As the trend of big data continues to emerge, it’s not hard to get lost in the noise. Everyone wants to write the next great post about it, analyze its utility, and declare his or her prophetic insights for the industry’s future. You could easily spend a week reading post after post without gaining a single new piece of valuable information.
So we did the sorting for you, and came up with this list. These represent not only some of the most popular posts to do with big data so far this year, but also those that can truly inform what the future holds for data science.
“Scientists say all the world’s data can fit on a DNA hard drive the size of a teaspoon” – Quartz
This post, written by Quartz’s Mike Murphy, explores the future of data storage—a problem that will continue to become more relevant as we expand on the 455 exabytes of data that currently exist in the world.
As more small businesses adopt the use of big data, and more and more companies begin to rely on Microsoft or other big companies’ servers, it’s curious to think how might find ways for tiny startups to manage their own big data.
“Big Data and Bacteria: Mapping The New York Subway’s DNA” – WSJ
Back when the threat of an Ebola outbreak in NYC seem imminent, this big-data focused article written by Robert Lee Hotz was actually featured in the Wall Street Journal. It showed, as many of the other posts on this list will, one of the many ways that big data has become increasingly relevant in the way we solve problems.
The article tracks a project where microbiologist sorted 10 million fragments of biochemical code, hoping to find better ways to prevent disease transmission and understand the sources of much of the bacteria New York commuters interact with. (Germaphobes beware).
“Why Businesses That Use ‘Big Data’ Make More Money” – Entrepreneur
This insightful infographic from Entrepreneur’s Catherine Clifford is the perfect cap to this list—and an encouraging note as we march on through the year. While we may debate endlessly about the future of data scientists, the over-use of the phrase “big data” and the inevitable change in the data landscape, the reality is we’re doing something right.
“Big Data Adoption in Mid-Market Companies at Record Levels” – icrunchdata News
This article was written by Mithun Sridharan of BlueOS, who is based out of Frankfurt, Germany. He provides an international perspective that’s especially helpful in terms of the “changing landscape” discussion. He posits 6.5% growth by 2016 in the collective spend in IT for mid-market companies—outpacing enterprise.
He believes that this growth indicates how these mid-market companies will increasingly use big data for analytics-based problem solving across multiple areas. If he’s right, it’s presents an extremely positive outlook for small businesses and big data.
“U.S. millennials post ‘abysmal’ scores in tech skills test, lag behind foreign peers” – Wonkblog
As we try to determine what the future roles of data scientists will look like—and how we adopt that in the new millennial workforce—this article begs the question of how we can better train the next generation of IT and data personnel.
“27 hilariously bad maps that explain nothing” – Vox
This light-hearted post written by Vox’s Matt Fisher is a painful reminder of the importance of data visualization: If you don’t have someone who can actually transform your big data into something useful, you’re dead in the water.
“Can Big Data Save The Last of India’s Wild Tigers?” – Ensia
Researchers are using 25,000 data points to determine the changing hotpots for poaching, harvesting and trading of tigers. Signs have showed that their program is helping to curb poaching and stay “a step ahead” of the ever-changing schemes.
“Most HR Data is Bad Data” – HBR
In this article, Marcus Buckingham challenges the idea that human ratings can provide accurate “people data.” In fact, he seems to say, our own narcissistic ideas of “potential” in others makes us a poor judge of how others may perform in the future—and it rather tells a lot about who we are more than anything else.
This notion should challenge data scientists to find new data points and explore an objective method for analyzing HR/people data. It’s certainly an affirmation that big data can help us to make smarter decisions.
“Data Mining Indian Recipes Reveals New Food Pairing Phenomenon” – MIT Technology Review
Never shying away from the strange and innovative, this post from the Emerging Technology section of MIT’s Technology Review examines links in otherwise subconscious culinary decisions—all with the help of big data.
Analyzing 194 ingredients among 2,500 recipes from 8 different Indian sub-cuisines, these data scientists aimed to challenge the notion of a positive correlation in ingredient pairing (i.e. similar ingredients often go together in dishes). The results were surprisingly supportive of this challenge, and show us that big data can even play a role in culinary innovation.
“Learning To See Data” – New York Times
For this article, Benedict Carey explores a different side of data science: perception. One of the major developments that this article addresses is an attempt to bridge a gap between computation-, scientific-based minds and visualization.
The hope is that, using something called perceptual thinking, data scientists can learn how to look at data in a visual way that gets outside the realm of graphs and charts. We’re seeing this already with developments in data viz, and it’s exciting to think about the continued innovations in this area.
Big data is driving business in a positive direction, helping us to make better decisions and produce smarter insights. If these posts can teach us one thing, it’s that innovation is not in short supply. As we continue to discover new ways to use and apply big data, it will become a more accessible tool to share with the world.