In a recent APEX of Innovation post, we examined why data science is the decade’s most desirable job. While this may be true, it doesn’t mean it’s always smooth sailing for today’s data scientists. As companies continue to push for deeper, richer insights to share with a growing variety of stakeholders, it should come as no surprise that many data scientists are feeling overwhelmed.
According to a recent study conducted by Ascend, 96 percent of teams are at or over capacity, with 93 percent of respondents anticipating that the number of data pipelines in their organization will increase between now and the end of the year. There are various solutions companies can investigate to address this workload drain, but chief among them is automation.
In fact, more than half of the respondents in the Ascend study point to automation as the best path forward. Read on to learn more about why automation is critical for companies seeking to leverage data and increase volume and scale.
Automation Handles Mundane Tasks
Mundane tasks such as scrubbing and maintenance often prevent data scientists from focusing on high-level projects that will yield greater insights. When these duties and other routine work are automated, data teams have more time to allocate to visualization and interpretation—ultimately driving better decision-making. In addition, automation eliminates data issues arising from human error, ensuring that data is in its best possible form when it arrives for analysis.
Automation Allows for Scale
Quite simply, automation accelerates results. The technology makes teams more agile which, in turn, means they can take on more projects and significantly reduce the time to ROI. In addition, automation provides a foundation for greater agility. Small data science teams or even single data scientists can develop models and adjust the pipeline to account for changes in direction or new needs.
Automation Helps Teams Weather Disruption
Cloud advances mean that on-premises is no longer a prerequisite for data science. As a result, teams can literally work from anywhere, delivering data-driven insights from the office, the home, the local coffee shop, or any combination of the above.
Of course, automation isn’t a magic pill, and algorithms are ultimately only as good as the people running them. As such, it’s important that companies continue to have human oversight to ensure that things stay on track and that teams can clearly explain results.
For more on how automation can help alleviate issues with overwork among your organization’s data scientists, check out this article in RTInsights.