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While AI is getting all the attention, the main enabler, the underlying data is almost always an afterthought, since the linkage to revenue is sort of indirect. There is a very strong but indirect relationship. Data is an enabler of good decisions, it is more and more part of a better product, a higher quality service. Attend a business conference, and you’ll see sales, marketing, and finance leaders dedicating slide after slide to their data strategies—often crediting their wins to data insights. We would like to uncover these stories and give some of the credit where it's due - data people.
There are fascinating things happening in the data world. We at 205 Data Lab consider ourselves forward-thinking in both our tech stack and our approaches. But what excites us most are the domain-specific challenges that push organizations to innovate with creativity and cutting-edge technology. These are the “war stories” we love to hear and share.
They’re solving some of the most complex problems across very different industries. They’re not only bridging the gap between business and analytics but also wrestling with rapidly evolving technology and ever-changing expectations. These are professionals who thrive in a field where job titles are fluid, standards barely exist, and business teams often don’t fully understand their challenges. We want to spotlight their work, amplify their voices, and help foster a deeper understanding of the data domain.
As the first in this series, we had a quick sit down with a data science leader from a major North American retail company to discuss how they’re tackling challenges in their industry.
Interesting takeaways from our call:
Our data leader in this episode is the head of Data Science from a leading north american retailer.
Pressure on Data Science in Retail: Retail grocery operates on razor-thin margins, which intensifies the pressure on the data science team to deliver timely, actionable insights. At the same time, these low margins require analytics investments to demonstrate clear value for dollars spent.
Small Teams with Big Expectations: They have a small team of 3 professionals where all the data science, forecasting space optimization and other retail analytics are developed and updated at a very fast pace, while expanding the coverage by adding digital data sources.
However, they often spend more time on data and feature engineering than optimizing high-value data science models.
Insight: "Data science teams are closer to the business, under pressure to meet tight deadlines and deliver results. Yet they struggle with a lack of consumable data, often relying on data engineering teams that have other objectives. Without greater investment in data engineering, even the best models are limited by the quality of their inputs."
Balancing Innovation and Stability - but not falling behind on Generative AI: Retail organizations tend to favor proven, stable solutions, which can sometimes stifle innovation. This cautious approach contrasts with the fast-evolving landscape of the data field, where new tools and techniques emerge regularly.
The team is experimenting with local implementations of LLMs for privacy and speed. Generative AI holds significant promise for automating data cleaning and feature engineering, potentially alleviating bottlenecks for small, resource-strapped teams.
This interview reminded us of the incredible impact small data science teams can have despite limited resources. Their ability to innovate and deliver under pressure is inspiring. At the same time, their challenges highlight a growing need for investment in data engineering and better alignment between data science and engineering priorities.
We appreciate your perspectives!
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