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ACTIVE DATA GOVERNANCE SERIES
Part 2: What Active Data Governance Actually Means
Active Data Governance Series
Part 2 of 3
Series Context
Strategic Context
Welcome to Part 2 of this three-part executive thought leadership series on Active Data Governance. As senior executives and data leaders, you navigate the complex operational realities of governing data in increasingly AI-driven environments. This series constructs a rigorous argument for a fundamentally different approach to data governance, with each installment building directly on the insights of the last.
What Part 1 Established
Part 1 exposed a critical gap in how most organizations currently approach data governance. It proposed that traditional governance delivers visibility without operational confidence. Organizations achieve the ability to find data, describe it, and assign responsibility, yet they remain unable to confidently act on it at the point of use. Governance, as widely practiced, ceases at awareness and falters at runtime enforcement, characterized by extensive documentation but limited execution.
This foundational argument underpins our exploration. Part 2 builds directly on this premise, defining a governance model that operates beyond mere awareness and demonstrating why this distinction is critical in the age of AI.
For the full argument presented in Part 1, visit: data-tiles.com/blog-78-active-data-governance-series-part-1
Series Architecture
01
Part 1: The Problem
Why traditional governance creates visibility without operational confidence
02
Part 2: The Definition
What Active Data Governance actually means and why it matters now
03
Part 3: The Practice
How organizations make Active Data Governance real
This discussion maintains continuity with Part 1 in tone, structure, authority, and visual identity. The intellectual positioning is deliberate and precise. Our goal is not to simplify complex realities, but to clarify them with the rigor that senior data leaders expect and deserve.
Part 2 Begins
Active Data Governance Series
Part 2: The Definition
This installment shifts our focus from the challenges to the solutions, establishing precisely what Active Data Governance entails and why this critical distinction matters for the modern enterprise. We move beyond theory to define what it truly means to operationalize data governance effectively.
Context Reset
Part 2 — What Active Data Governance Actually Means
In Part 1, we surfaced an uncomfortable reality:
Most data governance programs stop at awareness.
BARC's Data Governance Survey consistently shows that while organizations improve cataloging and visibility, they continue to struggle with trust, usability, and operational adoption of governed data.¹ This is not a fringe finding. It reflects the dominant pattern across industries and geographies. Organizations invest heavily in governance infrastructure, build catalogs, assign stewards, write policies, and then discover that the gap between knowing about their data and confidently using it remains stubbornly wide.
They help organizations find data, describe it, and document responsibility, but fall short of enabling confident, real-world use. The catalog is populated. The lineage is mapped. The glossary is maintained. And yet, when a business user needs to act on data at the point of decision, governance provides no operational assurance that the data is fit, compliant, and safe to use in that specific context.
The missing link is not more policy. It is not more metadata. It is not another layer of documentation.
Forrester has repeatedly noted that many governance initiatives remain documentation-centric, focused on artifact management rather than embedded control.² This observation cuts to the heart of the problem. Organizations have conflated governance activity with governance effectiveness. The volume of documentation produced has become a proxy for maturity, when in reality, it often masks the absence of operational enforcement.
What's missing is governance that operates when data is actually used.
This is where Active Data Governance begins.
Governance that cannot operate at the point of data use is governance in name only.
Defining the Shift
Moving Beyond the Word "Active"
"Active" is an easy word to misuse. The governance market is littered with rebranding exercises that apply new language to old approaches. It is therefore essential to be precise about what Active Data Governance is and, equally importantly, what it is not.
Not Faster Documentation
Accelerating the production of governance artifacts does not make governance active. It makes it faster at being passive.
Not Real-Time Dashboards
Dashboards that report on governance metrics in real time are still retrospective. They show what happened, not what should happen next.
Not a New Interface
Layering a modern UI on top of existing governance tools does not change the underlying model. It changes the experience of a fundamentally limited approach.
