Getting into Analytics: One Professional's Story, Advice

Aimee Lessard didn't start off as a high-ranking analyst with the intelligence community and data analytics private sector companies. She learned on the job. Here are some of her recommendations for how to get started with analytics, machine learning and AI.

Jessica Davis, Senior Editor

June 26, 2018

4 Min Read
Image: vicky_81 - iStockphoto

Technology professionals are flocking to online courses these days, looking to gain knowledge of the hot new coding language or the skill that will command higher pay and more opportunities. Certifications can pave the way to landing a new job.

But some people may have a natural talent for the technology and find their way to the top of their field without formal training. Aimee Lessard had joined the US Marine Corps and was working in administration -- spending 4 years making ID cards.


"I didn't go to college," she told InformationWeek in an interview. "I had a very good Officer-in-Charge who realized I might be a little brighter than some of my peers and suggested I give analysis a chance."

That was in the early 1990s and marked the beginning of a successful career for Lessard, who has served as a counterterrorism analyst and team leader within the special operations community. She is a veteran of the Marine Corps, served as an analyst for the US Department of Defense, and went on to work in the private sector at companies including Palantir and Adobe. She currently operates her own firm, Seafront Analytics and also joined data fusion and content analysis startup Signafire about a year ago as Director of Analysis.

Lessard recently spoke with InformationWeek about her career, the state of the analytics market today, and what recommendations she has for those interested in a career in analytics.

Although she initially focused on making ID cards, it may not have been surprising that Lessard had an aptitude for analysis and analytics. Her father was a cryptologist and her mother worked in the national intelligence community. Lessard said that after that first person saw the potential in her, she learned her new profession primarily through on the job training. She spent 8 years in the Marines and 13 years in the Defense Department. She left for her first private sector job at Palantir, the secretive, privately-held big data analytics company, in 2010, where she served as an analyst.

How you can get started with analytics

For those considering an analytics career but don't know how to get there, Lessard said "there are plenty of ways to pull that thread and jump into it." If you are curious about the world and the environment we live in, analytics may be a match for you. The first step is one you've probably already considered.

"There are tons of free courses that young and even old people can take to understand the basics," Lessard said. [Check out our list of free Machine Learning courses here.]

Lessard said that RStudio is a great tool for people to use if they want to dabble and learn basic programming.

"You can take that as far as you want," she said. "With those opportunities to learn there's a low barrier to entry in terms of cost."

Aspiring analysts and data scientists should also check out local Meetups. Plus, it's a good idea for them to look into how mentorship works in the field.

Analytics challenges, mistakes

One of the first important lessons for aspiring analysts to learn is how to identify bias.

"That's a foundational component of analysis," Lessard said. "You need to figure out if what you are dealing with is true."

Taking any one of these steps can help tech pros take the first step on the path to a career in analytics, and that's one that looks to be very much in demand in the future as organizations look for professionals in machine learning and artificial intelligence -- fields where skilled professionals are very much in demand.

In her roles at Signafire and Seafront, Lessard works to "help people understand how to make that first step into understanding that massive amount of data less daunting…The technology we are building for clients is helping them take internal and external data and make sense of it."

While machine learning and AI can help with that task, Lessard says there's still very much a need for a human to be in the loop as organizations incorporate new methodology.

Lessard said one of the mistakes organizations make when they are working with analytics is the "overpurchasing of technology. Every time there is a new trend, there is a new technology" that promises to help with the new trend.

For enterprise organizations, that becomes especially tricky. Different departments may come up with 14 different pieces of software that their core development team needs to use, Lessard said. The systems don't talk to each other, and you cannot move data from one of those platforms to the next.

These situations actually slow down the workforce and make it less productive. It also results in a lot of technical waste.

"There are tons of legacy systems sitting around and not being used to their full potential," she said. "It's important for businesses doing analysis to be more thoughtful in how they are purchasing tech and what that means to their analytic core as well."

About the Author(s)

Jessica Davis

Senior Editor

Jessica Davis is a Senior Editor at InformationWeek. She covers enterprise IT leadership, careers, artificial intelligence, data and analytics, and enterprise software. She has spent a career covering the intersection of business and technology. Follow her on twitter: @jessicadavis.

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