Paige Bartley is a senior research analyst at S&P Global Market Intelligence. She leads the data management research within the 451 Research Data, AI & Analytics channel and frequently contributes to the Information Security channel. Covering the technologies that compose the “plumbing” of the data and analytics stack, Paige examines the flow and control of data within organizations, as well as how organizations can better align their business functions and technology. Paige came to S&P Global Market Intelligence through its 2019 acquisition of 451 Research, which she joined in 2018. Prior, she held a senior analyst role with Ovum (now Omdia), researching similar topics in enterprise data management and analytics. She began her career on the vendor side, working in marketing and strategy for an unstructured data governance specialist. Data privacy operations (PrivacyOps) and data governance are areas of specialty, particularly regarding how these initiatives can be linked to customer experience and profitability outcomes. Other areas of concentration include AI, data integration, metadata management, data quality and data observability, and master data management. Broader concepts include enterprise data culture, digital rights and data ethics. Paige holds a BS in psychology and neuroscience from Duke University and a Masters of Management (MMS) from the Duke University Fuqua School of Business.
In the GenAI era, businesses are prone to store and retain sources of data that they might not need for business purposes, creating a conflict of interest for data security and privacy efforts. To address this, Ardent Privacy helps customers identify sensitive data sources, establish data-centric AI governance and reduce risk.
Consent is a fundamental legal mechanism for data collection and use. In Part 1, we explored the history and challenges of current digital consent models. Here we shift to focus on innovations in digital consent technology, the future of consent and possible alternatives to current consent models.
The company aims to provide more consistent visibility into compliance and risk posture through a "continuous controls monitoring" approach, enabling organizations to accelerate compliance attestation and shorten sales cycles with other regulated firms.
The use of proprietary organizational data and intellectual property is foundational for accelerating outcomes in enterprise AI, but it must be managed appropriately to maintain quality, relevance, governance and privacy. Businesses seek to manage data actively, using various automation techniques, but do so with caution to avoid inadvertently leaking or sharing information with unauthorized parties and models. Yet, while data management and DataOps functions are vital to outcomes in leveraging AI, many organizations’ efforts still lack dedicated leadership.
This report highlights key topics for 2026 in data, AI and analytics. Generative AI continues to permeate all aspects of the modern IT stack, supporting functions that rely on high-quality data, user-friendly tools and robust infrastructure. While some organizations continue to experiment with GenAI, an increasing number are moving into full-scale adoption and considering the business impacts and outcomes of their GenAI investments.
The cloud sector continues to transform as hyperscalers, managed service providers, global systems integrators, hardware vendors and independent software vendors advance overlapping visions for IT infrastructure and application modernization enabled by automation and powered by the disruptive promise of generative AI. Developer services, integration, automation and business processes take on new importance as modern enterprise IT environments mature and we transition from the "era of cloud" to the "era of AI."
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