Not More Frequent Governance
Performing governance activities more often does not make them active. Frequency is not the same as operational integration.
Active Data Governance is a fundamental shift in where governance lives and how it behaves.
Gartner's research on active metadata and continuous intelligence highlights this shift, from static governance repositories to operational, embedded control mechanisms within data workflows.³ The distinction is architectural, not cosmetic. It is the difference between a governance system that observes and one that participates. Between a system that records and one that decides.
Traditional governance exists around data. Active governance exists within data usage.
Paradigm Shift
From Passive Oversight to Real-Time Decisioning
Passive governance answers questions after the fact:
  • What data was accessed?
  • Who accessed it?
  • Did that violate a policy?
This retrospective model mirrors traditional audit-based controls described in regulatory frameworks such as BCBS 239, which focus heavily on reporting and aggregation accuracy, but not necessarily real-time enforcement.⁴ These frameworks were designed for a world where data moved more slowly, where human review could reasonably keep pace with data consumption, and where the primary risk vector was inaccuracy in periodic reporting rather than misuse in automated systems.
Active governance answers a fundamentally different question:
Should this data be used — right now — in this context?
The NIST AI Risk Management Framework (2023) makes clear that risk must be managed continuously throughout AI system operation, not only during design or review.⁵ This is not a subtle recommendation. It is a structural requirement that reflects the reality of how modern data systems operate. Data decisions are made in milliseconds. Governance must operate at the same tempo or accept irrelevance.
Passive Governance
Asks: "What happened?"
Retrospective. Audit-oriented. Report after the fact.
Active Governance
Asks: "Should this happen?"
Real-time. Decision-oriented. Enforce at the moment of use.
That distinction is everything.
Operational Scope
Governing Data-in-Use, Not Just Data-at-Rest
Most governance controls are applied when data is created or classified. This is understandable from a historical perspective. When governance programs were first established, the primary concern was ensuring that data was properly cataloged, labeled, and stored according to organizational standards. But the data landscape has shifted dramatically. Data is no longer a static asset that sits in a warehouse waiting to be queried by analysts. It is a dynamic resource consumed by APIs, automation engines, AI models, and cross-domain workflows at a pace that far exceeds the design assumptions of traditional governance.
IDC's research on AI governance maturity highlights that many organizations apply controls primarily at ingestion and classification stages, leaving execution contexts under-governed.⁶ This finding is both predictable and alarming. It means that the moment data moves from rest to use, governance effectively disappears.
Where Active Data Governance Applies Controls
When data is queried
Every query represents a decision to use data. Governance must evaluate that decision in context.
When datasets are combined
Combining datasets can create new risks that neither dataset carried individually. Governance must assess the combination.
When data is exposed through APIs
API exposure extends data beyond organizational boundaries. Governance must enforce controls at the point of exposure.
When data is fed into automation or AI
AI and automation consume data without human judgment. Governance must provide the judgment that machines cannot.
When insights are shared beyond their original domain
Cross-domain sharing changes the context of use. Governance must assess whether the new context is appropriate.
The EU AI Act reinforces this operational requirement, mandating ongoing monitoring and risk management of AI systems during deployment and use, not merely at development stage.⁷ The regulatory trajectory is clear: governance must follow data into execution, not wave goodbye at the warehouse door.
Governance becomes part of execution, not a review step.
The Control Plane
Context Is the New Control Plane
Active Data Governance is driven by context, not static rules. This is perhaps the most profound shift in the entire framework. Traditional governance operates on fixed permissions: you either have access or you don't, based on your role, your department, or your security clearance. This binary model was sufficient when data environments were simpler and use cases were predictable. It is wholly inadequate for the complexity of modern data operations.
Gartner's research into risk-based and context-aware access models underscores that modern control systems must incorporate user, purpose, and behavioral context, not just role-based permissions.⁸ The question is no longer simply "Can this person access this data?" It is "Should this person access this data, for this purpose, at this time, in combination with this other data, with the output going to this destination?"
