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More Than Half of Enterprises Will Embrace DataOps by 2026

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."

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More Than Half of Enterprises Will Embrace DataOps by 2026

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."

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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