DataOps, short for data operations, is a term for an agile, process-oriented way to organize, deliver, and leverage data analytics. It is a group activity that aims to improve the communication, integration, and automation of data flows across an organization.
By speeding up the design, development, and maintenance of applications based on data and how it is used, DataOps aims to deliver value more quickly in a fast-moving business world. In addition, it addresses the problems of low-quality data and not being able to use the available data.
Even though the name DataOps comes from DevOps, it is not just data analytics for DevOps. Instead, it arranges, manages, and monitors data pipelines in a way similar to the lean manufacturing process, where operational statistics should always be in the right ranges. This helps to accelerate understanding and implementation of data-driven insights. Otherwise, data analysts might notice that the data isn’t normal or isn’t correct.
With DataOps, there is no specific software—only frameworks and toolsets that support the approach to collaboration and increased agility in delivering high-quality data. Below are a few best practices in implementing a DataOps methodology:
Build the Right Team: Form a cross-functional team by incorporating data experts among developers and operations professionals to ensure collaboration and communication. Working together allows them to share a common goal and design a solution that addresses a particular need or problem.
Examine Your Data Architecture: Raw data comes in large volumes, but how confident are you with its quality? Cleaning data is crucial, which is why you should have an infrastructure that makes data ready for use. Develop or invest in tools essential for data governance and integration.
Monitor Constantly: Reduce the risk of poor quality data and faulty systems with automation. Automated applications and tools can monitor bottlenecks and data silos, alerting data analysts of errors. Don’t forget to define semantic rules for data and metadata, set progress benchmarks and performance measurements, and include feedback loops for data validation.
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