Who
Who is requesting the data? Identity is necessary but insufficient. The same person may warrant different access depending on every other contextual factor.
Why
Why are they requesting it? Purpose drives risk. A marketing analyst querying customer data for segmentation carries different risk than the same query for an AI training set.
How
How will the data be used? The method of consumption, whether direct query, API call, model training, or dashboard, changes the governance calculus.
What Else
What other data will it be combined with? Data that is safe in isolation can become sensitive or non-compliant when combined.
Where
Where will the output be consumed? Internal dashboards, external reports, third-party systems, and AI models all represent different risk contexts.
This aligns closely with Zero Trust architectural principles outlined by Forrester, where trust decisions are dynamic and contextual rather than predefined and permanent.⁹ Two people accessing the same dataset may receive different outcomes, and that is by design. One may see the full dataset; another may see an anonymized or aggregated version. The governance system makes that determination in real time based on the full context of the request.
Governance adapts dynamically to intent.
The AI Imperative
Why AI Makes Passive Governance Obsolete
AI systems don't interpret policy documents. They don't understand "intent" unless it is encoded. They don't pause to ask for approval. This is not a limitation to be worked around. It is the fundamental nature of how AI operates, and it renders passive governance not just inadequate but actively dangerous.
McKinsey's State of AI research makes clear that AI systems scale decisions faster than human supervision models were designed to handle.¹⁰ The speed and scale at which AI consumes data, generates outputs, and propagates decisions through downstream systems means that governance gaps are amplified exponentially. A policy violation that might take a human analyst weeks to commit can be replicated by an AI system across millions of transactions in minutes.
They consume data at scale, at speed, and without judgment.
Hidden Risk Inheritance
AI models inherit hidden risk from ungoverned training data. Bias, inaccuracy, and compliance violations become embedded in model behavior without detection.
Inappropriate Data Use
Sensitive data is used inappropriately when governance controls don't operate at the point of AI consumption. The model doesn't know the data is sensitive.
Post-Impact Discovery
Bias and compliance issues surface only after impact. By the time a governance review identifies the problem, the damage is operational and potentially public.
Blurred Accountability
Accountability becomes blurred when governance exists only as documentation. Who is responsible when an AI system makes a harmful decision using properly cataloged but inappropriately consumed data?
Harvard Business Review and MIT Sloan have documented multiple AI failures where governance gaps were only identified after operational harm occurred.¹¹ The World Economic Forum has similarly warned that AI accountability mechanisms must be embedded in operational systems, not layered externally.¹²
Active governance provides the guardrails that AI cannot infer on its own.
Role Transformation
The Shift in the Role of Governance
In traditional models, governance is perceived as:
The function that slows things down.
BARC research shows governance is frequently viewed internally as bureaucratic overhead rather than an enabler of value.¹³ This perception is not irrational. When governance manifests primarily as approval workflows, documentation requirements, and access request queues, it introduces friction without delivering corresponding operational benefit. Business users experience governance as a gate they must pass through, not a system that helps them move faster and more safely.
In active models, governance becomes:
The system that allows safe speed.
Accenture's AI: Built to Scale research reinforces that organizations embedding governance into execution workflows accelerate innovation while reducing risk exposure.¹⁴ This is not a marginal improvement. It is a categorical shift in how governance relates to the business. Instead of asking "Can I use this data?" and waiting for approval, business users operate within a system that automatically ensures their data use is compliant, appropriate, and safe.
This is a profound change. Governance stops being a gatekeeper and becomes an enabler, not by loosening control, but by applying it precisely, automatically, and consistently. The rigor does not decrease. The friction does. That distinction is what makes Active Data Governance transformative rather than merely iterative.
Active governance enables safe speed by applying control precisely, automatically, and consistently.
