DataOps

[4] DataOps incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals.

[3] DevOps focuses on continuous delivery by leveraging on-demand IT resources and by automating test and deployment of software.

[6] Automation streamlines the daily demands of managing large integrated databases, freeing the data team to develop new analytics in a more efficient and effective way.

[12] It emphasizes communication, collaboration, integration, automation, measurement and cooperation between data scientists, analysts, data/ETL (extract, transform, load) engineers, information technology (IT), and quality assurance/governance.

Toph Whitmore at Blue Hill Research offers these DataOps leadership principles for the information technology department:[2]