Laura Ellis is trying something different—not that change is anything new for this data virtuoso who seems up to every challenge. Laura spent more than fifteen years at IBM, evolving her skills in business intelligence, data science, and predictive modeling before jumping into a new role at Rapid7, a company focused on cybersecurity and compliance services and solutions. 

“Although I love building strictly technical skills, I was beginning to realize that I had a deeper appreciation for the intersection of business needs and the tech to enable them,” she said. 

Laura is an active member of the data community and is eager to share insights and learn from others. Her Little Miss Data blog is an excellent resource and even includes tips on introducing kids to data science. 

Read on to learn more about this inspiring woman in data, how she got interested in technology, her recommendations for people interested in a career in data science, and more.  

Discovering a Love of Data 

How did you get interested in technology?

I became interested in technology by mere happenstance. My parents were both teachers, and, growing up, I had planned on being a teacher, ideally in math and science.  

I was required to complete a college undergraduate degree before applying to teachers’ college. In my senior year of secondary school, I took my first computer science course. I enjoyed the course, but even more importantly, I was encouraged, in that class, to pursue the field. My teacher explained that the field was growing quickly and that even if I were going to be a teacher, it would be an excellent specialty field. At the end of the course, I received a computer science award. With that extra boost of confidence, I decided to give it a go for my undergrad. I received my Bachelor of Engineering degree with a specialty in software engineering from the University of Western Ontario in Canada.

Understanding how to leverage data is essentially like developing a superpower that allows you to extract insights at a scale that humans aren’t effectively designed to do.

How did you get started in your data science journey? 

I’ve been working in the data field for over fifteen years. However, it wasn’t until I started focusing on analysis that I realized the power of data. Understanding how to leverage data is essentially like developing a superpower that allows you to extract insights at a scale that humans aren’t effectively designed to do.   

I functioned as a lonely data analyst for several years before the big data boom hit. I distinctly remember taking a one-year maternity leave in Canada and coming back to a buzz snowballing around the data field. Suddenly, the job that I’d held as a lonely data analyst was exciting to people.   

Teams were looking to staff up with data professionals, specifically data scientists. I had an opportunity to try my hand at data science. I quickly learned that I lacked the necessary statistical knowledge and thus began my Master in Predictive Analytics at Northwestern University. 

What prompted you to co-found Data Mishaps Night? 

Years ago, I met Caitlin Hudon, Principal Data Scientist at OnlineMedEd, at an RLadies Austin meetup. We share a love for family, data, hiking, and tacos. During the pandemic, we discussed missing the ability to attend meetups and share stories with other data professionals.  

Caitlin had given a talk at the 2019 RStudio Conference about the lessons she’s learned from her decade of data science work and the mistakes she encountered along the way. I was in the audience cheering on my friend, and it was amazing to see how much her stories resonated with the community. Caitlin believes that sharing mistakes helps everyone fend off impostor syndrome and discover pitfalls before they happen. I fully agree.   

When I first started in the data field, I distinctly remember how fearful I was about making a mistake. Making a mistake with data is especially terrifying because you can inadvertently give people incorrect information. Data often fails silently, presenting itself as truth when, in fact, it is not.  

We launched Data Mishaps Night in 2021. We had sixteen presenters and over 250 attendees participate in a magical night of shared data mistakes and learnings. It was such a wonderful experience that we decided to make it a yearly event.   

In 2022, Hilary Mason, Co-Founder of the Hidden Door, kindly agreed to kick off the evening with a keynote full of her data mistakes and lessons learned. We followed with twelve speakers and opportunities for audience participation. It was another great night reaching over 350 attendees. We’re grateful that the event has received such a warm welcome, and we look forward to next year. 

What advice do you have for women specifically who seek to advance their careers in the technology sector? 

My advice for women in technology is to invest in your network, especially your women’s network. Find other women you can learn from and ask to grab a virtual coffee with them. Offer advice or assistance to women you believe can benefit from your experience or connections.   

You will learn something from every connection that you make. Some of these women will help shape your career, some will provide you with insight, and some may just help you with a laugh (or a cry) after a particularly difficult meeting.   

Networking is sometimes considered a transactional tool to get ahead in your career, but truly it’s so much more. The people in your network become your friends, your inspiration, your support systems, and your lifelines.

Data often fails silently, presenting itself as truth when, in fact, it is not.

What’s Next for Data Science?  

How have you seen data science change over the years?  

I’ve noticed that data science has risen in popularity over the years as data collection became increasingly pervasive and computers became more cost-effective.  

With this rise in popularity, data science packages and tooling have become much more accessible. Developing a model takes considerably less knowledge and time, which has caused the community to shift focus from what we can do with data to what we should do with data. We’re having deeper discussions around data ethics and our responsibilities as data practitioners.   

How can we make the data science and analytics realm more accessible? 

For internal data platforms, I believe that companies need to invest in building easy-to-use systems (simple data, simple tools) coupled with robust support and enablement programs. We need to give people access to data in a reasonable form to navigate and ensure that we have low-friction internal support paths to assist with their journey. Building this framework is not easy, and the blueprint varies greatly with business needs and constraints. It takes time, commitment, and focus to bring this vision to life.   

More generally, I suggest that all internal data platform teams seriously invest in understanding and improving your data consumer’s user journey. This type of work can be difficult for internally focused teams to prioritize. It’s often seen as a luxury among other high-priority requirements.

After all, your product is typically not customer-facing. 

However, creating a positive user journey for your internal data platform will pay off in spades. The more your consumers can confidently self-serve, the more bandwidth you will have, as a company, to employ data-informed decision-making.  

Building the Next Generation of Data Leaders 

What resources do you recommend for someone interested in working in data science? 

The good news is that there is an endless supply of accessible beginner material available. The bad news is that there is endless material available, which can be incredibly overwhelming. 

My advice for those starting is just begin.  

Select learning material that looks appealing, and then work through it. Apply this material to a real-world scenario to solidify your understanding of the concepts you learned. Then iterate, moving a little closer to the material you enjoy and a little farther from the material you don’t enjoy. 

Along with this, seek out data communities that interest you. Speaking with others about their work helps tremendously to build your knowledge of the domain while also building a powerful support system.  


Blog: Little Miss Data