Meaning Without Truth
A Monograph on Trust, Context, and Master Data Management in the Age of AI
By Malcolm Hawker, CDO, Profisee
Executive Summary
As organizations accelerate their adoption of artificial intelligence, meaning has become the dominant concern in data management. Ontologies, knowledge graphs, and semantic layers are rightly viewed as essential to providing the contextual understanding of data that Generative AI systems require. Structured data alone is no longer sufficient.
But meaning without truth is confusion.
Trustworthy AI systems require more than semantic clarity. They require truth - grounded, explainable, and contextually appropriate truth. And truth, unlike meaning, lives at the record level. It is established through consistent, auditable identity resolution and reconciliation across systems, sources, and increasingly, across both structured and unstructured data. And it is applied to individual records, not objects or schemas or tables.
This monograph argues that the historical struggles of master data management do not reflect its irrelevance, but rather the ways in which it has been approached. Big-bang and consultant-driven implementations, framework-first thinking, domain-centric scope, and control-oriented governance have all undermined MDM’s ability to deliver value.
In an AI-driven world, MDM must evolve.
Modern MDM programs must support multiple versions of truth, recognizing that truth is contextually bound and purpose driven. It must act as connective tissue between structured data, unstructured knowledge, and semantic representations. It must prioritize explainability, and not just accuracy. And it must be governed pragmatically, where governance policies are focused on enablement and business outcomes rather than control.
When approached with discipline, pragmatism, and an unwavering focus on value delivery, MDM remains a foundational capability for trustworthy analytics and AI. Meaning provides understanding. Truth provides trust. Together, they enable confident, responsible decision-making at scale.
Part I: Meaning Without Truth
There is much talk these days about the importance of understanding meaning in data. This focus is largely a function of the fact that the one thing Generative AI needs most - rich contextual understanding - is largely missing from our highly structured databases.
Rows and columns are excellent at storing facts. They are far less effective at conveying intent, nuance, and real-world relationships. If we expect GenAI to unlock the latent value buried inside decades of highly structured enterprise data, we must provide more than schemas and tables. We must provide context.
This realization has driven a resurgence of interest in ontologies, knowledge graphs, and the broader discipline of knowledge management. Many are already calling 2026 the “year of the ontology,” and given the strategic importance of context in AI systems, that prediction may prove accurate.
This focus is entirely justified.
But in our rush toward meaning, many in the data management community are forgetting something equally important.
Truth.
To establish trust in data - and in the AI systems increasingly built on top of it - we must provide both meaning and truth. One without the other is insufficient.
Over the years, if there is one lesson my career has reinforced repeatedly, it is this: simply centralizing data does not make it accurate. Moving data into a warehouse, lake, or lakehouse does not ensure that it reflects reality. Scale does not create truth. Integration does not guarantee correctness.
Meaning without truth is confusion.
You can build the most elegant ontology imaginable - one that beautifully models customers, products, accounts, and interactions - but if you cannot say with confidence whether Bob Smith and Robert Smith are the same person, your insights will be wrong. Worse, they will look right, and GenAI solutions will confidently, but incorrectly, amplify the error.
This problem has existed for decades, and for decades many organizations have quietly worked around it rather than confronting it directly. We have often accepted ambiguity as inevitable, even as decisions have become more automated and more consequential.
The importance of pairing meaning with truth is something my friend Scott Taylor, the “Data Whisperer,” has been emphasizing for years. Much of my thinking on this topic has been shaped by that perspective, and I remain convinced that it is even more critical in an AI-driven world.
As an industry, we have built impressive capabilities around consistency, standardization, and integration. But accuracy - whether the data represents the real-world entity it claims to describe - has often eluded us.
This is where master data management becomes unavoidable.
MDM creates a bridge between meaning and measurement. It anchors semantic understanding to verifiable identity. Without that anchor, the progression from data to information to knowledge collapses under its own weight.
AI is the forcing function that makes this bridge impossible to ignore, and why MDM remains a critical enabling capability of any modern data ecosystem.
Part II: Why MDM So Often Failed
If truth is so fundamental, why has MDM struggled for so long?
The answer is not that MDM is wrong. It is that it has too often been pursued in ways that were disconnected from business reality.
