The Art of Data Management and Governance

In the first blog of this series, we explored how data governance has evolved in the age of artificial intelligence (AI). As organizations adopt AI technologies and rely more heavily on data-driven decision-making, governance frameworks have become essential for ensuring that data remains accurate, secure, accessible, and trustworthy.

But governance alone is not enough.

Poor data practices aren’t just inefficient; they create unwanted cost centers. Organizations lose an average of $12.9 million annually due to poor data quality, and by 2026, organizations are expected to  abandon 60% of AI projects unsupported by AI-ready data.

To fully unlock the potential of AI, organizations must also build strong data management practices that ensure data flows through systems efficiently and reliably. While these two concepts are closely related, they serve distinct roles within a broader data strategy.

Understanding the relationship between data management and governance is critical for organizations seeking to build scalable, AI-ready operations.

In this chapter, we’ll explore:

• What data management is and how it works?
  
• How does data governance enable and differ from data management?
  
• Why are both disciplines necessary for enterprise AI initiatives?
  
• How does effective data management accelerate AI adoption?
  
• Why do organizations ultimately need structured governance frameworks?
  

What is data management?

Data management refers to the day-to-day operational activities that control how data is collected, stored, processed, and maintained throughout its lifecycle.

While governance defines the policies and standards around data, data management teams execute the work required to make those policies a reality. It involves the technical implementation of systems, processes, and workflows that ensure an organization’s data remains usable and reliable.

Core data management activities typically include:

• Data ingestion: collecting structured and unstructured data from files (such as documents, images, videos, etc.), internal systems, applications, sensors, and external data sources.
  
• Data storage: organizing and maintaining information across data warehouses, lakes, and other digital storage systems.
  
• Data integration: combining data from different systems to create a unified view of an organization’s data landscape.
  
• Metadata management: capturing relevant metadata that describes assets and their context to make them more accessible and usable for multiple downstream use cases such as monetization.
  
• Data quality checks: monitoring accuracy, completeness, and consistency across datasets.
  
• Data lifecycle management: managing how data is created, stored, archived, and eventually retired.

These processes ensure that raw data becomes usable data, enabling organizations to leverage it for analytics, reporting, and AI applications. 

In large enterprises, these responsibilities often fall to data engineers, data architects, and data stewards who manage data pipelines, maintain data infrastructure, and ensure the organization’s data flows smoothly across systems. 

However, recent research shows that data professionals still spend significant time preparing data, with analysts dedicating an average of 10–11 hours per week to data collection and preparation tasks, highlighting the ongoing operational burden of poor data management practices

Without strong enterprise data management, organizations struggle to maintain data integrity, share data effectively across business units, or maintain the quality standards required for AI systems.

Data management vs data governance

Although the terms are often used interchangeably, data governance and data management serve different functions within an organization’s data ecosystem.

At a high level:

• Data governance defines the rules.
• Data management executes them.

Data governance provides the overarching framework of policies, standards, and procedures that dictate how data should be handled, protected, and used across an organization. It establishes accountability, defines data ownership, and ensures compliance with regulatory requirements.

Data management, by contrast, involves the technical and operational work required to implement those rules in practice. 

Below highlights the differences between the two:

Together, these two disciplines create the foundation of a successful data governance strategy.

Yet, despite growing investments in data infrastructure, trust in data remains a significant challenge. In fact, 64% of business leaders say their organization does not trust data for decision-making, highlighting a persistent disconnect between governance policies and day-to-day execution. This gap underscores the reality that building data systems alone is not enough. Organizations must ensure that governance and management practices work together to produce reliable, high-quality data that stakeholders can confidently use.

Why governance without management fails

Many organizations begin building a data governance program by drafting policies, defining data ownership, and creating governance committees. While these steps are essential, governance alone cannot improve data quality or accessibility. Policies only matter if they can be implemented.

This lack of integration and operationalization leads to significant waste. Research from Seagate and IDC shows that as much as 68% of enterprise data goes unused, often due to challenges in making data usable, managing storage, and breaking down silos across systems.

Without strong data management practices, organizations often face problems such as:

• Data silos across different business units
  
• Inconsistent metadata and documentation
  
• Lack of visibility into data lineage
  
• Poor data quality and unreliable analytics
  
• Difficulty enforcing governance policies
  

For example, a company may establish a policy that sensitive data must be encrypted and restricted through Role-Based Access Control (RBAC). However, without the proper infrastructure and data management processes in place, enforcing these policies across multiple systems becomes nearly impossible.

