DataOps is a collaborative practice to improve data analysis and management. It improves the speed, quality and agility of data analysis. It is constantly being developed as an independent approach to data pipelines and architecture. Similar to DevOps silos are broken down here too, facilitating collaboration between data scientists, entrepreneurs and software developers so that the organisation's data is used in the most effective way to achieve the desired business outcomes.  

The information gained with the help of DevOps is used to optimise the data 
management using automated systems that match the AI era and maximise high data volumes. The software supports a wide range of open source tools, from the moment data is created to the moment it becomes obsolete, with a focus on using high volumes of data to add value to businesses. This approach incorporates multiple technologies and data practices into a single integrated environment. Progress benchmarks are used throughout the data lifecycle and a significant part of the process is automated through business intelligence platforms and designs for growth and scalability.  

DataOps improves the communication of data flows between business people, data analysts and other stakeholders to add value faster while improving the usability of data in a dynamic environment. The software includes micro services architecture tools, data curation tools and open source software such as MapReduce, which can be used to combine unstructured and structured data. DataOps simultaneously builds new code and monitors data analysis pipelines via SPC to quickly respond to anomalies and increase data processing efficiency.