Kubrick Data Product Consultant Sydnie Lesser explores her journey into data and technology after graduating from Tulane University. She spoke with the Tulane Alumni Association Professional Development Series to share her insights on the skills she gained from her studies which have supported her to learn today’s most important tools and tech, as well as bring a fresh approach to business challenges.
Breaking into the tech industry with a non-technical background can seem extremely daunting, especially as a new graduate. Leaving college with degrees in Neuroscience and Environmental Studies, I knew that I had to shape my skills to fit an unfamiliar industry. Although I knew I wanted to make the jump into tech and data, I didn’t know just how valuable my non-tech background could be in this sector.
Working in tech and data requires diversity of thought. I’ve found it beneficial to enter the industry without a traditional computer or data science background because it allows me approach business problems with a different lens; I know what it is like to be on the other side of a technical tool without an understanding of how it works. Since I can put myself in the shoes of a non-technical business user, it is easier to scope and develop simpler, more cost-effective solutions. Instead of trying to develop the most complex, high-tech tool that may not translate to a business audience, wasting time and resource on a product which does not get adopted, I can create something that is tangible for a non-technical audience to use.
Moreover, data exists in and is transforming every industry. Having subject matter expertise is important to contextualize data within its sector. These nuances may be arbitrary to others that are unfamiliar with the environment that the data exists within. For example, data is becoming an increasing vital resource for life sciences and pharmaceuticals, helping to better understand patients’ needs and so improve the safety and efficiency of developing and trialing treatments. Combining domain knowledge and experience from biomedical backgrounds with data skills can help data teams to better use patient data, with understanding of the dangers of biases and sensitivity of the data they are handling.
Analytical thinking is another important skill that I’ve recognized when working with data. Problem-solving skills taught in other disciplines can be applied to technical problems. Those coming from STEM backgrounds like me may be specifically familiar with taking concrete knowledge and applying it to a critical thinking question. No one dataset is created equally, meaning that you need to be agile when proposing solutions to data-related problems that not only solve the issue but also comply with stakeholder requirements, budget constraints, and the available technologies.
Communication and presentation skills are also valuable. Being able to translate technical information to a possibly non-technical audience, i.e., senior stakeholders, is vital for supporting the evolution of businesses. You need to be able to demonstrate how a technical solution positively impacts the business at a larger scale and why it would be worthwhile to implement that solution. Being able to show the overall picture, as opposed to zeroing in on small technical details, will resonate better with your audience and help breakdown the siloes between the business and technical teams which often hold organizations back from embracing data and technology.
Ultimately, a willingness and hunger to learn is critical. Tech and data are constantly changing, whether that be through the release of new software, coding languages, and even shifting industry standards. Take the recent global phenomenon of ChatGPT, an AI chatbot which is disrupting search engines and content creation. Within weeks of its launch, this tool had the world questioning if it could replace the industry-leading search engine Google, as well as raising concerns about bias, its use in education and software development, and more. For those in the data and AI world, ChatGPT has prompted countless conversations about its ongoing evolution, with a focus on driving continual learning in order to advance and evolve this technology. Like other research-based industries such as medicine, it is important to stay up to date with current developments to stay relevant.
