Big Data has generated a buzz-worthy frenzy within the tech sphere in recent years, largely because it is a breed apart from what the Valley has otherwise been feverishly churning on. Data science does not simply add a veneer of convenience to an existing solution, like many of Silicon Valley’s most recent darlings — mobile apps that get your lunch sushi more quickly, have your laundry done more easily, hire a chauffeur more affordably. Technologists equipped with a sound knowledge of how to wrangle Big Data don’t get advantages in the tech game — the rules of the game change altogether in their favor. With this paradigm-shifting power comes a heavy responsibility for us technologists – the responsibility to vigilantly steer Big Data’s growth in a way that protects those who will inevitably fall within its omnipresent reach.
Big Data emerged as the trend du jour in the tech sector within the last decade, but the concept of gathering and analyzing large amounts of data has existed for much longer and spanned industries. Though often mistaken for related disciplines like machine learning or AI, data science is simply “the machine-based collection and analysis of astronomical quantities of information”. In short, it uses technology to analyze staggering amounts of previously idle data to uncover previously obscured insights. It helps us understand our data in real-time, instead of retrospectively, bringing to the surface different observable patterns that can help us make decisions around that data and even predict its future trajectory.