Data Governance Goals
The primary aim of data and analytics (D&A) governance, our research has taught us, is aligning data and analytic content with business objectives, including regulatory requirements and stakeholder expectations. At its core, it is about establishing a framework for the optimal curation, control, and utilization of data to support strategic and operational goals. Jonathan Reichental, in “Data Governance for Dummies,” defines data governance as the practice of managing data well to deliver maximum organizational value. This means ensuring data is consistently available, usable, secure, and trustworthy. For this reason, it involves formalizing roles, processes, and responsibilities for data control and oversight.
As such, D&A governance encompasses the policies, standards, decision rights, procedures, and technologies that govern data and analytic content across the organization. This includes not only structured data and reports, but also emerging assets such as machine learning (ML) and artificial intelligence (AI) models, their algorithms, and the data used to train or tune them. To work, effective governance establishes clear roles for executive sponsorship, steering committees, data owners, stewards, and subject matter experts. It also creates collaboration to ensure data quality, security, appropriate usage, and regulatory compliance. Finally, it standardizes practices for the quality, security, privacy, and lifecycle management of data assets across the enterprise.
The goals of D&A governance include:
- Recognizing and managing data and analytics as corporate assets
- Creating and enforcing common standards and definitions
- Reducing costs caused by duplication, inconsistency, and error
- Minimizing regulatory, security, and reputational risk
- Ensuring analytic content is trustworthy and high-value — including ML and AI components
- Enabling discoverability while controlling and cataloging access appropriately
In short, D&A governance is not simply about control — it’s about creating an environment where data can be leveraged confidently and responsibly to drive business value.
A Complete D&A Governance Platform
It is important to recognize that a complete, end-to-end D&A governance platform does not exist. No single vendor today provides the full spectrum of capabilities needed to support business-focused governance across the entire enterprise. As a result, data leaders need to stitch together their governance platform by integrating tools from multiple vendors — each specializing in different aspects such as data quality, metadata management, policy enforcement, access control, and AI model governance.
Effective Governance Requires People, Process, and Technology
To be effective, D&A governance is not just a technology issue — it’s a disciplined, ongoing practice grounded in people, processes, and technology. Technology alone is not a solution; success depends on well-defined roles, clear processes, and empowered individuals who can drive governance outcomes across the entire organization. If you need more on how to do this, the Data Governance Institute does a great on summarizing how to do this.
Governance efforts should begin with a formal, resourced program aligned to mission-critical objectives. Starting with a focused scope — governing only the most vital data and analytics assets — allows organizations to deliver value quickly. The scope should expand only as needed and be reevaluated if not clearly tied to performance indicators or business value chains. A robust governance framework requires formal ownership structures, including data and analytic product owners, executive sponsors, and decision-makers. It must span the full D&A lifecycle — covering structured and unstructured data, reports, models, and algorithms — and remain business-centric to balance value creation with risk management.
As governance matures, it should align with a unified, business-driven strategy and support a shift toward active data architectures. This evolution depends on enabling technologies that integrate with governance processes and empower people to act on trusted, well-managed data. Without this foundation, scaling D&A efforts will be inconsistent and ineffective.
2025 Dresner Advisory Research Findings
Despite some progress, data and analytics (D&A) content remains difficult to find for most organizations. According to recent research, in 2025, 62% of survey respondents reported at least some difficulty, with 35% describing it as “somewhat difficult” and 28% experiencing even greater frustration. While this marks a modest improvement over previous years, data accessibility remains unresolved for most organizations. The slight gains over previous years are likely due to the broader adoption of data catalog and metadata management solutions, which help index, classify, and expose data assets. However, traditional cataloging alone is insufficient. Many vendors are now integrating AI-driven semantic layers and intelligent discovery features into these platforms. If successful, such agentic AI systems — capable of autonomous reasoning and action — could significantly reduce the time and effort required to locate trusted, contextually relevant data.
Addressing data discovery challenges will require more than technology. Improved governance, better metadata practices, and data literacy are essential to ensure data remains findable, usable, and valuable. Yet formal D&A governance remains limited. In 2025, only 41% of organizations reported having a formal governance body — up from 32% the prior year, but still not widespread. Adoption is largely driven by regulation: 50% of compliance-focused firms and 63% of very large enterprises have governance programs, as do most financial services and healthcare organizations. In contrast, retail lags significantly, with only 11% reporting formal governance.
This uneven adoption reveals a gap between the recognized strategic value of governance and actual organizational investment. Informal or fragmented efforts often lack the consistency and authority needed to drive enterprise-wide impact. The link between governance and BI success is clear: organizations with underperforming BI initiatives are far more likely to lack formal governance or rely on ad hoc teams without clear mandates. In these groups, 33% reported using virtual teams composed of individuals with secondary responsibilities. Conversely, organizations with more successful BI programs were significantly more likely to have adopted a distributed governance model — embedding formal roles across business units. These organizations also demonstrated a more balanced governance approach, supplementing formal structures with informal contributions from dedicated individuals where necessary.
Despite rising demands and the growing influence of AI, most organizations have yet to broaden their governance scope. Governance efforts still focus primarily on analytical data, master data, operational data, and reports — all cited by over half of respondents. However, newer analytic assets like ML models and AI algorithms remain largely ungoverned. As AI becomes more pervasive, this blind spot could pose serious risks to enterprise oversight and performance.
Across the organizational activities surveyed, the importance of D&A governance is clear: All ten activities received over 80% of responses rating them as “critical,” “very important,” or “important,” with seven exceeding 90%. Leading the list, “data and analytics quality” was universally recognized — receiving no responses below “important” — and was most frequently marked as “critical.” Close behind was “controlled access to data appropriate to role,” also recording over 50% of responses as “critical.”
These results reaffirm consistent findings across other surveys: Sata quality and security remain the top priorities in data and analytics. They also define the core scope of effective D&A governance programs — where quality and controlled access form the foundation for trusted, secure, and actionable insights. The full distribution of responses is shown in the chart below.
A well-rounded D&A governance program, according to the research, should prioritize four core features: security, quality assurance, privacy, and the governance of data models. Together, these elements ensure that data is protected, reliable, compliant, and used responsibly in analytics and AI. As organizations scale their data initiatives, these pillars become essential for building trust, enabling innovation, and supporting informed decision-making.
Parting Words
The rise of generative and agentic AI demands a D&A governance foundation that is both robust and adaptable. Our research shows that organizations view data quality and controlled access as the most critical enablers of success, with security, privacy, and the governance of data models rounding out the essential components. These priorities reflect a broader shift: to realize the full potential of AI, enterprises must move beyond hype and ground their strategies in proven data practices and thoughtful integration of human and virtual agents. By aligning governance with emerging capabilities, organizations can unlock meaningful business value while maintaining trust, compliance, and control.
