Analyzing Big Data: Key Traits of a Good Analytical Worker


To be competitive, organizations have to make sense of all that structured and unstructured that’s flowing into the business every day.

So companies are investing in business intelligence, analytics, and data science to turn all that data into actionable insights, according to an article in InformationWeek.

But as the article points out, corporations need more than just technology – they also need the right people and processes.

While it’s true that more employees are analyzing big data as part of their jobs, not everyone has to be a data scientist. However, increasingly organizations are looking to hire people who are analytical thinkers, according to InformationWeek.

To help hiring managers determine which potential new hires are analytical by nature, InformationWeek has identified some of the key traits of an analytical mind when analyzing big data.

For one thing, people with analytical minds are naturally curious. They want to know what’s going on beneath the surface. Analytical thinkers are also able to break down the myriad problems that businesses face into the components that affect those problems.

Although the left-brain/right brain theory indicates that left-brainers tend to be more analytical, better at math, and more logical than their creative right-brained counterparts, “analytics is not limited to the realm of left-brain dominant people, and for good reasons,” according to InformationWeek.

In fact, right-brainers tend to be good at visualization. Lavastorm data scientist Kwan Lin tells InformationWeek that left-brained people may be better at analyzing structured databases, while right-brained individuals may be better at analyzing unstructured data such as graphics, geospatial shapes, or bodies of text.

According to the article, people who want to be analytical should be “constantly hungry and motivated to figure things out, and not stop until things make sense.” Additionally, those people should have open minds and be willing to change their assumptions if faced with new evidence.