Outcomes Over Rules
From Rules to Outcomes
Active Data Governance focuses less on rule enforcement and more on outcome assurance. This is a subtle but critical distinction that separates truly active governance from governance that merely operates faster. The question shifts from "Was the rule followed?" to "Was the right outcome achieved?"
Gartner's research into Decision Intelligence argues that governance and decisioning must be integrated, ensuring controls are aligned to business outcomes rather than abstract compliance artifacts.¹⁵ In a rules-first model, an organization can be fully compliant with every governance policy and still produce poor outcomes: biased AI outputs, misused sensitive data, or decisions based on stale information. The rules were followed. The outcome was harmful.
Active Data Governance inverts this relationship. Rules still matter, they are the mechanism through which outcomes are achieved, but they are no longer the end goal. The end goal is that data use produces the intended business result while remaining compliant, ethical, and safe. Rules become instruments of outcome delivery rather than ends in themselves.
This reorientation has profound implications for how governance programs measure success. Instead of counting policies written, stewards assigned, or catalog entries completed, organizations measure whether data was used confidently, correctly, and without harm. The metric shifts from activity to impact.
Rules still matter, but they are no longer the end goal. Outcomes are.
Mental Model
A New Mental Model
Think of Active Data Governance not as a checklist, but as something fundamentally different in kind. The mental models we use to conceptualize governance shape how we design it, resource it, and measure it. If governance is conceived as a compliance checklist, it will be designed as one, staffed as one, and evaluated as one. And it will deliver accordingly: box-ticking without operational impact.
A Decision System
Active governance makes real-time determinations about whether data should be used, by whom, and in what context. It doesn't record decisions. It participates in them.
An Execution Layer
It operates within the data workflow itself, not adjacent to it. Governance is not consulted. It is embedded. It executes alongside the data operations it governs.
A Trust Engine
It continuously generates and validates trust. Every governed data interaction produces a trust signal: this data, for this use, in this context, is safe. Trust becomes computable.
The World Economic Forum has described future data governance models as operational control planes, embedded mechanisms that continuously assess and enforce intent within digital ecosystems.¹⁶ This language is precise and revealing. A control plane is not a report. It is not a policy document. It is an active, operational system that makes decisions in real time based on the conditions it observes.
Active Data Governance sits between data and its use, continuously assessing and enforcing intent. It is the layer that ensures every data interaction is not just possible but appropriate. It transforms governance from something organizations do to something their systems are.
Active Data Governance is not a checklist. It is an operational control plane.
Business Impact
What This Enables
When governance becomes active, the downstream effects ripple across the entire organization. This is not incremental improvement. It is a qualitative change in how organizations relate to their data and, by extension, how they operate, compete, and manage risk.
Business Users Gain Confidence to Act
When governance operates in real time and provides active assurance, business users no longer hesitate at the point of decision. They know the data they are using has been evaluated, in context, and cleared for their specific purpose. Confidence replaces caution.
Data Teams Are Freed from Manual Approvals
The manual approval queues that consume data team capacity are replaced by automated, context-aware enforcement. Data engineers and stewards shift from gatekeeping to value creation. Their expertise is redirected from processing requests to improving the governance system itself.
Risk Is Managed Continuously
Risk management becomes a continuous, embedded process rather than a periodic, retrospective exercise. The Bank for International Settlements has emphasized the need for continuous digital risk management in automated environments.¹⁷ Active governance delivers exactly this.
AI Becomes Usable at Scale
With active governance providing the guardrails, organizations can deploy AI systems confidently, knowing that data consumption is governed at runtime. AI scales from pilot to production without the governance bottleneck that stalls most organizations.
Trust Becomes Reusable
Perhaps most importantly, trust becomes reusable, not reinvented. Each governed interaction builds on the trust established by prior interactions. The organization develops a compounding trust asset that accelerates future data use.
Governance finally delivers on its promise.