Historically, many MDM programs shared a common set of characteristics. They were broad in scope, domain-centric, framework-driven, and dependent on centralized control of data. They promised transformation but delayed value. They prioritized architecture over outcomes.
MDM became something organizations were told they needed, but rarely something they could articulate the value of. This is especially the case where the data and governance requirements of a given business domain (like marketing), do not fully align to those at higher, more cross-functional levels of the organization.
Governance was positioned as a prerequisite rather than an enabler. The business value of MDM was only rarely quantified – and if it was – it only rarely connected with meaningful business KPI’s. Business stakeholders were asked to wait while foundations were laid. Inevitably, they stopped waiting.
Instead, they built workarounds - spreadsheets, shadow pipelines, manual reconciliation processes - not because they rejected MDM or governance, but because they needed results.
There is no better example of stakeholders getting tired of waiting than the birth and meteoric rise of the software market for customer data platforms (CDPs). CDP’s are marketing-specific solutions that use all the same capabilities of MDM’s, but are built for the scale, flexibility, and ease-of-use required by marketers. They were created out of a desire of marketing teams to have their own, domain-specific form of MDM, which is a demand that centralized IT teams managing enterprise MDM platforms were unwilling, or unable, to provide.
Marketers stopped waiting for central IT teams to deliver a ‘customer 360’, and many went and built their own solutions. These solutions, among many others, became the CDP software market - one that now cumulatively dwarfs the market size for MDM solutions.
This broader pattern of MDM being too big, or too bulky, or incapable of meeting the data requirements of business functions outside the analytical realm did real damage. It eroded executive confidence not just in MDM, but in data leadership more broadly. And once trust is lost, no amount of tooling can restore it.
In the age of AI, repeating these mistakes is far more dangerous.
AI systems amplify both insight and error. Plausible but wrong answers are more harmful than obvious failures. When AI outputs influence pricing, credit, medical decisions, or compliance, ambiguity around truth becomes unacceptable.
MDM must therefore be re-approached - not as a monolithic infrastructure initiative, but as a disciplined, pragmatic capability for enabling trustworthy decisions at scale - regardless of if those decisions are made by humans or algorithms.
Part III: Master Data Management Best Practices for an AI-Driven World
Business Value as the North Star
MDM should never exist for its own sake.
Truth is not valuable in the abstract. It is valuable only when it changes a business outcome. Executives do not fund accuracy; they fund results.
The most successful MDM programs I have seen begin by aligning data management and quality KPIs directly to business KPIs. Not loosely. Explicitly.
This requires reframing the conversation. Instead of asking how to improve data quality, the question becomes:
How does identity uncertainty increase operational cost?
How does duplicate data inflate business risk?
How does lack of trust slow decisions or force conservatism, and what are the financial consequences of these delays?
Where does ambiguity require manual intervention, and how much does this intervention cost?
Answering these questions requires partnering with the right business leaders - often in finance, operations, risk, or revenue functions - who understand both the business process being improved and the economics behind it.
Together, these partners can build pragmatic value models. These models do not need to be perfect. They need to be credible. For far too long data leaders have shrugged at the idea of building business models to measure the impacts of their investments in data management, largely because many believe that full causality is required for any model to be accepted in the business. Put another way, there is a long-held myth of many CDOs that if you cannot prove with 100% certainty that an investment in data management is the sole reason a business outcome has been delivered, then there’s no point in building those metrics.
This is an incorrect assertion, and it’s one that data leaders must abandon - both for the sake of their careers, and their integrity within their organizations.
After all, CDOs are the data people, and hearing the senior-most data person suggest that it’s impossible to generate data about the financial benefits of a mission-critical dependency for our organizations success seems both implausible and completely ridiculous.
Yet, many CDOs remain steadfast in this perspective, while all their peers in the C-suite can generate attribution models which strongly suggest that investments in things like employee satisfaction, better marketing, or more efficient procurement methods can all lead to improvements in the bottom line.
While many CDOs cling to the notion of ‘anything less than causal metrics to prove data is valuable is a waste of time’, their peers are taking far more pragmatic (and successful) approaches.
Leaders in departments across the org are showing that having fully causal models to prove, without a shadow of a doubt, that investments in marketing, HR, or finance can improve the bottom line are simply not required. They show that capturing data which is directionally correct, via attribution models, is more than sufficient to establish priorities, guide iteration, and justify continued investment.
