The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG).
The ISG Buyers Guides for DataOps, produced by ISG Software Research, predict more than half of enterprises will adopt agile and collaborative DataOps practices by the end of 2026 to enhance responsiveness, avoid repetitive tasks and deliver measurable data reliability improvements.
"As enterprise use of AI moves from initial pilots and trial projects through deployment and into production at scale, many enterprises are realizing the critical importance of agile, responsive data processes," said Matt Aslett, Director of Research, Analytics and Data, for ISG Software Research. "DataOps enables enterprises to effectively monitor the quality of data used in analytics and governance projects and ensure the reliability and health of the data environment."
Healthy data pipelines are necessary to ensure data is ingested, processed and loaded in the required sequence to generate business insights and AI, the report says. As data sources and requirements grow increasingly complex, enterprises are looking to automate and coordinate the creation, scheduling and monitoring of data pipelines as part of a DataOps approach to data management.
Such data orchestration automates and accelerates the flow of data to support operational and analytics initiatives and drive business value. By 2027, ISG says more than half of enterprises will adopt data orchestration technologies to automate and coordinate data workflows and increase efficiency and agility in data and analytics projects.
To fully deliver on the promise of DataOps, enterprises must adopt new approaches to people, processes and information, the report says. Processes and methodologies that support rapid innovation and experimentation, automation, collaboration, measurement and monitoring, and high data quality will improve the value generated by analytics and data initiatives.
"Enterprises need to enable data operation activities across business and IT to improve the agility of data scientists and data analysts in their daily work," said Mark Smith, Partner, ISG Software Research. "Orchestrating and managing pipelines of data to streamline the development of AI requires the efficient processing of data and governance of analytical and operational processes."
