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Data Quality: Data Reliability Engineers
The role that data plays in an organization is becoming ever more important. Gartner estimates that poor data quality can be expensive and they assign a cost of $12.9 million as the amount lost by an average company due to poor decisions and opportunity loss.
As a result, data quality has become a top priority for many organizations, and to help enable this goal, dedicated roles for data reliability engineers are being created. These roles often adopt best practices established by other IT and software development groups within the organization that include ideas like continuous monitoring, SLAs, change management, incident management, and tracking.
Egor Gryaznov, CTO at Bigeye, said that “I see Data Reliability Engineering as a natural extension of the data team. … Data Reliability Engineering means treating data quality like an engineering problem. It’s applying applications and tools to see that data stays for the variety of application use across the business.”
Kyle Kirwan, CEO of Bigeye, said that “Data Reliability Engineers work to improve data quality, keep data moving on time, and ensure that analytics and machine learning products are fed with a healthy set of inputs.”
Shoaib Mohammad, founder at Lumiq.ai, said that “a DRE-focused approach removes repetitive manual processes, resulting in reduced overhead and fewer human errors. Unlike data scientists, data reliability engineers monitor the data at a much deeper level, thus leading to higher reliability. Automation reduces manual mistakes, manpower, and time for tackling higher-order problems. A DRE-focused approach helps remove complexity to a great extent and minimizing complexity leads to increased reliability.”
