Paige Bartley is a senior research analyst at 451 Research from S&P Global Energy Horizons. She leads data management research within the 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 also specializes in data privacy operations (PrivacyOps) and data governance, 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 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. Paige holds a BS in psychology and neuroscience from Duke University and a Master of Management (MMS) from the Duke University Fuqua School of Business.
Age-restricted industries and content are not new, yet the internet has made content infinitely more scalable and accessible to those who may easily evade "traditional" age verification. A surge of online age-verification legislation in the US is putting pressure on providers and threatening to erode consumer trust and loyalty.
Historically, data governance and privacy practices were often siloed and highly policy-oriented. Today, these disciplines demand intensive involvement and decision-making from technical roles to align with AI initiatives. As a result, there is a broad shift underway, transitioning from reactive, requirement-focused approaches to frameworks that support consistent and safe value generation from business data using AI tools and models. Multinational organizations often face distinct challenges compared to peers operating on a local or national level, yet nearly all are undertaking extensive efforts to tighten control over sensitive and proprietary business data, increasingly via sovereign cloud architecture.
Data privacy and data governance regulations have evolved rapidly around the world since the EU's General Data Protection Regulation was first announced in 2016. But the baseline pressure to comply with these regulations has somewhat fractured the business landscape. Some companies operate locally or within a single nation, while others operate multinationally. Often, this distinction can tell us more about data governance and privacy behavior than other traditional measures of business size or scope.
Voice of the Enterprise: Data & Analytics provides you with actionable data and insight to understand the key dynamics driving the information technology market.
The Data Governance & Privacy 2026 survey tracks the behavior and technology practices associated with the organizational effort to maximize data-driven value while minimizing data-derived risk.
This online survey of 650 qualified respondents was conducted from November through December 2025. Approximately 200 respondents saw the full Data Governance and Privacy survey section. The margin of error for top-line statistics is +/- 3 points at the 95% confidence level.
Data governance and privacy practices poised to support enterprise AI (Customer Voice report)
Historically, data governance and privacy practices were often siloed and highly policy-oriented. Today, these disciplines demand intensive involvement and decision-making from technical roles to align with AI initiatives. As a result, there is a broad shift underway, transitioning from reactive, requirement-focused approaches to frameworks that support consistent and safe value generation from business data using AI tools and models. Multinational organizations often face distinct challenges compared to peers operating on a local or national level, yet nearly all are undertaking extensive efforts to tighten control over sensitive and proprietary business data, increasingly via sovereign cloud architecture.
A tale of two companies' data governance behaviors – Highlights from VotE: Data & Analytics (Spotlight)
Data privacy and data governance regulations have evolved rapidly around the world since the EU's General Data Protection Regulation was first announced in 2016. But the baseline pressure to comply with these regulations has somewhat fractured the business landscape. Some companies operate locally or within a single nation, while others operate multinationally. Often, this distinction can tell us more about data governance and privacy behavior than other traditional measures of business size or scope.
Crosstabs (Excel tables | Download link in sidebar to the right) — Crosstabs of complete worldwide and regional survey results datasets, including key metrics and segmentation.
Charts and Figures (PowerPoint charts | Navigate charts in sidebar to the left | Download link in sidebar to the right) — PowerPoint slide deck of complete top-line survey results in charts and figures.
Data Insights Deck (PowerPoint charts | Coming soon) —PowerPoint slide deck of advanced charts supporting deeper analysis and insight.
Tool fragmentation has made it difficult to achieve the low-latency Iceberg data pipelines that enterprises seek. This is where Etleap looks to differentiate, helping create managed, Iceberg-native data pipelines that live entirely within the customer's cloud.
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.
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