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According to Silicon Angle’s infographic, “IBM reported that 90 percent of the data in the world today was created in the last two years alone,” with “2.5 quintillion bytes of data every day,” but too few individuals are skilled enough to effectively interpret it for brands.
The Skinny on Big Data
“Data used to be the backup for instinct and intuition,” says Mark Bregman in his article for InformationWeek, “now it's instrumental in how companies know which marketing campaign is working, which new markets have the best potential, and which consumers represent the best prospects for growth. Data is the ultimate roadmap, and job candidates who can navigate it are poised for success.” It’s no surprise that companies are desperate for employees who are data savvy and able to churn the large amounts of information being generated daily into digestible pieces of information. So, what exactly is a data scientist?
Data Scientist 101
According to Anjul Bhambhri, Vice President of big data products at IBM, data scientists are “part analyst, part artist” – someone “who is inquisitive, who can stare at data and spot trends.” Individuals in this particular role don’t just sift through and organize piles of information for companies, they are part of a cross-functional team within an organization who provide various departments with pertinent information to facilitate growth and innovation. Now that you know what a data scientist is, let’s discuss how you can be on your way to mastering this much needed skill.
Becoming One of Them
In his post, “How to Start Thinking Like a Data Scientist,” Thomas Redman indicates that anyone can gain the basic skills to help the professional become more “data literate” in their everyday lives – it just takes a bit of practice and diligence. In his exercise, Redman takes the ordinary office nuance of whether or not it’s true that meetings always start late and analyzes this issue through the eyes of a data scientist. He starts off by identifying the issue and posing it as a question to verify its validity. From there, Redman gathers data from various meetings held over a couple of weeks and begins analyzing the data, creating “summary statistics” with the findings.
In his example exercise regarding meeting start times, Redman found that over a two-week period, 10 percent of the meetings he had attended started on time, starting 12 minutes late. A data scientist would take that information and decipher how much money that costs the company, as Redman poses, “If those two weeks are typical, I waste an hour a day. And that costs the company $X/year.” Continue digging deeper into the data until you find tangible evidence that you will positively influence the company’s operations and bottom line.
To read Redman’s full post, inclusive of his full exercise, read here.
The Reality of Big Data
Unfortunately, there aren’t enough trained individuals in the world to become big data scientists – and that has a lot to do with early education (i.e. high school) not pushing STEM (science, technology, engineering, mathematics) courses enough, or at all. The New York Times reports, “There will be almost half a million jobs in five years, and a shortage of up to 190,000 qualified data scientists, plus a need for 1.5 million executives and support staff who have an understanding of data,” according to findings of a recent McKinsey Global Institute report.
The younger generations might not even understand the massive role that data science plays in their everyday lives – like, generated ads based on Internet search history, or suggested pages to “like” on Facebook. Without more able bodies and minds to fill these data science roles, the umpteen amounts of data being generated every day will be an utter waste.
Tell Us What You Think
Does data science sound like a viable and interesting career for you? Share your thoughts with our community on Twitter or in the comments section below.