HITECH: are you optimizing the wrong metric? and Spirit Airlines data goes to auction


Presented by Contemporary Analysis & Omaha Data Science Academy​


A Weekly Upskill for Yourself, Your Data, Your Team

August 20, 2026

Issue 125

Join me for a beer

TONIGHT from 4:30-630pm at the Casual Pint!

Omaha is full of incredible people doing incredible things. This meet up is one small way to bring more attention to and strengthen our local data science community.

I'll be waiting for you, drink tickets in hand! Whether you pop in or stick around for a while, we can't wait to see you and hear about the amazing data+AI related projects you're building!

​RSVP on LINKEDIN​

See you there—first round’s on me!


Giveaways, Dirty Soda, and the Human Side of AI

On Tuesday, ODSA instructor and founder of Selvo, Emily Kenney spoke on "The Human Side of AI Software Engineering" at the Women In Tech of the Heartland event. The Omaha Data Science Academy sponsored and provided ODSA cookies, food, and a dirty soda station where we had an ODSA drink made of Sprite blue raspberry and coconut syrup. Delicious!

It was a great turnout and we even had a giveaway where two lucky attendees won a " gift certificate" to our Data Visualizations module! Congratulations to our two winners and thank you to everyone who came out on Tuesday.


Client Spotlight

Meet Crop Tech Solutions

When Crop Tech Solutions came to us, they had a vision that was reshaping agriculture: help farmers optimize for profit, not just yield. They were telling farmers something the seed companies didn't want them to know: planting more seed doesn't always mean bigger yields—and bigger yields don't always mean bigger profits.

The Wrong Metric: Why More Seed Meant Less Profit

The seed companies came to founder Jim Aden’s farm every year with the same pitch: plant more seeds per acre to get better yields. When he was growing up, his dad planted at a seed rate of 15,000 every year. After Jim took over the farm, the seed company reps said it should be 20,000. The next year, 22,000. Then 25,000. By the time it hit 30,000, Jim wondered…

I’m not sure the extra seeds are making me more profit. More bushels? Maybe, but also more input costs. What if I just went back to what my dad did?

So he tried it. He planted fewer seeds. What he found: more bushels don't necessarily equal more profit.

The revelation was that seed companies weren't exactly lying. There was a measurable increase in yields. But yields are only one part of the equation. They were just optimizing for the wrong metric: yields, not farmers’ profit.

More seed meant more revenue for seed companies, not more profit for farmers. The problem is, by planting more seeds per acre, the plants become crowded. Each stalk gets less sun, less soil, and less room to grow. More stalks, yes, but less yield per stalk. In addition, farmers spent more on fertilizer, water, and diesel used during harvest. All those inputs shrank profits to levels lower than those from planting fewer seeds.

These revelations warranted more study.

So, Aden created Crop Tech Solutions to give farmers the analysis seed companies weren’t: which seeds actually perform best in which soils, and importantly, at which densities. ​
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The data to optimize for farmer profits, not just for higher yields.

Now the question was: how do we analyze data across thousands of acres to support more farmers?

When we came in to help answer this question, Crop Tech’s process looked something like this:

  • Manually collect zip files from half a dozen sources
  • Drop zip files into a custom-built desktop app
  • The Desktop app produces an Excel file
  • Data Expert manually runs an analysis on the Excel file

This was a time-intensive process. That worked fine when Crop Tech was analyzing the founder’s farm and a handful of neighbors. But now they were servicing hundreds of fields across dozens of farms. Manually downloading and processing files per field and per season would take weeks or months to handle each year of data.

Through our work together, we helped Crop Tech Solutions:

  • Automate the Manual Work
  • Find the Right Data
  • Rebuild for Scale
  • Make the Data Usable and Readable
  • And Solve the Biggest Company-Wide Bottlenecks

The result?

Crop Tech Solutions went from processing 30,000 working rows of data to 3 Billion (yes, with a B).

Crop Tech now ingests billions of rows of farm data across hundreds of farms, dozens of customers, multiple states, going back over 10 years. They can run reports in minutes that used to take weeks. They can re-import everything from scratch if they need to, without blowing their budget, and they can produce each planting file without the old labor-intensive, multi-step process.

And most importantly, they can successfully scale a service they already know farmers value without burning out their team.

Because at the end of the day, this was never about building tools.

It was about helping farmers and changing agriculture for the better.

​If you want a deeper look at how we helped Crop Tech Solutions achieve this, download our latest case study. ​


Around the Data World

The Proprietary Data Race

Now that all the "public" data is consumed, big AI is racing to find and buy proprietary Data to continue training it's models.

This week, Google won an auction to buy Spirit Airlines business data for $10 million.

After Spirit Airlines infamously closed its doors in May of this year, its 40+ years of data went up for bid in a bankruptcy auction on Monday. ​

Google’s auction purchase reportedly contains roughly 100 million emails, about 500 million Microsoft Teams messages, millions of files in OneDrive and SharePoint, IT tickets, spreadsheets and documents, operational records, software/code, financial and accounting information, marketing and website analytics, and other records documenting how Spirit functioned as an airline.

​Reportedly, "the data ​will be de-identified before ​the sale is complete, containing no customer ‌information ⁠or personally identifiable information."


Spirit Airlines isn't the only one.

​The Information and Business Insider report that more companies are receiving offers to buy their internal data.

This signals that the next wave of training AI is to teach them how people actually work.

Your data is one of your most value assets. How are you protecting and managing it?

If you're answer is "I'm not sure" or "It could be better," schedule a call with our team and we'll help you uncover your next step to protecting and optimizing your company's data. ​

AI Hardware Heist

If the first Fast and Furious only came out in 2026, they'd be heisting AI hardware. Apparently this is happening all over the country as "cargo theft losses across the U.S. and Canada more than doubled year over year to $304.6 million in Q2 2026."

In a recent Fast and Furious style heist, Cargo thieves ram security escorts to hijack AI hardware shipments in California — brazen thieves employ PIT maneuver, rear-ending tactics to secure goods for the black market​

Hard to believe when that movie came out in 2001 they were stealing dvd, VCRs, and TV VCR combos...


We're not new to this

AI (as we know it now) might only be a few years old, but we've been doing this work for close to two decades.

Circa 2012, I said it this way...

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"We go in to a company prior to them purchasing analytics and help them understand:

  • what the process is going to be,
  • what the data might find and,
  • how they would go about implementing it once they’ve found the answers that they need.

We then go back after we’ve done the data searches and revisit that last point."

Now, you can replace "analytics" with AI, and it still rings true.

We don't only build your solutions, we help you decide which solutions are worth build.

​Schedule a call with our team today. ​

"The process of connecting with people, of understanding what their needs truly are and how we can affect that change drives me to get up in the morning" - also still true.

From a much older, wiser, and fully gray bearded Nate:

Go build something amazing,

NATE WATSON

CEO, Contemporary Analysis

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