Dave Elkington of InsideSales.com explains the role of data analysts, and why cleaning up data takes up the vast majority of their time. Algorithms and computer science play a minor role.
What does a data analyst do? Apparently, a lot of cleaning up.
According to the CEO of InsideSales.com, Dave Elkington, 80pc of the role is cleaning up data, removing false positives and, essentially, getting everything in place.
Algorithms are solid
The algorithms built to support data analytics are largely already in existence, some for decades. What’s needed is a way to maximise their effectiveness.
“A data scientist job is to understand the data, clean the data, write the algorithm and optimise the algorithm,” said Elkington, adding that “there are some misperceptions about this”.
Elkington claims the algorithms are already solid, with the vast majority of employees’ time spent polishing the data. “The rest of the job is about optimising the algorithm, understanding the output, understanding the meaning of the problem.”
Comparing the role to someone improving the running of a car, Elkington said tweaking bits and pieces here and there is the true skill.
“I love the math, but it has been around for a long time. The real value in data science is in the data.”
David Pardoe of Hays Recruitment recently wrote on this topic, agreeing that the most critical aspect of data science is doing things that can result in better, or quicker, decisions being made – not the technology at hand.
The tech sector currently shows incredible growth in data-related fields. Areas such as data science, analytics and fintech are huge, and are changing the face of legacy sectors.
Indeed, Hays listed data scientists and data analysts as some of the most in-demand roles for 2017.
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