It’s easy to make the case for building a data mesh within your business. By enabling a decentralized approach to data management, data meshes can improve agility, reduce bottlenecks, and help teams make decisions based on the most recent, accurate information.
What’s often harder, however, is actually implementing a data mesh — and not only because of the technical complexity of data mesh technologies. Businesses must also navigate cultural, financial, and organizational hurdles. Failure to get ahead of the challenges in these domains could result in a situation where a company invests in expensive data mesh technology while reaping little value in return.
I’m writing this article to help organizations avoid this pitfall. Drawing on my extensive experience helping businesses implement data meshes in the real world, I’d like to offer guidance on what it takes to implement a data mesh that delivers real value, and how to handle the many challenges that can arise during the process of data mesh adoption.
The What and The Why of Data Meshes
A data mesh is a framework that decentralizes data within an organization, making it possible for different groups within the business to access, manage, and analyze data on a self-service basis.
This approach makes data management more agile, scalable, and effective than the traditional strategy of relying on a team of data professionals to serve as brokers for data within a company. With a data mesh, a department that wants to analyze information doesn’t need to issue a request to data experts and wait for them to fill it. Instead, business users can serve their own requests.
Given these benefits, it’s unsurprising that 60% of businesses with at least 1,000 employees are already using data meshes, with evidence suggesting that this figure will continue to grow steadily in the coming years.
The Challenges of Successful Data Mesh Implementation
Deciding to implement a data mesh is one thing. Implementing one that delivers true value and is not hindered by cultural, technical, and financial challenges can be quite another.
The most common barriers that businesses run into when attempting to deploy and adopt data meshes include the following.
Resistance to Decentralization
At an organizational and cultural level, businesses often find it hard to adjust to a data mesh approach because they’re accustomed to a more centralized data management model. Shifting ownership of data management and analytics to teams across the organization requires a cultural shift that some may resist, either due to lack of expertise or concerns about added responsibilities.
In addition, the decentralization that accompanies data mesh technology 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 within silos. This leads to interoperability issues and data quality inconsistencies and undercuts the purpose of implementing a data mesh in the first place.
Technical Barriers
From a technical perspective, the main challenge is not finding a data mesh to deploy; many vendors offer data mesh platforms that are easy enough to set up. Instead, the issue is implementing an underlying data infrastructure that enables discoverability, interoperability, and self-service capabilities while also enforcing effective governance controls. Businesses also often need to address requirements like data lineage tracking, metadata management, and access controls, all of which are technically complex to implement in a distributed data environment. The need to integrate with legacy systems exacerbates the problem further.
These challenges are all solvable with the proper investment of time, effort, and expertise. But too often, organizations make the mistake of thinking that implementing a data mesh boils down simply to setting up a decentralized data platform and calling it a day. In reality, there is much more to address in areas like governance, visibility and integration.
Financial Hurdles
Data meshes can create substantial value over the long term, but in the short term, implementing them is typically a costly initiative. Businesses must invest in training teams, implementing new governance frameworks, and building out the technology stack necessary to sustain an effective decentralized approach to data management.
Organizations that fail to plan their transition carefully may run into financial inefficiencies, redundant efforts, and spiraling costs, all of which can doom the long-term ROI of data mesh technology.
Best Practices for a Successful Data Mesh Roll-Out
While the challenges to successful data mesh adoption are significant, the good news is that actionable strategies exist for mitigating them.
1. Assign Data Product Owners
To help bring order and consistency to decentralized data management, it’s best practice to assign data product owners within each business domain. The product owners should be responsible for maintaining and documenting the data that their business unit uses and developing best practices for members to follow when working with data.
2. Preconfigure Data Access Processes
The more automated controls a business builds into its data mesh, the easier it is for business users to benefit from decentralized data without undercutting data governance or quality priorities. Rather than leaving it to users to adhere to best practices solely of their own volition, self-service data platforms should include automated ingestion pipelines, pre-configured governance policies, automated lineage tracking and data catalogs (which help streamline the data discovery process).
3. Start Small and Scale Up
To mitigate the financial risks surrounding data mesh adoption, companies should take an incremental approach. They should start with a pilot implementation within a high-impact domain that can showcase the value of a data mesh — such as enabling faster decision-making or reducing data bottlenecks. From there, they can extend the data mesh to support other business domains, gradually scaling up until they’ve implemented a decentralized data platform that addresses the needs of the entire organization.
4. Incentivize, Rather Than Impose, Data Mesh Adoption
Instead of creating mandates for business users to adopt a data mesh, it’s a best practice to incentivize teams to make use of decentralized data management of their own accord. Organizations should make the value of the data mesh readily visible such that users begin turning to it whenever they need to work with data, rather than sticking with the slower, less efficient approach of centralized data gatekeeping.
5. Iterate and Improve
The long-term success of a data mesh depends on continuous iteration and improvement. Businesses should track adoption metrics, monitor the effectiveness of governance policies, and refine their approach based on real-world feedback from users. Instead of treating data mesh adoption as a one-time transformation, an organization should embed its data mesh into its long-term data strategy and expect to grow with the platform over time.
