Q&A WITH LAURA ELLIS
–– What advice do you have for women specifically who seek to advance their careers in the technology sector? My advice for women in technology is to invest in your network, especially your women’s network. Find other women you can learn from and ask to grab a virtual coffee with them. Offer advice or assistance to women you believe can benefit from your experience or connections. You will learn something from every connection that you make. Some of these women will help shape your career, some will provide you with insight, and some may just help you with a laugh (or a cry) after a particularly difficult meeting.
After all, your product is typically not customer-facing. However, creating a positive user journey for your internal data platform will pay off in spades. The more your consumers can confidently self-serve, the more bandwidth you will have, as a company, to employ data-informed decision-making. BUILDING THE NEXT GENERATION OF DATA LEADERS –– What resources do you recommend for someone interested in working in data science? The good news is that there is an endless supply of accessible beginner material available. The bad news is that there is endless material available, which can be incredibly overwhelming.
With this rise in popularity, data science packages and tooling have become much more accessible. Developing a model takes considerably less knowledge and time, which has caused the community to shift focus from what we can do with data to what we should do with data. We’re having deeper discussions around data ethics and our responsibilities as data practitioners. –– How can we make the data science and analytics realm more accessible? For internal data platforms, I believe that companies need to invest in building easy-to-use systems (simple data, simple tools) coupled with robust support and enablement programs. We need to give people access to data in a reasonable form to navigate and ensure that we have low-friction internal support paths to assist with their journey. Building this framework is not easy, and the blueprint varies greatly with business needs and constraints. It takes time, commitment, and focus to bring this vision to life. More generally, I suggest that all internal data platform teams seriously invest in understanding and improving your data consumer’s user journey. This type of work can be difficult for internally focused teams to prioritize. It’s often seen as a luxury among other high-priority requirements.
Data often fails silently, presenting itself as truth when, in fact, it is not. “
My advice for those starting is just begin.
Select learning material that looks appealing, and then work through it. Apply this material to a real-world scenario to solidify your understanding of the concepts you learned. Then iterate, moving a little closer to the material you enjoy and a little farther from the material you don’t enjoy. Along with this, seek out data communities that interest you. Speaking with others about their work helps tremendously to build your knowledge of the domain while also building a powerful support system.
Networking is sometimes considered a transactional tool to get ahead in your career, but truly it’s so much more. The people in your network become your friends, your inspiration, your support systems, and your lifelines. WHAT’S NEXT FOR DATA SCIENCE? –– How have you seen data science change over the years? I’ve noticed that data science has risen in popularity over the years as data collection became increasingly pervasive and computers became more cost-effective.
Follow Laura for more insights about data and discovery:
Blog: Little Miss Data Twitter: @LittleMissData LinkedIn: lauragraceellis
26 | Taking the Next Steps in Data and Discovery
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