Across industries, organizations must harness the immense potential of data to drive decision making and innovation or lose ground to their data-savvy competitors. For the last decade-plus, most enterprises have sunk significant time and resources into digital transformation initiatives chasing this goal.
Infrastructure has been moved to the cloud, processes have become agile, and organizational structures, roles, and goals have changed. Where data leaders once focused on protecting data at all costs, they now face increasing pressure to drive tangible value from data.
To fully realize this mission, data leaders must enlist and empower their data engineering teams. Over the past decade, StreamSets has seen firsthand the incredible impact data engineers haveon deriving value from data quickly and at scale. But it’s also clear that plenty of data engineers out there—and their business counterparts—don’t yet understand the full potential of their work.
As a catch-all data role in many organizations, data engineers are used to rolling up their sleeves and getting stuff done. They build the underlying infrastructure and pipelines to deliver data to downstream users smoothly and lay the groundwork for analytics. Though not flashy, their work enabling data science and analysis is crucial. And the more data engineers understand, the better business outcomes are.
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