In 2006, British mathematician Clive Henry proclaimed that “data is the new oil.”
By many metrics, that remains true today. Data — once refined into a form that provides value — has proven to be a massively profitable resource for companies across industries.
Data drives everything from key business decisions and personalized customer experiences to predictive analytics and supply chain optimization.
At the very center of these efforts are the data scientists who transform raw data into actionable insights and useful tools and products.
There’s a reason that, according to the U.S. Bureau of Labor Statistics, data scientists are projected to be one of the fastest-growing occupations between 2021 and 2031, with a growth rate of 36 percent.
Despite data scientists being such a sought-after commodity, one’s success in the field is not a foregone conclusion, and staying ahead of the curve is a necessity.
Amid a competitive business landscape and the emergence of generative AI and large language models, data scientists must be forward-thinking, never losing sight of what’s next and how they can implement the latest technologies into their techniques and tools.
At Reebelo and Sojern, data scientists aren’t just keeping up with new technologies; they’ve already begun to incorporate them into their recent projects.
Reebelo is a marketplace for refurbished tech devices and lifestyle products that allows customers to purchase or flexibly rent.
What are the emerging technologies that you see impacting data science right now or in the immediate future?
I think AI is already transforming the data science landscape. With more computing power and more data to train the large language models, we are going to see the technology expedite the transformation in data science in the near future.
In the business world, data science is a multifaceted field that leverages diverse techniques and tools to analyze, interpret and derive valuable insights from various data sources, driving data-driven decision-making and business impact. The immediate application of language models such as ChatGPT is to help data scientists provide interactive data explorations to the business stakeholders, automating routine KPI monitoring with greater speed and accuracy. By leveraging its capabilities, data scientists can focus their expertise on higher-value tasks, accelerate data analysis processes and ultimately enhance the overall productivity and effectiveness of their job.
How do you and your team members stay atop these technologies?
To stay at the cutting edge, embracing a culture of continuous learning and adopting new technologies is imperative. As a dynamic and fast-growing company, the data team at Reebelo prioritizes dedicating time to brainstorm innovative ways of integrating emerging technologies to enhance our productivity. Amid our bustling day-to-day operations, we embark on an exciting journey, exploring how these new technologies can be applied to drive our success as a team.
To stay at the cutting edge, embracing a culture of continuous learning and adopting new technologies is imperative.”
How have you incorporated any of these technologies into recent projects, or how do you plan to? How has — or will — that benefit your team?
We actually are already in discussion with our engineering team to test some ways to leverage ChatGPT to enhance our internal BI toolsets. As a starter we plan to build a BI reports assistant. This AI-powered assistant would help business stakeholders to answer some basic report questions and, down the road, intelligently curate and organize reports. The goal is to make it effortless for our business stakeholders to access relevant insights at a faster pace. By embracing this innovative approach, we aim to empower our team to focus on more complicated analyses or projects and at the same time facilitate our business partners with actionable information for better decision-making.
Sojern is a travel marketing platform that leverages multi-channel branding and performance solutions to drive travelers to their ideal destinations.
What are the emerging technologies that you see impacting data science right now or in the immediate future?
The most important emerging technology I see is generative AI and large language models. Another technology that specifically impacts machine learning advancement is explainable AI, which helps us understand how AI models provide certain recommendations or predictions. Reinforcement learning is also a key aspect of emerging techniques that helps us learn the optimal behavior to obtain maximum reward.
How do you and your team members stay atop these technologies?
We organize internal data science discussions to understand the usability of some of these key technologies within our machine learning work. Within those discussions, we compare our current machine learning approaches with the latest technologies to see if we can build a more optimal solution by utilizing them.
We compare our current machine learning approaches with the latest technologies to see if we can build a more optimal solution by utilizing them.”
How have you incorporated any of these technologies into recent projects, or how do you plan to? How has — or will — that benefit your team?
We have started incorporating reinforcement learning techniques like Thompson Sampling and Upper Confidence Bound to solve multi-armed bandit problems such as effective budget allocation in the travel advertisement domain.
An important usability of the emerging technologies could be to use generative AI tools for unit testing our services and functions. In the future, AI will be used more heavily to ensure the robustness of software code.
Another usability that we are currently looking into is using generative AI tools like ChatGPT and Bard for code optimization — reducing execution time, memory usage or other resources consumed by a program — and text generation for code snippets based on natural language descriptions.
