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When it comes to big data insights, how do you know you’re asking the right questions? Hiring data scientists is a good start – we’re seeing their growth both on LinkedIn and at LinkedIn. But even data scientists are not immune from the myriad of hidden pitfalls that keep your key insights out of sight.
Drawing from a deceptively simple exercise that I’ve used to haze dozens of data scientists on their first day, I will discuss the good, the bad and the ugly lessons we’ve learned about asking the right questions, denominators and being a data skeptic.
Monica Rogati is an independent data science executive and advisor who has built key data products and teams at Jawbone and LinkedIn; she now helps startups make the most out of their data. As the VP of data at Jawbone, Monica built Jawbone’s data science and engineering team, focusing on developing data products that helped millions lead healthier lives. Her team also analyzed Jawbone’s wearable data to derive novel insights about sleep, movement, and food, then turned these insights into smart product features, compelling data stories, and interactive visualizations. At LinkedIn, Monica was one of the early members of the data science team. She developed LinkedIn’s key data products for job matching and recommendations, and she doubled the effectiveness of the “people you may know” machine-learning algorithm that drives the growth of LinkedIn’s connection graph. Monica is also an equity partner at Data Collective, an early-stage VC firm focused on the big data space.
Monica’s data stories have been published in the Wall Street Journal, the New York Times, and Time and on NPR and CNN. Fast Company recognized her as one of the 100 most creative people in business, and Fortune named her as one of the Big Data All-Stars. She has published numerous academic papers in top-tier journals and conferences and is frequently invited to keynote industry and academic conferences. She has a PhD in computer science from Carnegie Mellon, where she focused on text mining and machine learning, but is now focused on applied data science.
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