CDOs looking to invest in MDM and data governance must take the same approach. Over time, as MDM improves truth, the models themselves improve, and will justify further investment.
Figure 1 below is a useful framework for CDOs looking to build a model which links their various data quality KPIs (like uniqueness, accuracy, completeness, etc.) to business KPIs. This linkage needs to happen at multiple levels starting at a field level, working all the way ‘up’ the value pyramid to the high-level strategies of the company as a whole.
Figure 1 – The Data and Analytics Metrics and Value Framework
MVP Thinking and Fast Value Delivery
One of the most common causes of MDM failure is overreach.
Organizations attempt to solve too many problems at once, define too many entities, and reconcile too many systems. The result is complexity without momentum.
An MVP (minimum viable product) mindset changes this dynamic.
Minimum viable MDM means starting small, delivering value quickly, and learning continuously. It prioritizes proof over completeness. Early wins build trust, and trust creates scale. This approach mirrors how successful AI initiatives already operate. Hypotheses are tested. Models are refined. Value emerges iteratively.
MDM must adopt the same posture, and doing so is much easier than you might think. Asking ‘what is the minimum about of people, process, and technology, bounded within an MDM program, needed to solve a single business problem?’ This includes the minimum amount of governance, the minimum amount of data, and the minimum level of engagement from the business.
When you ask these questions, and when you put a rabid focus on the delivery of value to the customers of an MDM solution in as short a period of time as possible, you can change the dynamic of MDM being big, bulky, and too complex – to something far more nimble, far more attainable, and far more valuable than MDM programs of the past.
Figure 2 shows a high level diagram of the MVP approach to MDM in action through the lens of an MDM operating model. Each MVP release builds on prior releases, and with each release, you build a stronger data management foundation. In time, over multiple releases, you will get closer and close to having 100% of both a data governance and management foundation – but where business value has been delivered at every phase.
The outer ring of this framework highlights all the things that a data leader cannot change in the short term – such as the business strategy, the overall corporate culture, or the governance maturity. These things are important, but cannot be influenced through any one MVP of an MDM program.
As data leaders focus on the MDM operating model within the inner ring – and the iterative delivery of value through MDM over time – they will slowly start to influence the outer ring. Taking this approach allows data leaders to avoid getting mired in a focus on things they cannot immediately change (like corporate culture), where instead they focus only on those things which they can change.
With a well-defined business outcome as the goal (eg. ‘increase customer retention 5% by eliminating duplicate customer records in our CRM’), MDM program leads must then ask: ‘what is the minimum I must deliver, for each driver of this framework, for an MDM program to deliver value?’ What is the minimum technology? What is the minimum governance? What is the minimum level of business engagement and alignment needed to deliver on the goal?
When these questions are answered, the scope for an MVP deployment is defined, and the chances of short-term success are drastically increased.
Figure 2 – An MVP Approach to MDM
Finding the Right Internal Customer
MDM does not start with data. It starts with someone who has a problem they want solved.
The strongest MDM programs partner with business leaders who already feel the pain of unreliable identity. These leaders are open to analytical approaches and motivated by outcomes, not compliance.
This is where product management becomes invaluable.
Treating data capabilities as products forces data teams to deeply understand customer needs, success criteria, and tradeoffs. It shifts the focus from delivery to adoption.
A focus on product management within an MDM program helps data leaders ask better questions:
Who is the customer?
What problem are we solving?
How will we know if it worked?
What tradeoffs are acceptable?
MDM programs that adopt a product discipline are far more likely to deliver relevance - and relevance drives adoption.
Process Over Domain
One of the most persistent - and least productive - patterns in MDM initiatives is a fixation on data domains. Customer domain. Product domain. Supplier domain. The assumption is that if we “solve” a domain, value will naturally follow.
In practice, this almost never happens.
Domains are abstractions. Businesses do not operate in domains; they operate through processes. Orders are fulfilled. Claims are adjudicated. Customers are onboarded. Risks are assessed. Revenue is recognized.
When MDM is framed around domains, conversations quickly devolve into debates about ownership, stewardship, and scope. Who owns the customer domain? Which system is the system of record? Whose definition is correct? These debates are rarely resolvable in isolation, and they consume enormous organizational energy without producing outcomes. Its these conversations, and the tension they naturally create in any organization, which have helped to give MDM programs a negative perception in the past, and its these perceptions we must change.
