An Winning Framework for AI-Ready Data: MDM, Governance & Data Quality

The need for AI-ready data is advancing rapidly. According to Semarchy, 74% of businesses are investing in AI this year, eager to unlock efficiency, innovation, and competitive advantages. But ambition alone is not enough. The paradox? While AI promises data-driven decision-making, nearly all organizations (98%) say poor AI data quality is undermining success. 

Data isn’t just an AI input — it’s the foundation. And weak foundations lead to unreliable results.

Think of AI as a sportscar driving over a crumbling bridge. Powerful, advanced, and capable of high performance, but ultimately at risk of failure if its underlying structure is weak. No matter how sophisticated AI models are, without trusted, well-integrated data, they will generate flawed insights, biased decisions, and costly mistakes.

Our research reveals a gap between AI ambition and execution, posing risks to businesses that fail to prioritize data readiness. You can get the full report here.

In this accompanying blog, we’ll explore:

• Key findings from the research which expose AI’s data challenges
• How poor data quality derails AI-driven decision-making
• Practical steps senior leaders can take to build AI-ready data

The importance of AI-ready data

AI has big potential, but its effectiveness is directly tied to the quality of the data it processes. Data serves as the raw material for AI models, informing decisions, predictions, and automations. However, if the data is flawed—whether through inaccuracies, inconsistencies, or gaps—the outcomes can be unreliable or even harmful.

Consider a retail company using AI to forecast demand and manage inventory. If the data feeding into the AI model is outdated or incomplete, the model might predict low demand for a product that’s actually about to spike in popularity. The result? Stockouts, lost sales, and frustrated customers.

Common challenges that organizations face include:

• • Data silos: When data is trapped in different departments or systems, it can’t be effectively utilized across the organization. AI models need a holistic view of the data to provide accurate insights.

• • Poor data quality: Inaccurate or incomplete data leads to AI models that make errors, creating a domino effect of poor decision-making.

• • Lack of governance: Without proper governance, data can become fragmented, leading to compliance risks and unreliable AI outcomes.

• • Inadequate infrastructure: AI workloads require robust, scalable infrastructure. If your systems can’t handle the volume and complexity of AI data, your projects may struggle to perform.

If you’re facing these issues, you’re most likely not alone. Even without AI, these challenges are important ones to solve, but with AI initiatives knocking on the door, there’s an even bigger need for a magnifying glass on them.

The data paradox: big AI budgets, fragile data foundations

Globally, businesses are pouring billions into AI — but many are doing so on an unstable foundation. More than half (52%) of organizations will dedicate at least 10% of their tech budget to AI this year, showing a strong push toward AI-powered transformation. Yet only 46% of leaders trust the data powering their AI models.

This disconnect is what makes AI a high-risk investment without proper data management. AI doesn’t think for itself. It mirrors the quality of the data it ingests. If that data is siloed, inconsistent, or riddled with duplicate records, AI will scale those problems rather than solve them.

Our research identifies three key AI and data quality challenges blocking success:

1. Compliance constraints (27%) – Regulatory uncertainty making AI governance difficult.
2. Duplicate records (25%) – Mismatched, conflicting data leading to inaccurate insights.
3. Poor integration (21%) – AI trained on fragmented data, failing to see the full picture.

A surgeon wouldn’t work with outdated X-rays — the risk of misdiagnosis is enormous! The same applies to using AI without high-quality, well-governed data. If organizations don’t solve their AI data quality issues upfront, they risk increasing project costs, regulatory exposure, and executive mistrust in AI-generated decisions.

Why poor AI data quality is a silent killer

Flawed, inconsistent, or low-quality data flows directly into AI algorithms, affecting everything from recommendations to customer insights to risk modeling.

And businesses are already feeling the impact:

• 22% of AI projects are delayed due to insufficient data pipelines.
• 21% report operational inefficiencies caused by inaccurate AI outputs.
• 20% of organizations experience increased costs from fixing AI-related mistakes.
• 19% face compliance issues when AI fails to meet data security and governance requirements.
• 19% say trust in AI-generated insights is deteriorating 

Crucially, if business leaders don’t trust the data, they won’t trust the AI, creating a cycle of ambiguity and hesitation, rather than adoption and innovation.

Who owns AI strategy? Leadership disconnects stall progress

If AI is critical, who ensures its success?

Leadership responsibility is scattered, and this fragmentation is slowing progress. According to Semarchy’s research:

• 38% of CIOs consider AI their domain but focus more on infrastructure than data quality.
• 30% of CTOs push AI forward from a technology perspective.
• CDOs — despite their responsibility for data strategy — are among the least likely to own AI (only 15%).

This misalignment creates confusion. When AI leadership is unclear, data management suffers.

Yet, fewer than 7% of organizations have a cross-functional team responsible for AI strategy, including functions such as operations, finance, compliance, and marketing. This leadership vacuum means businesses risk accelerating AI deployment without aligning business and technical objectives, leading to bad data, flawed implementations and wasted investments.

