Data Governance vs. Data Management

Thanks to the ubiquity of cloud storage and computing, coupled with the explosion in AI and ML applications, the data landscape is shifting before our eyes. Technologies that worked seamlessly just a decade ago – some even more recently – can no longer keep up with the speed and scale of modern analytics.

At the same time, the volume of data users and assets continues to grow at a rapid clip. As these trends converge, implementing effective data governance and management strategies is becoming increasingly difficult, creating a dangerous ripple effect across the data security landscape. In a survey of 700 data professionals, one-third said a lack of visibility into data sharing and usage was their biggest security challenge.

While data governance and management are central to solving this challenge, they are not necessarily well understood. Recognizing their differences – as well as how they work together – is key to implementing them in a scalable, efficient, and effective way. In this blog, we’ll explore where these disciplines intersect and diverge, and how you can harness them both to de-risk data and derive new value from it.

What is Data Governance vs. Data Management?

Data governance primarily focuses on establishing processes and frameworks to ensure that data is properly used, protected, and maintained throughout its lifecycle. This involves defining ownership, stewardship, and accountability for data assets, as well as complying with compliance laws and regulations.

Data management, by contrast, is more operationally focused. It includes the processes and technologies needed to collect, store, organize, process, and analyze data, while ensuring that data is secure and accessible for decision making.

Here are the key elements of each practice:

Data Governance Data Management
Primary Focus
  • Establishing processes and policies to increase data’s value without compromising security or integrity.
  • Establishing and implementing operational needs to manage data throughout its lifecycle.
Objectives & Goals
  • Ensuring data integrity, security, and compliance.
  • Defining data ownership, stewardship, and accountability.
  • Facilitating efficient data collection, storage, processing, and analysis.
  • Ensuring accessibility, usability, and scalability.
Key Components
  • Data policies
  • Data stewardship
  • Data quality management
  • Compliance with rules and regulations
  • Data acquisition and integration
  • Data storage and architecture
  • Data processing and analysis
  • Data security and access control
Primary Stakeholder(s)
  • Data/Domain Owners
  • Data Platform/Architecture/ Engineering Teams
  • Organizational buy-in and change management
  • Data silos
  • Evolving regulatory requirements
  • Integration and scalability complexity
  • Accuracy and consistency
  • Security vulnerabilities
  • Improved data quality
  • Informed decision making
  • Regulatory compliance
  • Risk mitigation
  • Improved accessibility and usability
  • Better operational efficiency
  • Scalability and innovation

Differences Between Data Governance vs. Management

Key Components

Data Governance
Since modern data governance helps determine how data should be used, frameworks that assign roles and responsibilities are key.

Governance policies clarify who owns and maintains data, including how it may be used and who may use it. Therefore, data or domain owners – who have the most business context about their data – are highly involved in governance processes. Governance frameworks also align with data compliance regulations to provide a standardized approach to risk mitigation.

Data Management
Since data management is operationally focused, processes and technologies that make data usable are essential.

Data infrastructure – including how data is collected, stored, processed, and aggregated – is central to cloud data management. That’s why data platform, architecture, and engineering teams are key stakeholders. And while data governance defines how to protect assets in order to achieve compliance, data security and access control enforcement fall within the purview of data management.


Data Governance
Old habits die hard, which is why implementing new governance frameworks and policies is often met with resistance. Once you secure organizational buy-in, you must train stakeholders on new standards and procedures. In the process, you’ll need to identify and eliminate data silos, which block transparency and impede centralization efforts. This may delay the success of governance programs – but the long term results are worthwhile!

Additionally, a resilient data governance strategy must take into account evolving data rules and regulations. Staying up-to-date with relevant data security laws and industry standards may require a dedicated resource who can provide guidance on how to adapt and remain compliant.

Data Management
Unlike the culture and change management issues facing data governance efforts, technological challenges are a likely hurdle for data management.

If done manually, integrating data across diverse data sources and formats – while maintaining accuracy, consistency, security, and integrity – is highly complex and time consuming. Legacy or native solutions are often too slow, too inflexible, or too platform-specific to scale efficiently, leading to inconsistencies, errors, or system vulnerabilities. Amid an explosion in data volumes and users, any of these outcomes could easily dismantle data management objectives and introduce greater risk.


Data Governance
If implemented effectively, a governance strategy improves data quality and accuracy, giving you peace of mind that your data is reliable and trustworthy. Standardizing data policies and procedures ensures that data is handled appropriately, and provides visibility into usage across your organization. This drives enhanced decision-making, efficiency, and compliance, allowing you to mitigate risks like data breaches, legal penalties, reputational damage, and lost customer trust.

Data Management
While data governance improves your data’s usability (i.e. its relevance, reliability, and applicability for your use cases), data management increases its utility (i.e. its accessibility and availability for data consumers to use it). This is a nuanced but important distinction; since data management focuses on integrating technologies in an efficient architecture, it facilitates better data access and utilization.

As a result, you’re able to streamline data processing, analysis, and storage, while reducing manual effort and resource needs. This accelerates speed to insights, making data a strategic asset that can be used across functions to grow, innovate, and compete.

Bridging the Gap Between Data Governance & Management

Despite their differences, data governance and data management are intertwined processes. Together, they are necessary to build an effective data security strategy.

Data governance is the foundational framework for data management. It defines the policies, standards, and guidelines for how data should be managed, protected, and utilized. If data governance were the coach of a sports team, data management would be the players that execute the game plan. Everyone is on the same team – they just play different roles.

Data governance also establishes clear responsibilities and accountability for data stewardship, which clears the path for consistent, efficient data management. When it’s clear who is responsible for data, teams work more collaboratively and trust that data is accurate and high quality.

As decentralized data architectures like data mesh continue to grow in popularity, defining processes and responsibilities will profoundly impact data initiatives’ success. For Roche, a global leader in biotech, standardizing and centralizing data governance was essential for implementing management practices at the domain level and operationalizing their data mesh architecture.

“We were trying at Roche to force a very central monolithic analytics culture on a very decentralized company,” said Paul Rankin, former Head of Data Management Platforms at Roche. “And I think that it was quite attractive that we could now see that if we empower…those domains to be accountable for their own data, for their own pipelines, for their own governance, in some way this whole different mindset and way of thinking is maybe the way forward for Roche.”

Aligning data governance and management allowed Roche to delegate data ownership to domains. As a result, their domain teams built 200 new data products and generated over $50M in benefits, earning them IDC’s Future Enterprise Best in Future of Intelligence EMEA Award.

Putting Data Governance & Management into Action

To truly bridge the gap between data governance and data management, you need a tool that integrates and automates frameworks and processes. Automatically enforcing policies and providing visibility into how data should be used – and how it’s actually being used – reduces manual effort, minimizes risks, and eliminates inconsistencies. With efficiency, transparency, and continuous data monitoring built into your system, you’re better able to achieve compliance and de-risk data, so you can deliver new value and innovation.

To get the blueprint for implementing a cohesive data governance and management system, download the How to Build a Governance, Risk, and Compliance Framework for Data Analytics playbook.