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Why Big Data Needs Big Leadership

Why Big Data Needs Big Leadership

A few weeks ago, bestselling author and well-traveled public speaker Keith Ferrazzi wrote a blog post titled “Interview a Malcolm Gladwell and Create A Tipping Point of Your Own.” Keith was one of Bruno’s teachers in the past, and remains his friend and professional mentor.

I don’t know Keith personally, but the subject of his post, Bruno Aziza, is both my boss and (happily) someone I look up to as a mentor. Bruno has a combination of experience, intelligence, and humility that make him a great manager, and I’ve already learned a great deal from him.

All this thinking about mentors and learning from older, more experienced managers eventually got me thinking about the data science industry. Unlike so many other career options (management consulting, academia, medicine, investment banking, etc.) there isn’t yet a well-established path for people working in data science or related fields. There’s no obvious answer to the inevitable “and then…?” career question. In part, I think this is because data science became a career unto itself relatively recently.

In the past, data analysis was a stepping stone on the way to something bigger — analysts graduated more senior roles and were replaced after two or three years. You were an analyst until you were promoted, and then you were a product manager. Or, in finance, you were a numbers-cruncher until a “front-office” position became available. Data science has since become a discipline unto itself, and we need a better way to keep talented data analysts working in the field in the longer term.

Case in point: Bruno and I conducted a survey of hundreds of self-identified data scientists, asking for feedback on everything from education level to skills sets and years of experience. (The Data Scientist Salary Survey Results are available for download here.) Interestingly, fully one-third of the respondents had 1-3 years of experience in a data science role, while the number reporting 7-9 years of experience was just 12%. While it’s possible that more experienced data scientists are simply too in-demand at work to fill out career-related surveys, the general pattern suggests that too many analysts are moving horizontally (into other roles) or vertically (into more senior roles or management).

On the one hand, this observed trend is liberating—I might have more opportunity to shape my future than someone in a well-established career track in, say, management consulting. However, this open future has its disadvantages. Having a mentor around can be handy when deciding on a graduate school program, for instance.

Mentorship is one of the most important ways experienced data scientists can pass on knowledge not found in textbooks. If the next generation of analytics whiz kids can avoid repeating the mistakes and trial-and-error of the previous generation, the industry as a whole will move forward faster.

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Amelia Mango

Strategist and Marketing Manager, SiSense
Amelia is a strategist and marketing manager at SiSense; she writes about big data, predictive analytics, and data visualization. Her tech industry experience includes project management, NoSQL, marketing and communications systems, and log analysis. She is currently teaching herself d3.js ...