All kinds of organizations today are hiring data analysts to make sense of the growing amount and range of data they generate and collect. Wringing actionable answers out of data has become a key business skill. All kinds of organizations collect big data and want to use it to make or improve decisions. Firms in fields as varied at B2B and B2C commerce, health care, manufacturing, and marketing all use data analytics to improve processes and enhance profits.

For example, "Medicine uses data analytics in clinical studies to predict the efficacy of medicines and survival rates," says Carl Howe, director of education at RStudio, a company that provides open source and enterprise tools for use with the R programming language. "Factories are always looking to improve production yields — if you can improve yield by one to two percent, that can mean millions of dollars to a chip or drug manufacturer."

And while companies are working on automating data analytics, "around 80% of the job hasn't been automated, and the 20% that is being automated still isn't automated really well," says Matthew May, lead data scientist at URSA. "More importantly, any problem that auto-machine learning can solve is a 'softball problem.' Hard problems take one or more people to work on. So jobs doing data analytics aren't going away."

If you've read our piece on what you should know before getting a degree in data science, you may wonder how data science and data analytics are related. Data science, says Howe, "is about 'can we model the world — and use these models to make predictions,' while data analytics is more about extracting insights from big datasets. For example, 'we forecast the demand for oil will peak by 2030,' or 'the proportion of the world living in poverty has almost halved in the last 20 years' are both results from data analysis."

Kennedy Waweru wrote this.