Meet a Data Architect - Jer Tippets

"What's fascinating to me about analytics, is understanding the why."

What's a data architect? Or data scientist, for that matter?

Broadly, working with data means taking massive swaths of data and boiling it down to understandable patterns.

More specifically: a data scientist collects and analyzes data. A data architect designs data frameworks — how all that info will be stored and managed and accessed.

Jer Tippets does both.

A world of two parts

Jer's most passionate about efficiency and good design — which could be considered one and the same.

(His favorite font is Helvetica, if that tells you anything.)

"My world is in two parts," Jer says. "One side is about measuring, like behavioral analysis and science modeling on audiences. The other side is about the emotion and what people do, what are customers, viewers, clients—audiences wanting to experience. The art is bringing these two sides together to reach people on the emotional level and give them the experience they want."

That emotional side, along with his love of good design, is actually why he went into data science:

"I wanted to go beyond the subjective. People have lots of opinions about why some design is good—I wanted to prove why something was well designed. Data science lets me do that."

We find efficiencies to find what really matters

“I love efficiencies," Jer says. "I love making things in the most efficient way possible.”

This is perhaps most evident in how he loves to automate everything — especially in his home. (Like, making it so he doesn't have to follow his kids around the house turning off lights behind them.)

This creating efficiencies — this distillation — you might think is pretty unique to data scientists. But it's found in every field of human expression, from poetry (haiku) to art (Judy Chicago) to how you live (Marie Kondo) to language itself (check out de Saussure for starters).

Arguably, we're driven to do this so what we can focus on what really matters — our experiences.

As Jer points out, “The idea of efficiency is what proves the human emotion in my opinion. Because if you think about it, a computer is trying to find the most efficient process to get a project done. That's what a computer is meant to do."

"So when someone goes on and on about AI and how it's going to take over the world, I take a step back and think, ‘the most efficient way for AI to do anything is to not exist.’ The most efficient way of getting the job done is by the job not occurring."

"If we can automate our entire lives, what is left? Our experiences."

"And that is our purpose in this world is to enjoy what we had this life is the reward of efficiency: experiences are the things that bring us joy.”

Finding the metrics to measure emotion

Jer is also fascinated by another marriage of the objective and seemingly subjective: measuring emotion.

"When I see a Diet Dr. Pepper bottle," he explains, "it brings me joy — a way that I can't put in an NPS score. There's just things that I can't do when it comes to measuring things emotionally."

What's the answer to that? Finding a series of metrics to measure and show improvement in.

One way to do this is sentiment tracking. This generally means an efficient way of measuring the language of online interactions with the brand, whether positive or negative.

How do you or others measure emotions (or those more abstract concepts)?

Join us on Instagram and add your thoughts!

How to collaborate, and the complimentary color theory

Jer works with a team to build stories out of data. He finds that the best people to work with aren't those similar or opposite to him, but complementary.

This complementary concept isn't new — but we loved why Jer felt it was important:

“Complementary people can not only help me build on my momentum, but can also create momentum on their own that I can build on top of.”

If you want to geek out for a bit on color and why complementary colors work the way they do, take a look at these links:

Jer's tips on collaborating with data scientists

The first meeting

Jer recommends sitting down first and talking about the project and its expectations and goals — and when you do, not getting caught up in tasks (which is easy to do).

"I would rather say, 'what are you trying to finish in the end? And then let's work backward from there to get the job done.'"

Throughout the project

Jer's tip: Do not hold up testing with audiences! It's not worth it.

“One of the things that a lot of customers get caught up in is they have to have the perfect experience before they're willing to test something with an audience. That isn’t what people need or expect. People (test audiences) are generally understanding, and they understand that things don't have to be perfect right away.”

General tips

Come to the table thinking about relationships rather than approaching it as just a series of transactions.

  • Data might seem like just a clump of numbers and metrics — because, well, it is. As Jer puts it, “Data can’t make anybody do anything." But behind the data are humans craving relationships. Jer focuses on not just the tactics but the strategies to find those points of connection your audiences crave.

  • Let the task list come from the initial meeting.

    • Jer has been doing this for a long time with some of the biggest names in the industry, and tells us that some of the best projects aren’t started with task lists, but conversations. You should come to the first meeting ready to engage, but also come to the meeting with questions you need help answering.

  • Don’t expect one data scientist to know everything

    • Jer is a tried-and-true specialist. Like any specialist, he has focused on a deep rather than wide skill set. He will go through your project and identify what he can do, and then talk about how to cover the rest.

Jer's passion project

A solution that translates the experience of single mothers into terms companies are looking for — and then point them to resources these women need.

"Single mothers are a huge untapped resource of creativity and energy. They get the job done with very few resources."

As he put it into perspective: "Elon Musk is not that much smarter than anybody else. He just has resources and a team of a billion people and $700 billion to get it done. So how can we use data to change that, to give single moms, inner-city and country folks access to resources to build opportunities?"

Reach out to Jer

You can contact Jer by reaching out to us at

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