This is why governance must be supported by operational systems that enable:

• Data discovery and classification
  
• Metadata management
  
• Data lineage tracking
  
• Automated quality checks
  
• Secure data access controls
  

When governance policies are integrated directly into data management workflows, organizations can enforce standards automatically rather than relying on manual oversight.

Why data management without governance fails

The reverse is also true.

Organizations that invest heavily in data infrastructure but lack governance often encounter a different set of problems.

Without governance, data environments become chaotic. Teams may collect and store large volumes of data, but without clear ownership or standardized definitions, it becomes difficult to trust or use that information effectively.

Common issues include:

• Conflicting definitions for key metrics
  
• Uncontrolled data sharing across departments
  
• Poor documentation of data sources
  
• Lack of accountability for maintaining data quality
  
• Increased exposure of sensitive data
  

In these environments, data may technically exist, but it is not reliable enough to support critical decision-making or AI initiatives.

Effective data governance ensures that enterprise data management remains aligned with business strategy, regulatory requirements, and organizational accountability.

It also helps organizations answer critical questions such as:

• Who owns each dataset?
  
• What policies govern how the data can be used?
  
• Which teams are responsible for maintaining data quality?
  
• How should sensitive data be protected?
  

By establishing clear roles for data owners, data stewards, and governance committees, organizations create the oversight necessary to manage data responsibly.

The role of governance in managing AI training data

As organizations deploy AI systems, the relationship between governance and data management becomes even more important. AI models rely heavily on large volumes of training data, and the quality, provenance, and structure of that data directly influence model performance. 

This challenge is reflected in broader AI adoption trends. Research shows that the majority  of enterprise AI and machine learning projects never make it into production, with underlying data quality, integration, and engineering challenges often driving these failures.

Data governance plays a crucial role in overcoming this challenge with AI training data by ensuring:

• Training datasets are accurate and representative 
• Sensitive data is properly protected 
• Data sources are legally and ethically obtained 
• Metadata and lineage are documented 
• Data usage aligns with regulatory requirements 

These governance practices help organizations meet regulatory obligations such as the General Data Protection Regulation and Health Insurance Portability and Accountability Act, while also ensuring AI models operate transparently and responsibly.

At the same time, strong data management processes ensure that training data can be efficiently ingested, prepared, and delivered to machine learning systems. Together, governance and management help organizations maintain trustworthy AI systems.

How mature data management accelerates AI adoption

Organizations with mature enterprise data management capabilities are better positioned to adopt AI at scale. When data pipelines are well-organized, metadata is documented, and data quality checks are automated, teams can access reliable datasets much more quickly. This reduces one of the biggest barriers to AI adoption: data preparation.

In many organizations, data scientists spend the majority of their time locating, cleaning, and preparing data before they can begin building models. Strong data management processes streamline these tasks by:

• Ensuring data is already structured and documented 
• Providing searchable data catalogs and metadata repositories 
• Maintaining consistent data quality rules 
• Supporting scalable data pipelines for model training 

The result is a faster path from raw data to operational AI systems that requires high-quality data to create accurate and usable outputs. 

Enterprise use cases across industries

The urgency of strong data management and governance is rising as global data volumes continue to expand with the world hitting 180 zettabytes of data last year. Most of it is unstructured and requires advanced tools and policies to make it usable and secure. Across industries, organizations that fail to unify and govern this data struggle to derive reliable insights, increase operational cost, and unlock innovation.

Here’s how industries are tackling this challenge: 

Media and Entertainment

Media organizations generate massive volumes of unstructured video, audio, and image content that must be indexed and enriched before it can produce value. For example, PROGRESS used Veritone Digital Media Hub to process and create metadata for more than 24,000 historic films, making its extensive catalog searchable and monetizable for global customers, which significantly improved content accessibility and licensing revenue opportunities.

Similarly, Veritone recently announced a multi-year, global content licensing agreement with The Washington Post, one of the world’s most respected news brands, to enable broader access to its news content. Under the agreement, Veritone will represent The Washington Post’s video archive for licensing opportunities worldwide, covering breaking news, politics, culture, health, science, and interviews with prominent newsmakers. 

By transforming the news archive with AI-powered metadata extraction and indexing, Veritone helps increase discoverability,making it easier for partners and creators to license, and fully controlled by The Washington Post to maintain editorial integrity.

Sports

Sports leagues and teams face similar challenges managing performance data and multimedia archives at scale. Another powerful example comes from the San Francisco Giants, which used Veritone aiWARE and Digital Media Hub to unlock 60 years of analog media that was previously difficult to access. By automatically transcribing, tagging, and indexing this legacy content, the organization dramatically improved searchability, enabling internal teams to find and activate historical content for fan engagement, broadcast, and marketing more efficiently.

