In an industry where data is king and the landscape is ever-changing, the journey from a tech giant like Facebook to a forward-thinking company like ShyftLabs is filled with valuable lessons and insights.
Techtalent.ca sat down with Yi Ma, Chief Data Officer at ShyftLabs, to learn what it takes to be a successful leader in data science.
Ma emphasizes the importance of communication, strategic thinking, and team building while recounting a pivotal moment at Facebook where effective data storytelling led to significant business transformation.
In your journey from Facebook to ShyftLabs, you’ve navigated through various aspects of data leadership. What lessons have you learned along the way that you believe would be particularly valuable for individuals aspiring to leadership roles in the data science domain?
YM: One important lesson I’ve learned is the importance of communication. As a data leader, you need to be able to communicate complex data insights in a way that non-technical team members can understand. Also, it’s not enough to have technical skills; you need to be a strategic thinker as well. This is how great data leaders align data strategy with business objectives. Finally, building a strong team is crucial. Besides hiring people with the right skills, you have a significant role in mentoring and developing your team to drive the business forward.
Can you share one practical strategy or example from your own career where effective data storytelling played a pivotal role in driving business decisions or transformations?
YM: During my time at Facebook, we were tasked to figure out how to increase user engagement. By analyzing data, we discovered patterns in user behavior that differentiated people who were highly engaged with the product from those who were casual users. We used this information to create a narrative around the user’s journey and presented it to the leadership team. This led to the creation of the overall user engagement strategy in the company.
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How do you stay informed about the latest trends and technologies in data science, and what advice would you give to those seeking to stay at the forefront of this dynamic industry?
YM: I stay informed through a combination of reading industry news, blogs and publications, learning about new tools and technologies, and networking with other professionals in the field. My advice would be to do the same. Attend conferences, take online courses, read industry publications, and don’t be afraid to reach out to others in the field. Also, data science doesn’t exist in a vacuum, so understanding broader business and industry trends is crucial.
Can you share experiences where building strong cross-functional relationships positively influenced the outcomes of data-driven projects? What advice would you give to aspiring leaders looking to foster such collaborations?
YM: At Evernote, we had a big project that involved several different departments. By fostering strong relationships with leaders from these departments, we were able to work together more effectively and achieve our goals. My advice would be to always take the time and effort to build these relationships. Understand the needs and challenges of other departments and find ways to help them succeed. This will not only benefit your projects but also the overall success of the company.
Given your extensive experience in the field of data science, what piece of advice would you offer to data professionals looking to grow their careers?
YM: I would suggest that they stay curious and never stop learning. The field of data science is always evolving, so it’s important to keep up with new methods and technologies. It’s also crucial to understand the business side of things, not just the technical aspects. Understanding the problem you’re trying to solve and why it’s important to the business will help you provide more meaningful insights. Last, but not least, the ability to connect dots (data points and information), and come up with a compelling story will make you stand out.