Bridge to part 3
Setting Up the Final Step
Active Data Governance is not an abstract ideal. It is not a theoretical framework best appreciated in academic papers and analyst reports. It is achievable, without ripping out platforms, centralizing everything, or rebuilding from scratch. That pragmatism is essential to the argument. If Active Data Governance required a wholesale transformation of an organization's data architecture, it would remain aspirational for all but the most resourced enterprises. It does not.
Gartner's guidance on modernizing data governance emphasizes evolution over wholesale replacement, embedding control into existing ecosystems rather than restarting architecture.¹⁸ This is the path forward for most organizations: not revolution, but purposeful evolution. Active governance can be introduced incrementally, targeting the highest-risk data use cases first and expanding as the organization builds capability and confidence.
A Shift in Thinking
From governance as documentation to governance as decisioning. From compliance artifacts to operational assurance. From rules as endpoints to outcomes as goals.
A Shift in Operating Model
From centralized approval queues to distributed, embedded enforcement. From governance teams as bottlenecks to governance systems as enablers.
A Shift in Where Governance Is Applied
From data-at-rest to data-in-use. From ingestion and classification to query, combination, API exposure, AI consumption, and cross-domain sharing.
In Part 3, we move from concept to practice. We will explore how organizations enable Active Data Governance, and how they move from simply knowing data exists to confidently using it, every day, at scale. The intellectual groundwork is complete. The definition is clear. The imperative is established. What remains is the blueprint for action.
Governance that does not act at runtime cannot manage AI-era risk. Part 3 shows how to make it act.
Looking Ahead
Bridge to Part 3
This installment establishes what Active Data Governance means. It draws the line between passive oversight and operational enforcement, between documentation and decisioning, between awareness and confidence. But definition without implementation is incomplete.
Active Data Governance is not theoretical. It is operational.
Part 3 explores how organizations make it real.
The final installment in this series moves from concept to practice. It addresses the questions that every senior data leader will ask after absorbing this argument: How do we get there? What changes? Where do we start? Part 3 provides those answers with the same rigor and practitioner authority that defines the entire series.
This series is an invitation to think differently about governance, not as a compliance exercise, but as the operational backbone of data-driven enterprise. The arguments presented here are drawn from years of building, operating, and refining governance programs in complex, high-stakes environments. They are practitioner observations, not theoretical propositions.
If this installment has challenged your assumptions about what governance can and should do, that is by design. Active Data Governance demands a new mental model, and new mental models begin with productive discomfort.
Part 3 will move from definition to implementation. It will address the practical questions: how to begin, what to prioritize, and how to scale. The conversation continues.
Join a Data Conversation
Cameron Price.
References
  1. BARC Research, Data Governance Survey 2023.
  1. Forrester, Now Tech: Data Governance Solutions, Q3 2023.
  1. Gartner, Innovation Insight for Active Metadata Management, 2022.
  1. Basel Committee on Banking Supervision, BCBS 239.
  1. NIST, AI Risk Management Framework (AI RMF 1.0), 2023.
  1. IDC, Trust and Governance in AI, 2023.
  1. European Union, EU Artificial Intelligence Act, 2024.
  1. Gartner, Risk-Based Access Control Research, 2023.
  1. Forrester, Zero Trust eXtended Ecosystem, 2022.
  1. McKinsey & Company, State of AI in 2023.
  1. Harvard Business Review; MIT Sloan Management Review, AI governance case analyses.
  1. World Economic Forum, AI Governance Insights, 2023.
  1. BARC Research, governance adoption findings.
  1. Accenture, AI: Built to Scale, 2023.
  1. Gartner, Decision Intelligence, 2022.
  1. World Economic Forum, Data Policy and Control Planes, 2023.
  1. Bank for International Settlements, digital risk guidance.
  1. Gartner, Modernising Data Governance, 2023.
This is Part 2 of a three-part executive thought leadership series on Active Data Governance by Cameron Price. All references are cited for attribution and further reading. No embedded hyperlinks are included in body text. This content does not constitute product marketing or vendor endorsement.