Key indicators of AI-ready data

To overcome these common challenges and determine if your data is AI-ready, start by examining the following critical factors:

1. Data quality

High-quality data is accurate, consistent, and complete. Poor data quality can lead to unreliable AI outcomes, resulting in misguided business decisions and costing organizations $12.9 million annually on average. Investing in data cleansing and enrichment processes makes sure your AI models are fed with the best possible data. To improve data quality:

• • Implement data profiling and cleansing processes

• • Establish data quality metrics and monitoring

• • Use machine learning for anomaly detection and data validation

2. Data governance

Strong governance practices, including data stewardship, regulatory compliance, and clear data ownership, are vital for maintaining data integrity and trust in AI systems. However, less than half of organizations report having well-established policies and practices for effectively governing data. Without proper governance, your AI projects could not only fail but also face legal and operational risks.

Key components of effective data governance include:

• • Clearly defined roles and responsibilities

• • Data policies and standards

• • Data lineage and impact analysis capabilities

• • Regular audits and compliance checks

3. Data integration

More than 75% of teams report working in organizations with large blind spots due to silos. But comprehensive AI models require access to data from across your entire organization. Effective data integration breaks down these silos and provides a holistic view of all your business data.

Best practices for data integration include:

• • Utilizing APIs for real-time data access

• • Adopting a master data management (MDM) solution

4. Scalable data infrastructure

AI workloads demand scalable data infrastructure, particularly in cloud or hybrid environments. Employing an infrastructure that can handle the data volume, variety, and velocity required by AI is vital for long-term success.

To prepare your infrastructure for AI:

• • Assess your current data storage and processing capabilities

• • Consider cloud or hybrid cloud solutions for flexibility and scalability

• • Implement data virtualization to improve data access and reduce data movement

• • Invest in high-performance computing resources for complex AI workloads

How to improve data quality for AI success: a 6-step playbook

For AI to deliver real, scalable business value, organizations must move beyond experimentation to execution. That means ensuring data is structured, governed, and accessible before AI models go into production. 

To achieve this, business leaders should focus on six critical areas:

1. Understand and catalog your data before AI begins.

AI models rely on context-rich, well-documented data, yet most businesses operate with limited visibility into their own data assets.

Semarchy’s data profiling, cataloging, and lineage tracking tools provide organizations with a deep understanding of what data they have, where it lives, and who owns it, creating a clear foundation for AI-driven insights.

2. Unify enterprise data into a single source of truth.

AI trained on fragmented, inconsistent data will generate conflicting outputs. Just as a GPS requires a complete map to provide accurate directions, AI requires a unified, 360-degree view of enterprise data to make reliable predictions.

Semarchy’s master data management (MDM) solution eliminates silos, consolidating critical data into AI-ready golden records that ensure AI models work from a consistent, structured foundation.

3. Ensure data is clean, high-quality, and bias-free.

Garbage in, garbage out — AI magnifies data flaws rather than fixing them. Without automated stewardship, AI models may introduce systemic bias, produce misleading insights, or require costly retraining.

Semarchy automates data validation, enrichment, and cleansing, reducing redundancy, improving completeness, and ensuring AI-driven decisions are accurate, fair, and actionable.

4. Move data to AI models efficiently and securely.

AI models need fast, structured, and well-integrated data to function effectively. Slow, fragmented pipelines lead to stale, irrelevant insights that hinder real-time decision-making.

Semarchy’s built-in Extract, Load, Transform (ELT) capabilities enable real-time movement of trusted, governed data across enterprise systems, ensuring AI models are always working with current, relevant inputs.

5. Scale AI responsibly with compliance and governance in mind.

With AI regulations such as the EU’s AI Act still evolving, businesses can’t afford to take a wait-and-see approach. Without proper governance, AI models risk non-compliance, privacy breaches, and reputational damage.

Semarchy embeds audit trails, role-based access controls, and enterprise-grade data governance frameworks, helping businesses scale AI confidently while staying compliant with emerging laws and ethical guidelines.

6. Make AI-ready data accessible beyond IT teams.

AI shouldn’t be confined to technical experts alone. Many organizations struggle to democratize data, limiting AI’s value to a handful of teams.

Semarchy enables business users, analysts, and decision-makers to access AI-ready data securely. This ensures AI-driven insights are collaborative, aligned with real business needs, and accessible across the enterprise — not just locked within IT.

Is neglecting AI and data quality a gamble you can afford to make?

Organizations that fail to prioritize data integrity and governance will watch their AI investments falter. Those with a strategic data mindset will be the ones that turn AI ambition into real-world success.

Semarchy’s expertise in MDM and AI-ready data governance enables businesses to close the gap between AI potential and execution. Leading enterprises trust Semarchy unify data, ensure AI-quality records, and build AI strategies on a foundation of trust and compliance.

Want to see how AI-ready data powers real business results? Explore Demos of the Semarchy Data Platform today.