DataOps: Drivers, Challenges and Adoption Trends

As enterprises continue to accumulate data at increasingly large volumes and speeds, they are looking for ways to effectively and efficiently leverage that data to drive value for the organization. This has led to the emergence of DataOps–an automated, process-oriented methodology used by analytic and data teams to improve the quality and reduce the cycle time of data analytics. Despite the obvious and growing need for a discipline like DataOps across many enterprises, a majority of companies have yet to really embrace the practice. Join Issam Hijazi to learn more about DataOps drivers, challenges and adoption trends.