How Data Continues to Transform the World of Business: Key Takeaways From Two Industry Professionals

The world of data science, data engineering, and analytics hasn’t shown any signs of slowing down these past few years. It continues to gain momentum ramping up efforts to improve effectiveness and efficiencies across many different business facets.

In a recent report, IDC predicts that the collective sum of the world’s data will grow to 175 zettabytes by 2025, with an annual growth rate of 61 percent. To put that into perspective, a zettabyte is a trillion gigabytes — now multiply that 175 times.

By discovering meaningful trends and patterns in this vast amount of information, data experts can help teams collaborate, manage, and execute better projects and products.

Check out this March 2021 transcript from a virtual question and answer session with Ciera Lowe, Data Engineering Team Lead at Ocrolus and Dena Wilson, Data Engineer at Pacers Sports and Entertainment; hosted by Alison Abbington, Industry Engagement Manager, on behalf of 2U, Inc. Whether you’re just starting to delve into the field, seeking to change careers, or looking to advance your current skill set, these two industry professionals shared helpful advice for any aspiring or current data professional.

What is the difference between data science, data analytics, and data engineering?

Dena Wilson: Everything is about data. Data analytics is the front end of data: data strategy, providing dashboards, business intelligence. Data engineering is more about moving data; you do a lot of pulling from different sources and loading into an environment where you can analyze data. Data science is a cutting edge technique that’s occurring right now related to artificial intelligence. It centers more around Python, R coding, predictive models, and algorithms.

What does your day-to-day look like?

Ciera Lowe: The biggest part of the day consists of running scrum/agile sprints. I also serve as product owner for the team, so I attend quite a few meetings. At this point I do not code anymore. Instead, I spend time meeting with stakeholders, trying to understand their needs, managing expectations, and turning those into actionable pieces of work. In data especially there’s a lot of finesse around trying to get what stakeholders need and being able to code that. I’ve been a Python developer on and off for my career, picking up SQL on the job and setting out to teach myself Python. My first job was doing data analysis for hedge funds where I took over complicated models that data scientists worked on. As my career progressed, I had the opportunity to build entire frameworks by myself. My current company is at a hyper growth stage, so I’ve been meeting with recruiters and the growing team I’m leading.

Dena Wilson: My day-to-day is spent on an Azure platform where we have a data warehouse. I spend time loading data from Ticketmaster where we have a food and beverage service and pull that data into Salesforce. A lot of what I do is integration, so we like to see customer data like what they’re spending, what they’re buying, etc. Ultimately, we want to see a 360-view of our fans. Right now, I’m working on design architecture and how that data is going to flow most efficiently.

What skills are necessary to be successful in any facet of data?

Dena Wilson: I came from a finance world and that served me pretty well. I would say you need a natural aptitude for critical thinking, so looking at data in a way that would make sense. What would be the most valuable is to be able to understand business because if you understand business processes then you understand where you can fit in and be the most valuable. That’s where you find value — when you can fill a hole that the business did not understand they had. That’s when you start to become a leader and give “wow” moments to businesses.

Ciera Lowe: One of the biggest mistakes I see people make is saying they understand data when they don’t understand the business. I can’t emphasize enough how having genuine interest in the domain is important. For example, in my case, understanding small business lending and opportunities in the space our company is filling and trying to empower different areas or demographics that have been overlooked. I have a genuine interest in what I do and I don’t think you can be successful with data that you’re uninterested in. From the outside, don’t approach a job thinking that you’re not interested because once you’re there you can find what sparks your interest. Also, no matter where you are in the data space, take a minute to sanity check yourself.

Any advice for newcomers or early stage talent on tips for starting to look for jobs and how they can stand out in a sea of applicants?

Ciera Lowe: I did a lot of exercises [I] found in programming books. I also look for willingness, so rolling up your sleeves, doing the work, and wanting to contribute to the team. In a junior role you can’t really screen for experience, so wanting to do the dirty work for the team’s success is important.

Dena Wilson: Find something that you can hone in on and speak as an expert. For myself, it’s data warehousing. For others it can be a specific tool. I wouldn’t get too hung up on the coding aspects of things, instead I would obtain more knowledge about concepts and how things work. Develop[ing] your soft skills, [such] as personality, is important nowadays. I once had a director say that you can always teach tech but you can’t really teach soft skills — you either have them or you don’t.