We\u2019re living in a time when data is generated in enormous numbers\u2014by us, our devices, and the networks that transport it. Think of everyday interactions. Checked your e-mail? Data. Entered your PIN somewhere? Data. Drove your networked car somewhere? Data. Every revolution of a wind turbine? Data. Every phone call through a cell tower? Data. Then flip it to the business side\u2014every time one of those actions was performed, a data center somewhere was collecting, collating, analyzing, and generating insights from it.\nAccording to The 2020 Data Attack Surface Report, total global data storage is expected to exceed 200 zettabytes by 2025\u2014with half of it stored in the cloud. And the number of devices in play is also massive, with the estimated 31 billion Internet of Things (IoT) devices currently in use expected to grow to 75 billion by that same year. The report also suggests that by the end of this decade, 7.5 billion people, or 90 percent of the world\u2019s population over six years old, will be online and generating data.\nIt\u2019s a data-driven world, and as such, the data-driven business is sure to follow. That\u2019s one of the tenets of the Autonomous Digital Enterprise, where manual effort is minimized to capitalize on human creativity, skills, and intellect across the enterprise and businesses learn to continuously examine customer and partner relationships to intelligently create new value.\nWith data such an integral part of the business\u2014and a primary business driver\u2014it\u2019s vitally important to get a handle on and yield value from it now so we\u2019re ready five and ten years out when it increases exponentially. So, how do we do that? What if we could apply agile engineering and DevOps best practices to the field of data management and rapidly turn new insights into actionable deliverables? We can with data operations (DataOps).\nThe current problem\nIn the present data arena, data and analytics transformation, whether it's the older, traditional, siloed data or the more the advanced artificial intelligence-based analytics, is that it hasn\u2019t delivered on its promised value. There can be a number of reasons for this, but one of the biggest reasons is that the insights generated by the data are not translated quickly enough. By some estimates we spend 80 percent of our efforts on data quality or data integration. It takes so long to process and analyze it that when the insights are finally in hand, it\u2019s often too late to act upon them.\nSpeed things up with DataOps\nDataOps can help change that\u2014streamlining processes so that data moves along the pipeline much more quickly, continuously yielding actionable insights and demonstrable value to the business. As more companies recognize that they are in fact a data-driven business, they must also realize they need to change their current, disconnected data processes to leverage it. By implementing DataOps processes and infrastructures, and creating teams that include dedicated data engineers and data scientists, organizations will not only be able to yield insights that drive current and future decision making in near real-time; they\u2019ll also be better prepared to become an Autonomous Digital Enterprise.