As more and more of our lives and businesses become digital, we’re continuously generating exponentially more data. How much? Well, Statista expects that we’ll generate 97 zettabytes of data in 2022 and almost double that to 181 zettabytes by 2025.
Organizations that know how to mine that data for valuable insights are the leaders not just in digital transformation, but also ensuring future success. But only 15 percent of big data projects make it to production. Businesses are also facing hurdles in successfully implementing DataOps, which is the application of agile engineering and DevOps best practices to the field of data management. DataOps enables businesses to rapidly turn new insights into fully operationalized production deliverables that unlock business value.
By managing and integrating data successfully, organizations can adjust, respond, and even predict and act autonomously to what is happening in the business, the industry, the market, or the world. DataOps brings together DevOps teams with data engineers, data scientists, and analytics teams to accelerate how data is collected, used, and analyzed, and determine where it gets applied.
In a new Intellyx white paper sponsored by BMC, Why DataOps is the Missing Piece of Your Cloud Migration Puzzle, analyst Charles Araujo explores the complexities of including DataOps in cloud migration planning. In the paper, he addresses the speed with which companies are adopting cloud infrastructures and the complication of including modern, data-driven applications in those plans when so much data still resides in traditional environments at the core of the enterprise technology stack.
The always-on world and its modern applications require on-demand and real-time data. Organizations migrating to cloud must find a balance between cloud-based, data-driven applications and traditional data sources by managing the flow of this new data pipeline and workload.
Araujo adds that modern applications will need to understand that data may not be available on demand, while traditional sources of data must have a way to make data readily available to, and align with, the needs of those new applications.
The dance necessary to pull this off—and the foundation for DataOps—is application and data workflow orchestration. Taking the traditional and long-proven approaches to data workflow orchestration and wrapping them in a set of new perspectives and technologies such as automation, intelligence, and analytics is integral to a DataOps strategy. By doing so, organizations gain visibility across the entire spectrum of the enterprise data pipeline, and improve its manageability in ever-changing, modern environments.
Download the full white paper, Why DataOps is the Missing Piece of Your Cloud Migration Puzzle, to learn more about successfully including DataOps and data workflow orchestration in your cloud strategy.