Data Governance vs Data Management

With the introduction of recent privacy regulations like GDPR, data governance has become a hot topic for business everywhere but what’s the difference when it comes to data governance vs data management?  

In this guide, we’ll explore data governance vs. data management principles and how you can build an effective data governance plan for your business.


Understanding the difference between data governance and data management

Data governance vs. data management
Definition Data Governance Data Management
Data governance defines how data is accessed and treated within a broader data management strategy. Data management is the implementation of architectures, tools and processes to achieve stated data governance objectives.

What is Data Management?

According to DAMA International, the organization for data management professionals, data management is defined as;  “the development and execution of architectures, policies, practices and procedures that properly manage the full data life-cycle needs of an enterprise.”

If this seems like a broad definition, that’s because it is!  Born out of the computer revolution of the 1980s, the concept of ‘data management’ has had to evolve over the years from more technical constructs related to computer data storage to properly encapsulate all disciplines related to managing data as a valuable resource.

These days it’s most useful to think about ‘data management’ as an overriding umbrella term for all practices which relate to the development, execution and supervision of data and information assets. Within this framework, data governance should be considered an important sub-branch of data management.

Data Management
  1. Data governance
  2. Data Architecture
  3. Data modelling & design
  4. Database & storage management
  5. Data security
  6. Reference & Master Data
  7. Data Integration & Interoperability
  8. Data Warehousing & Business Intelligence
  9. Metadata management
  10. Data Quality

What is Data Governance?

Data governance is a key component in any enterprise data management strategy and relates to the way data is managed and protected as an asset.

Data governance can be best understood as the application of policies, people, processes and technology to create a consistent and appropriate use of an organization’s data.

Building and implementing a strong data governance strategy has many tangible benefits:

  • Enforcing roles and accountability for data management
  • Maintaining regulatory compliance
  • Standardising data management and utilisation
  • Reducing inefficiencies and costs for data management
  • Improving data security by defining and verifying the requirements for data access
  • Establish processing for data access to improve performance

Why is data governance important?

In an era of emerging regulations like GDPR and CCPA, and high-profile data leaks such as the Facebook / Cambridge Analytica scandal, data governance has become a top priority for data executives.

It’s no longer enough to just ‘manage’ your data records effectively, it’s now important for business to govern who can access and use data assets. An effective data governance framework helps businesses ensure that they have the necessary privacy and information security controls in place when moving or giving access to data.

How data governance and data management work together

Data governance is an important component in any corporate data management strategy, but it cannot work in isolation.

Data management policies and procedures are still required to ensure that data is collected, structured, organized and stored in appropriate ways.

Only when the appropriate data collection, classification, architecture, metadata management, quality control methods and integration mechanisms are in place can a data governance framework be implemented.

After all, you cannot govern data if you cannot define what it is, how it was collected, where it is currently stored and how it can be accessed.

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