Enterprises are shifting investment priorities toward data management software to solve the usability and governance hurdles preventing large-scale AI deployment. New research from Information Services Group (ISG) indicates that as organizations move from experimental AI projects to full-scale production, the demand for high-volume, high-quality, and "fresh" data for model training and inference is escalating. This strategic pivot suggests that the ability to scale generative and agentic AI is increasingly dependent on the underlying data infrastructure rather than just the AI models themselves. Consequently, companies are prioritizing tools that ensure data integrity, observability, and automated processing to mitigate the risks of inaccurate or poorly organized information.
The Shift Toward DataOps and Governance
The transition to AI-enabled operations is forcing a re-evaluation of how data is processed and governed. According to ISG, enterprises are increasingly viewing data usability as a primary obstacle to production-ready AI. To address this, many organizations are adopting DataOps—a set of practices and tools inspired by DevOps—to enable more agile, automated, and continuous data integration and orchestration. This approach allows for more flexible testing and deployment of data pipelines while incorporating stakeholder feedback into the process.
Governance is also emerging as a central function within the data management stack. As companies pursue agentic AI and advanced analytics, the risk of using outdated or inaccurate data increases. ISG research highlights that robust governance acts as a set of guardrails, helping organizations protect data and maintain regulatory compliance while attempting to move quickly. Furthermore, the adoption of master data management and data intelligence platforms is helping enterprises establish a "single version of the truth," which is necessary for democratizing data access across various business units and analysts.
ISG 2026 Buyers Guide Provider Rankings
To assist in these procurement decisions, ISG released its 2026 Buyers Guides for Data Management and Operations, evaluating 68 software providers across five distinct categories. The research assessed products based on five dimensions: Overall, Product Experience, Capability, Platform, and Customer Experience. The findings identify several "Leaders" based on how well they meet specific evaluation criteria within their respective technology segments.
In the Data Management and Operations category, Databricks was named the top Overall Leader, followed by Pentaho and IBM. Databricks also secured the top Overall Leader position in the Data Intelligence and Data Products guide, as well as the Data Engineering guide. In the Data Quality and Data Observability segment, Acceldata emerged as the top Overall Leader, with Pentaho and Databricks following. For Data Integrity, Salesforce was identified as the top Overall Leader, ahead of IBM and SAP. These rankings reflect a highly competitive landscape where providers like IBM and Pentaho appear frequently across multiple categories, signaling their broad reach in the enterprise data ecosystem.
Key Takeaways
- ISG predicts that two-thirds of enterprises will utilize agile and collaborative data practices to achieve faster AI adoption through 2028.
- Databricks was named the top Overall Leader in three of the five ISG Buyers Guides: Data Management and Operations, Data Intelligence and Data Products, and Data Engineering.
- The 2026 ISG Buyers Guides evaluated a total of 68 software providers across categories including Data Quality, Data Observability, Data Integrity, Data Intelligence, and Data Engineering.
TechInsyte's Take
In our view, this research underscores a fundamental reality for the enterprise: the "AI gold rush" is hitting a data bottleneck. While much of the industry focus has remained on model capabilities, the ISG findings suggest that the real competitive advantage is shifting toward the plumbing. The move toward DataOps and the emphasis on "data freshness" for inference indicate that the era of static, batch-processed data is insufficient for the requirements of agentic AI.
This signals that CIOs can no longer treat data management as a back-office utility; it is now a core component of the AI deployment strategy. The high frequency of providers like Databricks and IBM in the "Leader" rankings suggests that enterprises are gravitating toward integrated platforms that can handle the entire data lifecycle. For decision-makers, the takeaway is clear: scaling AI requires solving for data usability and governance before the models ever reach production.
Questions & Answers
How does the move to full-scale AI deployment change enterprise data requirements?
Scaling AI from small-scale projects to full deployments increases the necessity for higher data volumes for model training, greater data "freshness" for accurate inference, and higher data quality to ensure reliable outputs.
What is the strategic role of DataOps in an AI-driven enterprise?
DataOps provides a set of tools and practices for the continuous processing and delivery of data in changing environments. It enables more agile management through automation and provides a flexible environment for the development, testing, and orchestration of data integration pipelines.
Why is governance becoming a critical component of data management software?
As enterprises accelerate analytics and agentic AI, governance provides necessary guardrails by cataloging and protecting data. This helps reduce risk, supports regulatory compliance, and prevents the degradation of decision-making caused by outdated or inaccurate information.
Which providers were identified as leaders in the Data Integrity category?
Salesforce was named the top Overall Leader in the Data Integrity Buyers Guide, followed by IBM and SAP.
Source: Businesswire
