Big data rules.
These days, the smartest companies are the ones making effective use of this wave of newfound intelligence – in other words, building a more “data-driven organization.” Data science can be a fascinating, fuzzy field. It’s tricky to tell who does what in the grand scheme of the data-driven org. Defining what that means can also be tricky.
Here’s how DJ Patil, former head of LinkedIn's data and analytics team, breaks it down:
A data-driven organization acquires, processes, and leverages data in a timely fashion to create efficiencies, iterate on and develop new products, and navigate the competitive landscape.
For many companies, one of the first steps to elevating their strengths is hiring a data team comprised of a mix of data scientists, data analysts, and data engineers.
Given data science and its corresponding roles are fairly new, some recruiters and hiring managers may have a hard time differentiating between the types of data specialists – people whose jobs require them to know what type of information to collect, to gather, process, and sift through the data, and to report on the findings.
Having trouble distinguishing the three? In the first our two-part series, we compare the roles of a data analyst and a data engineer.