Unlocking the Power of Data: Introducing Datamesh Architecture

 

Many organizations have invested in a central data lake and a data team with the expectation to drive their business based on data. However, after a few initial quick wins, they notice that the central data team often becomes a bottleneck. The team cannot handle all the analytical questions of management and product owners quickly enough. This is a massive problem because making timely data-driven decisions is crucial to stay competitive.


On the other hand, organizations have also invested in domain-driven design, autonomous domain teams (also known as stream-aligned teams or product teams) and a decentralized microservice architecture. These domain teams own and know their domain, including the information needs of the business. They design, build, and run their web applications and APIs on their own. Despite knowing the domain and the relevant information needs, the domain teams have to reach out to the overloaded central data team to get the necessary data-driven insights.

With the eventual growth of the organization, the situation of the domain teams and the central data team becomes worse. A way out of this is to shift the responsibility for data from the central data team to the domain teams. This is the core idea behind the data mesh concept: Domain-oriented decentralization for analytical data.

 

 

What is Datamesh?

According to the founder of data mesh Zhamak Dehghani, “Data mesh is a decentralized sociotechnical approach to share, access, and manage analytical data in complex and large-scale environments—within or across organizations.”

A data mesh architecture is a decentralized approach that enables domain teams to perform cross-domain data analysis on their own. It treats data as a valuable product and encourages distributed ownership, self-service access, and domain-oriented teams. By adopting the principles of Datamesh, organizations can break down data silos and empower teams to become more autonomous and efficient in managing their data assets.

 

The 4 main principles of Data mesh include:

·        Domain ownership enables the process of decentralization by aligning technical and business teams. A decentralized domain-oriented approach optimally places the responsibility of turning data into information onto the domain that created the data in the first place. Each domain is accountable and responsible for data quality and completeness of what is shared with the organization.

·        Data as a product means applying product development thinking to data solutions. In this model, company data is viewed as a product and the data team’s role is to provide that data to the company in ways that facilitate good decision-making and meet company objectives. Data as a product also necessitates that products be valuable on their own to reduce interdomain dependencies that slow down progress of solution development.

·        Self-serve data platform allows for a framework for everyone within the organization to discover and use data products without having to develop particular point solutions or data pipelines to share information. This empowers cross-functional teams to share data seamlessly with one another.

·        Federated computational governance clearly defines parameters for how data can be shared and used—especially accounting for legal considerations, compliance mandates, and security protocols. It also enables correlation of independent yet interoperable domains, as well as codifying and automating policies—especially in avoidance of manual processes.

 

Advantages of Datamesh:

  • Implementing Datamesh can bring several benefits to organizations:
  •  Improved data accessibility and availability
  •  Enhanced data integration and reduced data silos
  •  Increased agility and faster decision-making
  • Fostered collaboration and knowledge sharing across teams
  • Strengthened data governance and data quality
  •  Scalability and performance optimization

 

Challenges and Considerations:

While Datamesh offers promising advantages, it's important to consider potential challenges, such as:

  • ·       Aligning existing data infrastructure
  • ·       Establishing effective governance frameworks and
  • ·       Managing the cultural shift towards decentralized data ownership

If you plan to adopt data mesh, your data strategy must address human interaction in central focus. Because sharing data among teams is at the core of data mesh, the main challenges you will face will more likely be related to human behavior than technical competency.

 

 

Conclusion:

Datamesh represents a new era in data architecture, empowering organizations to unleash the true potential of their data assets. By embracing decentralized data management, treating data as a product, and enabling self-service access, organizations can break free from data silos and embark on a journey towards greater agility, collaboration, and data-driven decision-making. Embracing Datamesh is not just a technological shift but a cultural transformation that can revolutionize the way organizations leverage their data for success in the digital age.

 

 

Reference: https://www.datamesh-architecture.com/

https://www.analytics8.com/blog/what-is-data-mesh/

 

 

-Ayushi pandey

Intern @ Hunnarvi technologies in collaboration with Nanobi Data and Analytics

#DataManagement #DatameshArchitecture #DigitalTransformation #DataDrivenDecisionMaking #Nanobi #Hunnarvi #ISME

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