It takes a community.
In celebration of our 10-year anniversary, we are highlighting some of our best people, partners, and instructors. Over the next few weeks, you’ll have the opportunity to be inspired by some incredible stories that have driven the success of our enterprise business.
Keep reading to meet Steven Longstreet, one of our instructors, who teaches data to enterprise businesses to help them upskill and reskill their employees for the future of work and is a member of our AI and Data Science standards board.
GA: Where are you located, and what is your role at GA?
Steven Longstreet: I work out of the DC campus and live nearby in northern Virginia. I work as an instructor, and I am also a member of the AI and Data Science standards board.
I first discovered GA through a team member, an instructor for GA, who took the 10-week part-time data science course. She wasn’t confident with the content, so I decided to take it with her to support her. I had a blast, and then the GA team asked me to become a data science instructor.
It was fun to take this class and see how different people were learning and how the program broke down the barriers to data science.
GA: What is the instructor’s role in learning?
Steven Longstreet: People who sign up for our classes truly want to learn and better themselves. That’s actually one of the coolest things to be a part of. However, the reality is some of the things that we teach are complex, with barriers to learning. My role in learning is making these topics approachable and removing those barriers for each student’s personal journey.
When learning something new, you’re making yourself vulnerable. The instructor has to create an environment where it’s okay to be wrong. It’s my job to understand the student’s barriers and make sure they are okay to be vulnerable and ask questions.
GA: Why did you get involved in our AI and Data Science standards board?
Steven Longstreet: The whole purpose of the (AI and) Data Science standards board is that the term “data scientists” means absolute dribble. To some people, a data scientist needs to have a Ph.D., and there are other groups who just give out the title to anyone.
I’ve always struggled with this concept. Part of joining the board was coming together with a group of like-minded people and saying, “What is this term for us? What does it mean? What are the aspects of the new data scientists? What does a career look like? How can we best empower and leverage these people?”
The concept of a scientist is someone who pushes human understanding, and it doesn’t have to be something incredibly dramatic. It can be one microscopic new thing that you uncover that allows human advancement. A data scientist within the organization pushes the understanding of data in a more usable way. We set a clear definition that worked across the range of companies participating in the board.
The other jobs of the AI and Data Science standards board are explaining what a career in this field looks like and making it clear what fields someone can specialize in. We need to be clear about the roles and career path and delineate what these terms mean.
GA: What do you think the most critical data skills are — right now?
Steven Longstreet: Let’s break it into two parts: data engineering and data literacy.
I’ll start with data engineering because it’s part of your workflow. No matter what. Data needs to be built in the context of the problem you’re trying to solve. These skill sets are both rare and powerful, as a data engineer looks at the needs and resources across the organization to build core data assets with the most powerful and broad applicability. We’ve been talking about big data pretty heavily for about seven years, which can be summarized as more data than you have the resources, skills, or knowledge to wrangle. Maybe 5% of people feel comfortable working with big data, and those people jump into that challenge head-on.
Data literacy is understanding basic data concepts. When I talk about data literacy, I want people to articulate the problem they are trying to solve. Are they trying to forecast something or classify something? It’s about understanding data isn’t perfect and has biases. We build data for a particular context, so you can take the same raw materials and build a pick-up truck or a car, depending on what you’re trying to do with it. In other words, you have to understand why you built your data in the first place.
It’s about knowing what’s possible with data in your field, asking the right questions, and effectively communicating with data people to get the answers.
GA: What advice do you have for leaders who are trying to prepare for the future of work?
Steven Longstreet: You have to listen to your team, but you also have to recognize that there’s a problem you’re trying to solve. I’m in an industry where people are serving people. Most of my employees are in front of people, but for the last year, we’ve all been working from home.
The future of work at my company, Hilton, isn’t changing from the face-to-face experience, but we’re listening to our team and realizing many people want flexibility. We are thinking of new ways to bring people together because there are aspects of work that you can’t replicate virtually.
I think the future of work doesn’t change the fact that human interaction is incredibly important to advance any problem. If one person could work entirely in a vacuum, we wouldn’t talk about Tesla and SpaceX; we’d only talk about Elon Musk. The reality is that a lot of people make work happen; one person rarely solves all the problems by themselves.
Stay tuned for more incredible stories from our team and partners in the coming weeks. Want to learn more about how GA can make a difference in your business today? Get in touch.