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10 benefits and challenges of data mesh

Strategy
  • Evan Smith

    Evan Smith

    Technical Content Manager

    Starburst Data

  • Monica Miller

    Monica Miller

    Developer Advocate

    Starburst

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Data mesh is a modern data architecture. It offers numerous advantages for organizations that adopt it to transform their business processes into a decentralized model. It directly empowers data consumers, allowing them to interact more directly with data sources. Furthermore, data mesh can be utilized to support various workloads, including data analytics, Artificial Intelligence (AI), and data applications. This article highlights the 10 benefits and challenges of data mesh, allowing you to deploy this data architecture in a way that empowers the data teams across your organization. 

5 Benefits of data mesh

#1 Increased efficiency

Data mesh is a more efficient data architecture compared to traditional ETL pipelines, particularly when utilizing central IT Teams

The image below shows a typical bottleneck created through a centralized IT team.

#2 Empower individual domains

With data mesh, every business division and team is empowered to use the data-as-a-product methodology to solve problems and add value. This leverages the expertise of the whole organization to help drive insights, improving the self-sufficiency and agility of domains. 

The owners of each domain are best positioned to understand the context of their data, and empowering them to interact with datasets directly eliminates many potential points of confusion between teams. 

The image below shows how data mesh empowers individual domains.

#3 Increase sharing across domains 

Data mesh, using data-as-a-product, enables organizations to expose data to all domains. This empowers sharing and collaboration across teams. It also avoids duplication of effort and helps ensure agility. 

The image below illustrates how different domains can share data as a product, thereby increasing agility.

#4 Maintain strong data governance and security

Successful data mesh projects rely on robust data governance to maintain the integrity of the shared datasets. Security and compliance will remain at the forefront of concern. 

The image below illustrates how the four pillars of data mesh collaborate to facilitate data sharing across domains while maintaining strong governance. This will have a significant impact on enterprises interested in becoming data-driven businesses. 

5 Implementation challenges of data mesh

For many enterprises, implementing a data mesh architecture represents a significant shift in the organizational structure of data management. Not all businesses are prepared to undertake a change of this magnitude. For this reason, it is essential to keep the following points in mind.

#5 Requires focus on essential business problems 

Data mesh is an organizational approach that often helps solve business problems more quickly when adopted. To benefit from this, it is essential to keep the business problems themselves in focus. This will help to determine the correct course of action for a given business. 

To assist with this, it is usually a good idea to include a description of how data mesh will be adopted and the impact that is hoped to be achieved.

#6 Requires significant effort

Data mesh requires a significant effort. Understanding the size and scope of this change is necessary to avoid unexpected difficulties in its implementation. As with any significant change, if poorly defined, it can lead to problems and exacerbate underlying shortcomings in current business processes.

#7 Requires a robust change management strategy  

To help manage the project’s risks, a thorough, concrete, and realistic change management strategy must be developed before deploying a data mesh architecture. This must include attention to all four pillars, particularly federated computational governance, as it sets the ground rules for the system as a whole.

#8 Must be tailored to individual businesses 

Each business is unique, and so too are its data mesh needs. 

To succeed, the change management strategy must take into account many factors, including: 

  • The size, scope, and type of datasets involved.
  • The current central IT processes.
  • The current nature of the domains. 
  • The desired future state of the domains and their relationship to data-as-a-product. 
  • The objectives of a decentralized paradigm relate to the business questions unique to the business.

#9 Requires buy-in from domains and management

Like other major projects that impact the business at a core operational level, data mesh requires buy-in from all key stakeholders. At a minimum, this typically includes central IT teams, individual domains, and management. The plan should be communicated clearly to all teams, and the changes in their roles and responsibilities must be articulated directly.

#10 You must decide the scope of data mesh adoption early in the project

Data mesh is not a one-size-fits-all solution. It is a framework for organizational change that offers greater agility in most circumstances. Organizations can adopt some of the principles of data mesh without others. For example, data mesh could be phased in at a department-by-department level. The risks and rewards of each level of adoption should be identified early in the project and communicated to all team members.

Learn more about data mesh

Download the free book by Zhamak Dehghani, Data Mesh: Delivering Data-Driven Value at Scale

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