Research Objectives
The need for rapid insight is forcing organizations to prioritize agility, transparency, and speed across their data ecosystems with a goal of improving operational efficiency, improving collaboration, and accelerating time to value from investments in support of data-driven initiatives. But organizations need help ensuring seamless orchestration, appropriate management, and timely delivery of data in support of the people, tools, processes, and environments that fuel their business. Between data quality issues, distributed data, tool proliferation, overburdened and under-skilled teams, rising costs, and increased risk, the complexity of today’s data ecosystem hinders democratization of data and analytics. This is a big reason why organizations are turning to DataOps—an agile, automated, and process-oriented methodology used by data stakeholders to improve the quality, delivery, and management of data and analytics. And the wide belief is that establishing DataOps will set organizations up for success as they look to achieve a data-driven future through an agile, process-oriented approach to securely accessing and analyzing data at scale.
