to return and dust off this place.
What do I want to write about? Life itself is my writing lifesource, and as I’ve been immersed in the start-up journey and the world of data — whelp, that’s my well. :) In particular I’m looking forward to exploring the nitty gritty of a question that’s been on my mind for at least a decade, but has sharpened into deeper focus over the past 2 years — what does it mean to use data well to build better things?
In the meantime, send me your curiosities about start-up life. What do you want to know? Maybe the responses will be the bones of a future book (The Making of a Founder?). But first, I still have a lot of ‘making’ to do.
Careers are Long
With all the layoffs in the tech industry right now (my Meta family is especially top of mind), the natural thing to focus on are the losses:
Loss of a job
Loss of a title
Loss of income
Loss of certainty
Loss of a shared mission
Loss of cherished colleagues
Loss of a dream, purpose or identity
These losses hurt, like a freshly gaping wound.
But remember the hero’s journey: in the long arc of your life, your career is defined by your skills and how you’ve used them.
Your career is NOT: your company, your level, how much money you make, your title, whether you were included in some prestigious group (a company or team, an exclusive conference, a list of N under N, an award recipient, etc.)
Each role you take is a chapter in the story of your career. Sometimes, that chapter ends darkly and unexpectedly, yanking control from your grip. Like in every superhero movie you know, the main character loses something precious.
In those moments, remember that you still get to choose what you take away — the lessons, the relationships, how you walk this valley of loss to emerge stronger, more sure of who you are.
And you choose as well what your next chapter is. Remember that the world needs you, and your skills and heart. All around us, lives will be made better with your light.
Some years ago, I wrote in “How to Think About Your Career” the following paragraph, which I still strongly believe in today:
“Even if your current company has a broken promotion system, even if your company collapses tomorrow due to the winds of ill fortune, even if every external measure you hold yourself to — title, salary, affiliation, awards — goes out the window, your skills are forever.
Nobody can take those away from you. No matter where you journey, your skills and your past experiences go along for the ride. This is why you shouldn’t worry too much if your career doesn’t follow some up-and-up external ladder structure…
Careers are long, so invest into them where it counts.”
Manifesto for the Data-Informed
What does it mean to use data well?
Data has been extolled as the vanquisher of uncertainty, the harbinger of a robotic future, a necessity in everything from product management to engineering, sales to design.
Yet many of us who have tried to use data for better decision-making have experienced a different reality in our organizations — one where we are constantly confused by how metrics are defined, bicker over how to interpret analyses, and struggle to apply insights into action.
Using data well is exceedingly hard. It’s easier to talk up the latest technology or wrap ourselves up in the gauze of its promise.
But truly becoming data-informed comes down to internalizing a set of 5 simple yet powerful values. They are….