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Amazon’s predictive algorithm suggests you buy a bike pump after you’ve purchased a helmet. Hospitals use analytics to save lives by resolving inefficiencies like patient wait times, patient readmissions, and understaffed units.
Across the country, intelligent power grids can increase production when and where it’s needed, and information systems are used to improve agriculture in developing countries. Even lawyers use data to analyze how specific federal court judges make decisions.
Analytics has become a necessity for businesses today to operate and succeed, and this is driven by the sheer amount of information we have. Two and a half quintillion bytes of data are produced every day—a number so great that, as of 2013, 90 percent of the world’s data had been created in the last two years, according to IBM. Formerly non-technical areas, such as sales and marketing, have now become data-driven. Data is even being captured in what we once regarded as purely qualitative aspects of life, such as conversation and sentiment.
Who Works with Data?
All of this data is empty information without people who can analyze, organize, and interpret it. Two of the most prevalent roles in data analytics are data analyst and data scientist. The former use data to solve business problems, while the latter use it to address open-ended issues.
With a foundation in data analytics, you can choose to follow the data science track or branch out into one of many other directions that require analytical knowledge, such as financial analyst, consultant, chief operating officer, or supply chain manager. Cheryl Richards, CEO and regional dean of Northeastern University–Charlotte, summed it up perfectly when she said that most analyst roles don’t even have the word “analyst” in the title.
Data analysts possess the hard and soft skills needed to translate data and use it to solve business problems. Many great data analysts have liberal arts backgrounds, yet complement their backgrounds by learning hard skills such as statistics, Excel, R, SQL, Python, Hadoop, and Tableau. Others have mathematical and programming backgrounds and learn data analytics to sharpen their business and strategy acumen.
By 2018, McKinsey estimates that the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts. Demand for people with data skills is skyrocketing, and starting salaries are beginning to reflect that.
Of the top 10 required skills for jobs that pay over $75,000, four of them revolve around data: SQL, Excel, Python, and SQL Server. Professionals with analytical skills typically earn higher salaries than their non-analyst counterparts. On average, an HR analyst makes 44 percent more than a recruiter, and a marketing analyst makes 75 percent more than a content writer. Managers, directors, and C-level professionals are increasingly required to understand analytics in order to manage teams that revolve around those skills.