A process-centric approach changes the conversation entirely.
Processes have clear owners. Processes have measurable performance metrics. Processes have failure modes that executives already understand. Most importantly, processes create moments where truth matters - moments where a decision must be made, a risk accepted, or an action taken.
By anchoring MDM to one or more business processes, data leaders can answer far more practical questions:
Where does identity ambiguity slow or distort this process?
Where does uncertainty force manual intervention?
Where does lack of trust increase cost, risk, or cycle time?
This framing naturally constrains scope. It also makes success visible, and infinitely easier to measure. Improving truth within a single high-value process often delivers more impact than attempting to standardize an entire domain across the enterprise.
Equally important, a process-centric approach reduces governance friction. Governance debates become contextual rather than abstract. Decisions about data definitions, survivorship, and matching logic are grounded in how the data is used, not theoretical completeness. The difference between a process-bounded approach to data governance policies and a data-bounded approach may seem trivial, but it is not. It’s foundational, and more of a process focus is the primary way how CDOs and other data leaders will transition from being a cost center, to a business enabler.
In an AI-driven world - where insights increasingly trigger automated or semi-automated actions - this alignment between truth (and the governance polices needed to enable it) and process is critical. Truth matters most at the moment of action, and processes define where those moments occur.
A unique defining characteristic of MDM is its unique ability, at least in comparison to other data management tools (like Data Quality or Data Integration tools) is its ability to influence how data is not only used in analytical environments, but also in how data is both created and consumed in operational systems – like CRM, ERP, and SCM applications. This is yet another reason why MDM is a critical enabling capability of any modern IT infrastructure – especially in situations where application-bound copilots and agents are interacting directly with data that may never even be replicated into a downstream analytical environment.
Figure 3 – Limiting the Scope of an MDM or Governance Effort
Avoiding Dogma in Implementation
Few areas of data management are as burdened by dogma as MDM.
Over the years, the industry has produced a steady stream of prescriptive debates: registry versus consolidation, operational versus analytical, centralized versus federated. These debates often masquerade as best practices, but in reality they are artifacts of specific contexts, technologies, and tribes.
The problem is not that these distinctions are meaningless. The problem is that they are often treated as decisions that must be made up front, rather than hypotheses to be tested.
In my experience, MDM initiatives that stall early almost always do so because too much energy is spent choosing an implementation philosophy, and not enough energy is spent delivering value. Architecture becomes a proxy for progress.
A more effective approach is to treat MDM as an analytical discipline first, and an operational capability second. Both are equally important, but if your goal is to realize the transformational value of MDM it must first be launched into the organization – and the best way to increase the probability that will happen is through an early embrace of an analytical style of MDM.
Analytical MDM - focused on insight generation rather than enforcement - allows organizations to:
Test identity resolution logic quickly
Minimize the complexity of governance policies needed to drive value
Measure the impact of improved truth on decisions
Learn where precision matters and where it does not
Build credibility before introducing operational dependencies
Avoid complex business process changes
This approach is particularly well-suited to an AI-driven environment, where iteration and learning are already normalized. It mirrors how machine learning models are developed: start with hypotheses, test them against reality, refine based on outcomes.
Operational MDM still matters. But operational rigor should follow demonstrated value, not precede it. When organizations attempt to operationalize MDM too early - before they understand where truth matters - they often hard-code assumptions that later become constraints.
Avoiding dogma does not mean avoiding discipline. It means allowing architecture to emerge from use, rather than imposing it in advance.
The right MDM implementation style is not the one that conforms to a textbook definition. It is the one that produces trustworthy insights quickly while preserving optionality for the future.
Just as important, modern MDM must be enabled by the business, for the business. The era of tightly centralized, top-down control over all representations of master data is effectively over. This is not a failure of discipline, but a reflection of organizational reality. Federated architectures are here to stay. Business units operate with different incentives, different risk tolerances, and different definitions of “fit for purpose.” Expecting a single, centrally imposed view of master data to satisfy every one of those contexts is neither realistic nor desirable.
