Ken Kaczmarek

About Ken Kaczmarek

Hi there, I’m Ken Kaczmarek, one of the co-founders of

Bean counter by trade, data wrangler by fate and homebrewer by necessity. I’ve gotten into trouble all around the world and currently on the lam in Chicago. Don’t tell my kids; they still think I’m on the up and up. A strong believer that anyone can make a good pie filling, but true transcendence can only be found in the crust.

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Integrating Web Services with Microsoft Excel and Google Sheets

November 4, 2019 by Ken Kaczmarek

If you could have any data in your spreadsheet with a single click of the mouse, what would it be?

Data from your CRM system? Data enrichment capabilities from an API? A research list from Twitter? An on-demand, refreshable view of a corporate database? That master item file that your colleague keeps locked up in his own Excel spreadsheet?

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Onboarding and API Service

The "Aha" Moment: How to Onboard an API Service and Get Active Users

February 21, 2018 by Ken Kaczmarek

Since the recent launch of our data feed API, onboarding has been top of mind. There have been gallons of ink spilled over the importance of SaaS onboarding, UserOnboard’s teardowns being one of the best.

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Share Data Without Sharing Credentials: Introducing Pipe-level Permissions

June 16, 2017 by Ken Kaczmarek

Throughout the journey, we’ve often used the phrase: “Data is a Team Sport.”

More often than not data processing requires some type of hand off between an ‘owner’ and a ‘user’. For instance, a business user might need an extract from an IT-supported system. Or a consultant may need a file that is owned by her customer.

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Just Binge-Listened to 95 SaaStr Podcasts, Here's What I Learned

February 6, 2017 by Ken Kaczmarek

Who needs a #drinkwithharry?

Over the past few weeks, I tasked myself with listening to all ~32 hours of the SaaStr podcast, in preparation for the SaaStr Annual conference. Each show is an incredibly dense, 20-minute nugget on the business of SaaS. IMHO, it is totally and completely worthwhile to meander through twice-weekly.

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Prepare your Data. Rinse. Repeat.

March 14, 2016 by Ken Kaczmarek

As everyone knows by now, data is the new black. Conventional wisdom says businesses will either be data-driven or be at a serious competitive disadvantage.

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Improving Data Access: Getting Past Step One

November 4, 2015 by Ken Kaczmarek

In my previous post, I explored commonalities found in any given “data project.” Here we’ll run the first of the five gauntlets: data access.

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Data Prep to Data Pipes: 5 Facets of a Data Project

October 27, 2015 by Ken Kaczmarek

At this point, we’ve all heard the news.

Data is growing at an exponential rate. Data is the new oil. Data is a strategic advantage. Data is big. Data is small. Certainly Goldilocks believes her porridge data is jusssst right.

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The Push and Pull of Enterprise Data Collaboration

April 2, 2015 by Ken Kaczmarek

Like Doctor Dolittle’s famed pushmi-pullyu, enterprise data projects have a serious agility problem.

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Let a Thousand Data Silos Bloom

January 21, 2015 by Ken Kaczmarek

It’s a story as old as time. It’s a story of evolution, a story of freedom. And it’s a story of stuffing a genie back into its bottle.

Yes, I speak of the ancient enterprise battle royale: Productivity vs. Control.

These battles are constantly happening throughout the enterprise, usually with very good intentions. But, like so many good intentions, they often pave the road to, um… employee circumvention. Hey, gotta get stuff done, right?!?

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Of Unicorns and Clydesdales: Data Science at the Departmental Level

November 7, 2014 by Ken Kaczmarek

Much electronic ink has been spilled on the rise of the data scientist.

They’ve been called sexy. They’ve been called unicorns. And, we clearly see a direct correlation between this recent adulation and the uptick in searches for “sexy unicorn”. (At least, I hope that’s the reason).

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Enterprise Data Projects: It Takes a Village

October 13, 2014 by Ken Kaczmarek

I recently found an article discussing the four different types of Data Scientists. Turns out there’s a quite a bit of wiggle in what the term “Data Scientist” might mean – from business savant to data viz wiz to world class coder to Ph.D. in statistics. A question is posed:

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