A comprehensive 2016 Data Science Report released by Crowdflower, a crowdsourcing company and data enrichment platform, revealed that, although data scientists felt that a large portion of their work was menial and least preferred, they still loved and enjoyed being in their jobs.

Over 80 data scientists were surveyed who encompassed varying levels of work experience.

According to the new report, most of the data scientists' time is spent on just cleaning and organizing data prior to the more important and enjoyable bit of processing the data. Sixty percent of their time is spent as "digital janitors."

Fifty-seven percent of the scientists claim that this whole "janitoring" bit is what they find least enjoyable in their job, but this non-enjoyable, time-consuming work is what their job largely encompasses.

Generally, we presume that data scientists are ideally spending their time doing analytical tasks such as building algorithms, training sets and mining for patterns, but sadly, only nine percent of their time is devoted to pattern mining and a mere four percent to algorithms.

"You have your hardest-to-hire resource spending most of their time cleaning data. It's a humongous waste for organizations," said Lukas Biewald, CrowdFlower's cofounder and CEO.

Despite this, the oddity is that these data scientists are, at a holistic level, immensely happy with their jobs. According to the 2016 report, over 80 percent of data scientists are happy in their work, with 35 percent of the respondents giving it a five on a scale of 1 to 5, and 47 percent giving their jobs a four.

Furthermore, one of the key takeaways from the report is that there is a growing shortage of data scientists, with 83 percent of the scientists claiming the same. They say that there aren't enough data scientists to go around.

The survey also determined some of the significant skills that are in high demand and particularly required for the present generation of aspiring data scientists. Scientists are expected to be adept at SQL, Hadoop and Python, reveals the Data Science Report.

"As more and more organizations adopt data as a key driver of decision making, the importance of streamlined, well-oiled data science teams is going to remain paramount. But the current status quo probably isn't sustainable," the report concludes.

"On the one hand, we see a shortage of data scientists while on the other, they're spending too much time cleaning and munging data. This is time that could be much better served doing predictive analysis and building out machine learning practices," 

Photo: Ken Teegardin | Flickr