How Industry Data Scientists Make Their Work Count
It wasn't that long ago that business leaders would earn nods of admiration merely by referring to their companies as "data-driven" or "data-informed." These days, leveraging data in the decision-making process of your organization is no longer cutting edge; it's the default.
Still, translating all those terabytes (petabytes?) of information into concrete strategies and measurable decisions remains a challenge for many. This is where entrepreneurial data practitioners can make a real contribution to the success of their teams, which is a powerful motivator for many of us—and even more so in times of economic uncertainty.
This week, we've selected a trio of recent articles that cover smart data projects and the positive business impact they can make. Enjoy!
- Zoom in on the top of the funnel. To grow, a business needs not just to retain existing customers but also to acquire new ones. Doing so effectively can make the difference between success and stagnation. In a comprehensive overview, Ivy Liu explains how data scientists can drive customer acquisition and support marketing efforts by "launching experiments, monitoring performance in real-time, and quickly iterating based on market feedback."
- The fanciest approaches aren't always the most effective. The rapid growth of algorithm-powered applications can be a cause of anxiety for smaller and less resource-rich businesses. Anastasia Reusova‘s latest post is a powerful antidote: it stresses that there are still many insights to draw just by analyzing raw data and making the most of signal-based scoring, with no ML models involved.
- If you have to make tough decisions, it might as well be the right ones. Many of us live in regions where brick-and-mortar businesses have struggled in recent years. Martin Leitner takes a nuanced look at the difficult process of store closures, and proposes a range of strategies data scientists can deploy to reduce some of the inevitable negative effects.
If bottom lines, low-hanging fruit, and funnel optimization are the kind of slide-deck buzzwords that make your eyes glaze over, don't despair! We've got a few more reading recommendations we think you'll enjoy:
- Future-proof your next machine learning project by following Khuyen Tran‘s end-to-end template, which centers reproducibility and maintainability.
- "I love how long this survey is!" said… nobody, ever. Trevor Coppins presents a strategy for reducing survey length without sacrificing the results' validity or reliability.
- In a fascinating experiment on text-pattern extraction, Maeda Hanafi benchmarked the performance of GPT-3 against a human-in-the-loop tool.
- "How can we safely benefit from the power of LLMs when integrating them in our product development?" Janna Lipenkova‘s deep dive examines ongoing efforts to control and enhance the behavior of large language models.
- If you'd like to give your Julia programming skills a boost, you have no fewer than 20 tips and tricks to experiment with, courtesy of Emmett Boudreau.
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Until the next Variable,
TDS Editors