In another case, the LA Chargers leveraged Veritone’s aiWARE platform and cloud‑based metadata tagging to transform what would have taken 371 days of manual cataloging into automated, AI‑powered workflows, unlocking searchable content across decades of games and media while avoiding additional staffing.

Additionally, U.S. Soccer extended its agreement with Veritone to license and monetize archive footage, using AI-powered content licensing to increase fan engagement and revenue from past matches. This kind of data management on the backend enables these opportunities for brands to not only streamline content workflows, but also make it possible to integrate and reuse media across broadcasts, social platforms, and fan engagement channels without manually wrangling terabytes of video and audio.

Public Sector and Law Enforcement

Government and law enforcement agencies are under continuous pressure to process and release large volumes of evidence while maintaining compliance with transparency laws, privacy mandates, and community trust requirements. For example, the Oregon Police Department selected Veritone Redact to automate the redaction of sensitive information in body‑worn cameras and surveillance footage prior to public release. By applying AI‑driven object detection and automated redaction workflows, the department was able to accelerate compliance with records requests, protect individual privacy, and reduce the manual burden traditionally associated with video handling. 

Federal agencies also face growing demands to manage massive volumes of data securely, efficiently, and in compliance with evolving regulations. The Department of War’s AI-first, modular open architecture strategy, issued January 2026, emphasizes rapid AI adoption and interoperable systems.

Veritone’s aiWARE™ platform, a FedRAMP-authorized AI operating system, supports these goals by orchestrating AI models, workflows, and data across federal environments. Already deployed with agencies such as the U.S. Air Force Office of Special Investigations, aiWARE helps transform unstructured data into actionable intelligence while maintaining governance, security, and operational control.

Designed for modular, open architectures, aiWARE integrates commercial, open-source, and proprietary AI models within a single platform and offers flexible deployment options in private AWS or Azure environments. This allows agencies to operationalize AI at scale while preserving security, data residency, and adaptability for evolving mission needs.

By combining robust data management with governance-aligned infrastructure, aiWARE enables federal organizations to accelerate AI experimentation, streamline workflows, and extract value from complex datasets while maintaining accountability and compliance.

Legal and Investigations

The legal industry has seen dramatic benefits from AI‑assisted analysis of large, complex data sets. In a case with TransPerfect Legal Solutions, Veritone Illuminate reduced the eDiscovery review load by 65%, automatically transcribing and filtering more than 517,000 files into a manageable subset for relevant content, saving time and resources while supporting efficient review..

These real‑world examples underscore how integrated governance and data management practices support compliance, efficiency, defensibility, and value extraction in data‑intensive legal scenarios.

Across these environments, organizations must balance data accessibility for analytics and AI with strong governance, security, and compliance controls. Without this balance, the scale and diversity of enterprise data can become a barrier to innovation rather than a foundation for transformation.

Why governance frameworks are the next step

As organizations scale their data environments and adopt AI technologies, the relationship between governance and management becomes increasingly complex. Policies must be enforced across multiple systems. Data pipelines span cloud environments, legacy infrastructure, and third-party platforms. Regulatory requirements continue to evolve.

This complexity makes it difficult to maintain consistency without a structured governance framework. Effective frameworks provide:

• Clearly defined governance processes 
• Formal data ownership structures 
• standardized policies and documentation 
• governance councils and steering committees 
• metrics to measure governance effectiveness 

In the next chapter of this series, we will explore how organizations can design and implement data governance frameworks specifically for AI environments, ensuring policies, technology, and operations remain aligned as data ecosystems grow.

Understanding the relationship between data management and governance is an essential first step. With both disciplines working together, organizations can build the solid data foundation required for trustworthy, scalable AI initiatives.

Download our latest ebook, AI Data Governance for the Enterprise: Solutions for Rights, Privacy, and AI-Ready Activation. 

Sources 

https://www.ibm.com/think/topics/data-quality

https://www.gartner.com/en/information-technology/topics/ai-readiness

https://media.bitpipe.com/io_32x/io_326741/item_2884688/2025-state-of-data-analysts-in-the-age-of-ai-en.pdf

https://www.forrester.com/blogs/b2b-marketing-measurement-isnt-trusted-and-its-about-to-get-worse-a-bonus-prediction/

https://www.seagate.com/stories/articles/seagates-rethink-data-report-reveals-that-68-percent-of-data-available-to-businesses-goes-unleveraged-pr-master/

https://datagardeners.ai/blog/why-ai-models-fail