In the converging worlds of business, design and tech, talk of “big data” is ubiquitous. As a term, I’ve heard of it. As a phenomenon, I recognize it. As both a topic and field, it goes over my head since it’s a “big deal” and hyped as such. What does it really mean? How does big data translate into tactical efforts in the workplace? This year, I’m making it a personal initiative to better understand big data.
Scouring books and blogs would certainly be good ways to learn about big data. Another method is attending a talk. I was relieved when serendipity struck—having recently discovered Promotable, a school that prides itself on the timely mission of “seeking to fill the data skills gap.” They offer courses (live and virtual) in Python Programming, Data Analytics and Product Management. They also organize talks centered on these topics. The first installment in this series was “Healthcare Analytics: Insights to Outcomes” presented by Pamela Taylor, an engineer and MBA, who is the Director of Quality and Affordability Initiatives at Wellcare-Meridian Health Plans.
Considering big data is rapidly evolving, the definitions vary. But this essential take by Lisa Arthur, who wrote “Big Data Marketing” (2013), establishes a foundation: “Big data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.” Some key qualifiers Pamela posed prior to diving into her main talking points was that the audience “won’t learn how to become a data scientist” and that her presentation “was designed for everyone.” Fidelity of understanding appreciated here! Because “big data,” let alone “data analytics,” is heady enough to fly over my head.
Three methods were used by Pamela that helped facilitate her presentation:
Themes: As a ramp-up to the core of her talk, Pamela identified a couple of overarching ideas that unified her presentation. They gave context to the working benefits of big data. One of these unifying concepts was how big data is applied to reducing environmental and monetary waste—a byproduct of the legacy healthcare system in the U.S. This thematic content helped frame the topical content.
Models: To describe the different modes for one of big data’s foremost practice areas, Pamela showed a funnel diagram that simply outlined a hierarchy of the vertical characterizations of AI/machine learning, from the fundamental to the advanced. Straightforward charts or graphics can help clarify a complex topic.
Use Cases: With the building blocks of themes and models as precedence, Pamela correlated examples demonstrating the models and supporting the themes. They actually indicated how big data helped a portion of the U.S. healthcare system for the better.
Altogether, these elements resulted in a cohesive talk. They also raised scaffolds to how big data could help deepen the analysis of other subject matter beyond healthcare.
A big shout-out to Pamela for giving an understandable glimpse of her work across data analytics, AI and machine learning in the healthcare space. And huge thanks to Promotable who imbue their contemporary suite of data-inspired and driven courses with contemporary narratives through their organizing of talks—done by people who give digestible approaches to a field and phenomenon that is becoming ever-voluminous.
My hopeful resolution → To get demystified about big data. Matched by my hopeful intent → To attend more events held by Promotable.
To view Pamela’s talk about healthcare analytics and more, go to Promotable’s channel on YouTube.
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