This does not mean abandoning rigor or accountability. It means recognizing that some degree of local control over highly contextualized views of master data is appropriate. Modern MDM leaders must distinguish between core identity truth - which requires consistency, explainability, and auditability - and contextual representations of that truth, which legitimately vary by use case and business domain. When MDM attempts to over-centralize the latter, it creates friction, drives workarounds, and ultimately undermines trust. When it enables federated consumption while anchoring identity through shared rules and transparent reasoning, it earns adoption. Avoiding dogma here means accepting that federation is not the enemy of truth - it is the environment in which truth must now operate.
Removing Unnecessary Dependencies
Perhaps the most damaging myth surrounding MDM is the belief that organizations must first “get their house in order” before they can begin.
A fully articulated data strategy.
A mature enterprise governance framework.
Clear operating models and role definitions.
These are all worthwhile goals. But when positioned as prerequisites, they become barriers.
In practice, many MDM initiatives die in the planning phase - not because leaders disagree on the importance of truth, but because the list of dependencies grows faster than momentum. Strategy documents are written. Frameworks are debated. Committees are formed. Value is deferred. Often, this is at the hands of consultants given too much free reign on program scope. But other times, its simply a result of well-intentioned program leaders who have been convinced by people insufficiently experienced in real-world data management scenarios that pragmatism equals risk
An approach where perceived procedural dependencies are prioritized ahead of the delivery of value is backwards. .
In successful organizations, governance, strategy, and operating models co-evolve with value delivery. Early MDM successes surface real governance needs. They clarify where rigor is required and where flexibility is acceptable. They turn abstract debates into concrete decisions.
Waiting for perfect alignment before starting MDM is not prudence - it is avoidance. And from the perspective of the current tenure of the program lead, it is often existential.
This is especially problematic in an AI context, where business leaders are accustomed to rapid experimentation and visible progress. Data teams that insist on long lead times and extensive prerequisites risk being bypassed entirely, or caught in the next round of corporate downsizing.
Value delivery must be the North Star. Everything else - governance, standards, strategy - should orbit around it, informed by real outcomes rather than theoretical ideals.
Part IV: Non-Negotiables for Modern MDM
Explainability and Contextual Truth
Any serious discussion of modern MDM must begin by dismantling the myth of a single, universal version of the truth.
Truth is contextual.
The “correct” representation of an entity depends on how - and why - it is being used. The version of customer truth required for regulatory reporting may differ from the version needed for marketing personalization. Both can be accurate. Both can be defensible. And both can be true within their respective contexts.
Historically, many MDM programs attempted to force convergence on a single canonical truth. In practice, this often resulted in compromises that satisfied no one, or in shadow versions of truth emerging downstream.
Modern MDM must explicitly support multiple versions of truth, each governed by context, purpose, and risk tolerance.
Figure 4 below shows that the concept of ‘truth’ exists in at least three different ‘levels’ in an organization. These levels are functional (or ‘local’), cross functional (or ‘regional’), and enterprise wide (or, ‘global’). Each context can have its own definition of truth, and all three can be correct – depending on the use case. A more modern approach to MDM recognizes that all three (or more) contextually-bound notions of truth can exist at the same time.
This diagram recognizes something that data practitioners have known for a long time – the needs of one individual department or function are often not the same as the needs that exist on an enterprise-wide level. This tension between local / decentralized and global / centralized has existed for as long as we’ve managed data, and in the real of analytics, the latter tends to rue the day. A more recent push towards more federated and decentralized architecture, fueled in part by an increasing focus on GenAI, is driving a need for a more pragmatic and flexible approach to both master data management, and data governance.
Figure 4 – Three Levels of Governance in all Organizations
In a world dominated by AI, what matters most for MDM is not uniformity, but clarity and explainability.
Explainability is what transforms truth from an assertion into an asset. In regulated and high-risk environments, it is not enough to know that two records were linked; organizations must be able to explain why. Black-box identity resolution may be statistically impressive, but it is operationally dangerous.
This is where MDM provides something probabilistic AI systems cannot: deterministic, inspectable reasoning about identity. Rather than competing with AI, MDM establishes the guardrails within which AI can operate safely.
As AI systems increasingly participate in decisions that affect customers, finances, and compliance, explainability becomes non-negotiable. I’ve learned this lesson the hard way.
MDM as Connective Tissue into Knowledge Management
Meaning increasingly lives outside structured systems.
