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DataOps: Unlocking the True Value of Enterprise Data
Many organizations are on a constant quest to squeeze every drop of value from their massive data troves. Enter DataOps—a game-changing approach to data management that’s shaking things up. By boosting communication, integration, and automation of data flows between data managers and consumers, DataOps is helping businesses tackle long-standing challenges in their data value chains.
At its heart, DataOps blends the best of software engineering, data engineering, and cultural change to build trust in data and speed up analytics delivery. As Ted Friedman, Distinguished VP Analyst at Gartner, puts it, “The point of DataOps is to change how people collaborate around data and how it is used in the organization.” This is especially crucial as companies strive to make faster, smarter decisions in an ever-changing landscape.
Companies diving into DataOps are reaping big rewards. According to McKinsey, organizations can boost the volume of new features by 50% through data automation, enabling quicker development cycles. Plus, by automating repetitive tasks, companies can slash the time spent on data prep from 80% to just 20% of total project time.
The benefits of nailing DataOps are huge. It not only enhances data quality and governance but also cuts operational costs by ditching manual tasks and optimizing resources for more valuable activities. As data volumes keep growing, DataOps makes scalable data operations possible without breaking the bank.
Likely, in the near future, DataOps will further streamline and automate end-to-end data pipeline operations. With AI and machine learning technologies advancing, we can expect even more sophisticated DataOps tools and practices to emerge. These advancements could be just around the corner, with early adopters already seeing impressive gains.
