Data literacy isn’t just for students hoping to become data scientists, writes Laurence Bradford for Forbes. Understanding, analyzing, and interpreting data are useful skills in any field.
“Organizations need a broad set of data skills, and they need to be in various different roles across the organization,” says Amy O’Connor, chief data and information officer at Cloudera.
With the rise of automation, employees need to understand how data collection and automatic processes are performed, and how those processes affect their work and the company as a whole. “For anyone in almost any business role these days, there’s an automation around that role,” says O’Connor.
But contrary to popular belief, grads don’t need to have a mastery of software or programming languages to become data literate. Data literacy starts “not from the perspective of the tool, but from the perspective of the data,” says O’Connor. “What information is being collected? What can it be used for? Focus on becoming literate about the concept of data in general. Once you have the basic concepts down, you can explore more complex topics.”
O’Connor adds that data literacy is even more valuable when students combine it with soft skills like communication. “For people who might not be the programmers or machine learning engineers, that’s where you starting to bridge between the art and the science of data,” says O’Connor. “Having people who can communicate about data results and visualize data results is vitally important.” In fact, this skill—known as data visualization or data presentation—was ranked by LinkedIn as one of the most in-demand skills for 2018.
ThoughtSpot Chief Data Evangelist Doug Bordonaro recommends that students begin learning data skills in college. For example, data analysis and statistics courses can help students become familiar with foundational language and data concepts.
In the workplace, grads can ask for access to the company data, Bordonaro suggests. “Get familiar with using data. Whenever a big decision is made, ask to see the data that backs up the decision. If you’re making the decision, ask yourself [what] data you’ve used to arrive at that conclusion,” he adds (Bradford, Forbes, 10/11; Bowley, LinkedIn blog, 1/11/18).
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