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Gulrez Khan spent nearly two decades inside two of the most
data-intensive companies on the planet — Microsoft and PayPal.
Built experimentation infrastructure running on tens of millions
of users. Drove a 35% increase in Gen AI adoption across PayPal's
developer community. And then he sat down and wrote a children's
book.
Not because the work got easier. Because he realized something
most data professionals never fully accept: the hardest part of
this job was never the math.
In the current episode of Masters of Data, BI & AI, he talks
about why he wrote Drawing Data with Kids, what almost two
decades at Microsoft and PayPal taught him about the gap between
insight and action — and the five-part framework he now teaches
for turning data into something people actually remember.
A few things that stuck with me:
On why the book had to exist: Gulrez grew up loving to write, but
"get a job first" won out over creative ambition. Years later,
homeschooling his three kids with his wife, he realized there was
no book that taught data literacy the way he wished someone had
taught him. So he wrote it — real memories with his own kids,
turned into realistic fiction. The Snoring Graph chapter, based
on his own sleep apnea, is now the one kids remember longest.
On the moment that made it real: An article he wrote for the Data
Visualization Society, "Drawing Data With Kids," unexpectedly
took off. That was the signal — not just him, but other people
wanted this too. The Kickstarter and the book followed from
there.
On the story behind his professional thesis: His father, in
India, would text him in Hindi typed in English letters —
autocorrect would mangle the words, and every time, his father
would apologize below the message: "I'm 76 years old, please
forgive me, I don't know how to use these devices." After his
father passed away this past December, Gulrez ran an analysis on
thousands of PayPal customer feedback entries and found the same
pattern from elderly users again and again — people quietly
explaining their age as an apology for not keeping up. That
discovery became one of his most-shared internal stories,
reaching even the CTO.
On the five-part storytelling framework: Start with a personal
story. Build common ground. Use contrast. Lead with empathy, not
sympathy. Close with a call to action, built together with the
people who actually own the outcome. Not a "rule of three" —
Gulrez insists you need all five to actually move a room.
On what AI can't replace: Gulrez's answer isn't storytelling in
the abstract — it's the specific, personal, unrepeatable
experience behind a story. AI can generate a technically
competent narrative. It can't generate his father's WhatsApp
messages, or the particular blend of a Bengali childhood and a
life spent in American tech. That specificity is the actual
differentiator, not "creativity" as a general skill.
On what should never change, even as AI evolves: The basics of
learning how to learn matter more than any specific tool. Ask
better questions about the data you're consuming. Learn to
present with intent, not the default rainbow-colored chart your
tool spits out. Use contrast deliberately — perception versus
reality, side by side.
What do you think — is data storytelling a skill every data
professional needs to build, or should organizations be doing
more of the translation work themselves?
️ The full episode is live now on Masters of Data, BI & AI —
link in the comments.
LinkedIn Profile
LinkedIn Profile of Gulrez Khan:
https://www.linkedin.com/in/gulrezkhan-data/
LinkedIn Profile of Rocky Khan:
https://www.linkedin.com/in/khanrocky/
Business Books
Effective Data Storytelling by Brent Dykes — Gulrez has been
reading it and recommends it directly for anyone serious about
the craft.
How I Built This, hosted by Guy Raz — not a book, a podcast, but
one Gulrez credits for showing him how founders actually talk
about the struggle behind their journeys.
The First 90 Days by Michael Watkins — his pick for anyone
stepping into a new role or company, useful well beyond data
careers.
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