The practical approach to building a data mesh [Q&A]

As businesses continue to generate and rely on vast amounts of data, the traditional approach to managing that data is no longer sufficient.

Enter the concept of a data mesh -- a decentralized, domain-driven approach to data architecture that promises to transform how organizations handle and leverage their data. But the question remains: should a business create a data mesh? What value does it add, and what challenges does it help solve?

We spoke with David Eller, data product manager at Indicium, to explore the key advantages of adopting a data mesh, including greater agility, scalability, and data ownership.

He highlights the obstacles businesses may encounter when implementing this approach, from cultural resistance to technical hurdles.

Finally, he offers actionable strategies to overcome these challenges, ensuring that organizations can make the most of a data mesh and its potential to drive more efficient, data-driven decision-making.

BN: Why (or why not) should a business create a data mesh? What value does it add and what challenges does it solve?

DE: Adopting a data mesh can bring significant value to a business by enabling decentralized data ownership, allowing teams with the deepest domain expertise to manage and govern their own data. This shift reduces bottlenecks, improves agility, and ensures that decision-making is based on the most relevant and up-to-date insights. It also alleviates common challenges seen in centralized data architectures, such as slow access to data, overburdened data teams, and rigid pipelines that fail to scale with business needs.

However, data mesh is not a one-size-fits-all solution. It requires a cultural and organizational shift beyond technology alone. Companies that embrace cross-functional collaboration, agile decision-making, and data-driven processes are more likely to benefit from its adoption. Conversely, businesses with rigid structures, highly centralized control, or resistance to change may struggle to implement it effectively, encountering more friction than value.

Ultimately, a data mesh is most valuable for organizations that prioritize adaptability, scalability, and business-driven data strategies. Its success depends not just on architecture but also on whether the company is prepared to evolve its processes, governance, and culture to fully leverage the benefits of decentralization.

BN: What are common challenges businesses face when creating a data mesh?

DE: Implementing a data mesh presents several challenges that go beyond technology, spanning financial, technical, and cultural dimensions. One of the biggest hurdles is organizational resistance to decentralization. Many businesses are accustomed to a centralized data team, and shifting ownership to domain teams requires a cultural shift that some may resist, either due to lack of expertise or concerns about added responsibilities. This decentralization also introduces the risk of inconsistent data standards across teams, making governance a critical but complex challenge. Without clear guidelines, teams may develop data products in silos, leading to interoperability issues and data quality inconsistencies.

From a technical perspective, implementing a data mesh requires robust infrastructure to support discoverability, interoperability, and self-serve capabilities. Many organizations struggle with setting up scalable data architectures that balance autonomy with governance. Ensuring proper access control, metadata management, and lineage tracking is crucial but can be difficult to enforce in a distributed environment. Additionally, integrating legacy systems with a decentralized data approach can be costly and complex, requiring significant investment in modernization.

Financially, a data mesh is not a low-cost initiative. While it promises long-term efficiency, the upfront costs of training teams, implementing new governance frameworks, and investing in the necessary technology stack can be substantial. Companies that do not plan their transition carefully may experience inefficiencies, redundant efforts, and escalating costs.

Ultimately, the success of a data mesh depends on an organization's ability to align culture, processes, and technology. Businesses that lack a strong data-driven culture or fail to establish clear ownership and governance frameworks may find that the challenges outweigh the benefits. However, for those willing to invest in structured implementation and continuous adaptation, a data mesh can lead to more scalable, agile, and value-driven data practices.

BN: What are some actionable strategies for overcoming those challenges?

DE: Overcoming the challenges of implementing a data mesh requires both strategic alignment and practical execution. At a high level, businesses need to shift from a centralized data management model to a decentralized, domain-driven approach, where data ownership is embedded within business teams. However, this must be balanced with a federated governance model that ensures interoperability, security, and data quality across the organization. Without clear guidelines and support, decentralization can lead to silos rather than agility.

To make this shift operational, organizations should assign data product owners in each domain, responsible for maintaining and documenting their data. These teams need training and support from a central data enablement team or center of excellence (CoE) to standardize best practices. A self-service data platform should be established, providing domain teams with access to automated ingestion pipelines, pre-configured governance policies, and tools like data catalogs and automated data lineage tracking to ensure discoverability and quality control. Security and compliance must be maintained through role-based access controls (RBAC) and identity and access management (IAM) policies.

Financially, companies should take an incremental approach, starting with a pilot implementation in a high-impact domain to showcase quick wins—such as faster decision-making, reduced data bottlenecks, or increased efficiency. Rather than enforcing adoption, organizations should provide clear incentives for domain teams to take ownership of their data by demonstrating tangible business impact.

Ultimately, the success of a data mesh depends on continuous iteration. Businesses should track adoption metrics, monitor the effectiveness of governance policies, and refine their approach based on real-world feedback. Instead of treating it as a one-time transformation, data mesh should be embedded into the company’s long-term data strategy, ensuring teams have both the autonomy and the structure needed to succeed.

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