Contracts, policies, emails, call transcripts, documents, and narratives contain enormous amounts of contextual information that structured data alone cannot express. Knowledge graphs and ontologies are essential for extracting and organizing that meaning.
But meaning without anchoring remains fragile.
MDM’s evolving role is to serve as connective tissue between:
Structured representations of entities
Unstructured expressions of meaning
Semantic frameworks that bind them together
In this sense, MDM is no longer merely a system of record. It becomes a system of reference and reconciliation, ensuring that entities described across different modalities - tables, text, graphs - are consistently understood and represented.
This evolution does not diminish the importance of MDM; it expands it. Truth becomes the stabilizing force that allows meaning to scale across systems and contexts.
Without that stabilizing force, knowledge management efforts risk drifting into semantic sophistication without operational trust. This risk will be completely avoided by forward-leaning CDOs who take the steps to better integrate their MDM programs – and all their data management initiatives – to any existing focus on knowledge management within their organizations.
Figure 5 shows the critical role that MDM plays in establishing a foundational layer of ‘truth’ that spans across all of data, information, and knowledge management.
Figure 5 – A Next Generation Semantic Layer
Pragmatic Data Governance as Enablement
No MDM initiative exists independently of data governance. But governance itself must evolve.
For too long, governance has been framed primarily as a mechanism of control. Policies, approvals, and enforcement have taken precedence over enablement. In many organizations, governance has become synonymous with friction.
This is not a failure of intent. It is a failure of orientation.
Effective data governance exists to reduce ambiguity, accelerate decision-making, and enable safe use of data in pursuit of business outcomes. Control is a byproduct, not the objective. The most
A pragmatic governance approach:
Aligns rigor with risk
Evolves alongside value delivery
Emphasizes stewardship as influence, not ownership
Prioritizes clarity over completeness
MDM provides a natural focal point for this evolution. Because it sits at the intersection of identity, trust, and use, MDM surfaces governance needs organically. Early successes reveal where rules are necessary and where flexibility is acceptable.
Governance that emerges from doing is far more resilient than governance imposed in advance.
In an AI-driven world, where speed and experimentation are the norm, governance that cannot keep pace with value creation will be ignored. Governance that enables value will be embraced.
Conclusion: Truth, Meaning, and Trust
Meaning without truth is confusion.
Truth without meaning is limited.
In the age of AI, trust emerges only when the two are intentionally combined. Master data management - pragmatic, explainable, and outcome-driven - remains essential to that convergence.
MDM may never be glamorous. But in a world where AI increasingly shapes decisions, truth is not optional.
It is foundational.
Thank you for reading this article and I sincerely hope you got value from it. If you are interested in learning more about MDM, then:
- Please connect with me on Linkedin: (19) Malcolm Hawker | LinkedIn
- Please check out the wealth of resources available on the Profisee website related to MDM best practices: www.profisee.com
- Please check out my podcast, CDO Matters: https://profisee.com/podcast/
- And last, but certainly not least, check out my latest book – The Data Hero Playbook. It’s a detailed exploration of the mindsets that data leaders need in order to succeed in an era of AI: https://a.co/d/6qxRTW5
And finally – a huge thanks to the legendary Bill Inmon for curating this content. I am humbled beyond words that he approached me to make this contribution.
Malcolm Hawker - February, 2026
Malcolm Hawker is the CDO of Profisee and is a thought leader in the fields of Data Strategy, Master Data Management (MDM), and Data Governance. As a former Gartner analyst, Malcolm has authored industry-defining research and has consulted some of the largest businesses in the world on their enterprise data and analytics strategies. Having served as a Chief Product Officer, Head of IT, and strategic business consultant, Malcolm is an industry leader with over 25 years’ experience at the forefront of data-enabled business transformations. Malcolm is a frequent public speaker on data and analytics best practices, and he cherishes the opportunity to share practical and actionable insights on how companies can achieve their strategic imperatives by improving their approach to data management. He is the author of the Wiley book ‘The Data Hero Playbook’, which details the critical role that a growth mindset plays in helping companies to realize the transformative value of data. When not sharing his passion for data or recording episodes of the CDO Matters podcast, Malcolm is an avid hobbyist landscape photographer and lives with his wife and two dogs in a small beach town in Florida.