Henry Martinez

As a kid, I was naturally curious, taking things apart, devouring science books, and fiddling with science kits. I also set up experiments all the time to analyze results and understand how things worked—in my parent’s driveway where no one could get hurt!

Since that time, I have been passionate about the restoration and repair of electronics. In high school and college, I repaired TVs, radios and communication equipment for gas, tuition and book money. More recently, I’ve rescued a lot of vintage electronic test equipment (oscilloscopes, meters and tube testers). My “fix-it shop” also repairs lots of broken modern electronics for family and friends, and I’m an avid 3D printer “maker.” As you can see, I’m always tinkering with something!

This curiosity for discovering how things work eventually bloomed into a passion for data science. When the microprocessor came along in the 70s, I was the first at my high school to build a microcomputer. Their power of computational strength was significant. If computers could sense the world and decide what was important, through analysis and logic, it seemed they could independently ascertain what to do and take care of it.

Key Experiences in Data Science

After all, the best user-interface is a computer that knows what you want and just does it. Whether it’s a product, a business strategy, or a manufacturing process, I love how data science helps companies do things in smarter ways. I enjoy working with executives to help them understand how technology can drive their businesses.

One of the most rewarding projects I worked on involved a new medical information system, which used analytics to save the life of someone who otherwise would not have survived. The data science ensured the appropriate medical supplies and treatments were staged and available based on the profiles of potential patients who were in the field and away from medical facilities. It was my idea to make smarter logistical decisions regarding the disbursement of the medical supplies to the field based on the knowledge of the potential patients being deployed. Instead of using a static list, we made the list contingent on anonymized but relevant personal attributes. That’s how it saved lives.

In another project, I was on a team that supported the development of machine learning algorithms for a life science company to analyze transthoracic echocardiographs so doctors can more quickly prescribe treatments for patients with heart problems. This project kicked off after a chance meeting with the client’s VP of Engineering (literally by accident in the lobby), and I asked him my favorite question, “What is your headache today?” This was totally unrelated to what Tiempo was doing for the client, but it opened the door for us to deliver a higher-value, more strategic service (third-party validation of analytics), which the client was very happy with.

And for a client involved in mergers and acquisitions, the board of investors was having trouble getting through all of the due diligence work, so I thought, why not put it into a database and slice-and-dice it to see what we can learn? We wound up making smarter acquisition decisions and better product harmonization plans while avoiding hidden infrastructure expenses and releasing greater dividends of consolidation. We also created a dashboard to determine which companies had the most congenial IT systems to determine their strength/weaknesses, thereby forecasting post-merger integration expenses for prospective acquisition candidates.

Key Questions to Ask on Data Science Projects

All of these projects contain the key elements for how I proceed on any data science project. I always ask the client…1) What is the decision that you want to make smarter? 2) What data do you have that makes you think you can make smarter decisions? 3) How does your business benefit from making that smarter decision?

Asking these three things helps set the stage for business-relevant positive outcomes and frame the kind of budget that is justified in doing the project. And my ability to help clients find the answers to these questions comes from my business experiences in many roles.

These include R&D Unit Manager, Director of Operations, VP of Engineering, Senior VP of Products & Technology, CTO, and interim CEO. They all taught me that sustainable competitive advantages can come from strategic thinking as well as tactical thinking. Having a strategic roadmap that invests in the continual examination of current technologies that comprise products and services (to be sure they aren’t stale) keeps a business out of technical debt PLUS keeps them in the lead as far as sustainable competitive advantages.

Making Sure Data Science Delivers Value:

On the personal side, I love my family (especially those grandchildren!) and am a very active volunteer at my parish church (Eucharistic minister, pastoral care, and a member of Legatus). I also play a little piano, enjoy photography, astronomy, and amateur radio. I live in gratitude for all of life’s blessings—and try to help others get there too.

At Tiempo, I’m passionate about serving as a member of the Data Science Guild. We share information on best practices, tools, and lessons learned so we can seed those techniques into our client’s data science opportunities. The bells and whistles of analysis may look like a shiny new toy, but it’s important to align your data initiatives with business objectives to make sure you deliver a sufficient return on your investment.

That’s the way to make sure data science truly drives value throughout your organization!

“Data is the new diesel.”

Henry Martinez

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