Big Data & Analytics Heroes
Jennifer Shin, principal data scientist at 8 Path Solutions, is this week’s big data and analytics hero. Read along as she discusses her thoughts about the blend of skills that makes data scientists effective, elaborating on the need for both technical and organizational skills.
What were your biggest challenges getting started with big data and analytics?
When I started my data science company in 2011, data science wasn’t even a term. I just knew that there needed to be a better way to manage and analyze large data sets that approached what could functionally be considered infinitely large. I started my company because no job descriptions accurately described my work and because very few people understood the value of using data.
Although the rise of the term data science in the popular consciousness has made it much easier for me to talk about my work, the industry public has had trouble nailing down the preferred data science paradigm. We see everything from companies claiming that data scientists don’t exist—because, they say, no one can possess all the skills expected of a data scientist—to organizations lumping data science into IT services. My greatest challenge has been working relentlessly for more than a decade to develop my data science skills while staying committed to what I originally envisioned for the field of data science.
How did you get organizational support for your big data initiatives?
Communication is integral to any big data initiative. Of itself, big data can seem static, but big data often has multiple representations within a single organization depending on how it is obtained, processed, maintained and reported. So I realized that a data scientist needs to be an educator, a businessperson and an inventor all rolled into one. Gaining organizational support requires an understanding of how each department interacts with the data and of the role of each department in the overall project—as well as the ability to make very complex ideas seem simple to nontechnical professionals. Most of all, however, I have found science to be the secret ingredient. When rigorously analyzing data with insights filtered through the tools and methods of science, the rest of the pieces fall into place.
How have big data and analytics impacted how you do your job today?
Big data and analytics have completely transformed my career path and have opened up new professional opportunities for me. Now that the concept of data is better understood, I can discuss my work with more colleagues and offer greater value to my clients, because both understand the importance of leveraging their data. When I worked in management consulting, I was expected to use specific skills for each project. As a data scientist, I have opportunities to use the full range of my skills by taking a holistic approach that doesn’t separate technical skills from analytical abilities.
Do you think big data and analytics will handle the data growth in 10 to 15 years, or will we need another shift in technology? Why?
As data science becomes more established, our focus will likely shift from collecting data to using data. The sizes of data sets will become less important than the efficiency and feasibility with which we can process them. Data scientists will have opportunities to establish best practices and seek greater standardization in the field. Accordingly, the growth of data may not require new technology for collecting increasingly larger data sets, but it may require technology that is better able to process and analyze the data sets we are collecting.
Data science, advanced analytics, Hadoop and Spark are core passions of today’s big data and analytics heroes. To deepen your skills in these areas, please peruse the online courses offered by Big Data University to find one that sparks your interest.
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