Implementing data governance roles in a data mesh environment

Enterprise agility is a critical component of business success. For many organizations, this means rethinking paradigms that focus on centralized command and control, focusing instead on decentralized authority and accountability for business outcomes. This shift in thinking requires businesses to not only recognize the strategic value of data but also prioritize the importance of managing data as a product. To that end, many organizations today are exploring – and embracing – a data mesh environment.

Data mesh emphasizes decentralized data ownership and domain-oriented architecture to deliver high-quality data views and perspectives packaged as business-relevant data products. By deconstructing traditional, monolithic data architecture into decentralized components, data mesh enables better support for specific business domain needs.

Moving to a data mesh architecture requires transforming how organizations manage data governance. In a data mesh architecture, data governance requires a federated approach. Organizations must balance the demand for domain team authority with the need for cross-domain interoperability, with data governance managers playing a pivotal role when it comes to implementing data governance practices and technologies such as a data catalog.

Understanding data mesh and its implications

Data mesh is an approach to data architecture where independent domain teams hold and maintain responsibility for managing their data. In a data mesh environment, business users receive relevant, timely, high-quality data views and perspectives, packaged as data products, that incorporate all the functionality required for a specific business need.

Four key principles of data mesh

Data ownership by domain: In a data mesh environment, data teams are embedded within the business and dedicated to those specific teams. They are both responsible – and accountable – for producing analytical data models with their data and for ensuring the quality, usability, and curation of those models, with the understanding that others across the organization could use their data as well.

Data product development: A second principle of data mesh is data as a product. Treating data as a product requires the data team to understand how their business unit will use the data, as well as what tools they want to use to consume it. Data teams must also consider the data quality, documentation, discoverability, and ease of use as key aspects of the data products they develop.

Self-service data: Data products promote a self-service approach for data users. Using native tools and approaches, they can access and use data products quickly, with an expectation that the underlying data is trustworthy, interoperable, and secure.

Collaborative data governance: When data teams are distributed within the business, it’s imperative that organizations have strong data governance standards in place to ensure interoperability, security, and compliance across the platform. By combining a decentralized approach to data management with federated data governance, organizations can easily add new data teams at any time, enabling them to scale as business needs evolve.

Because data ownership lives at the domain level, it’s important for organizations to clearly define roles and responsibilities between centralized data governance teams and decentralized domain teams. Coordination is key to avoiding conflicts when it comes to managing governance policies, workflows, and boundaries. As well, it will also aid the organization when it comes to setting up and maintaining the data catalog.

Data mesh teams step-by-step

Defining each team’s responsibilities is an important first step. When thinking about domain data teams, it’s important to set boundaries. The best practice is that one domain aligns with one business capability. Putting this into practice, however, is not quite so straightforward.

• To begin, analyze the analytical requirements for the business domain. What specific business metrics or business outcomes is the team trying to achieve? Then, define the required entities and attributes, along with the aggregates and hierarchy needs.That way, you can create context for the domain model. Finally, you’ll want to map the connections to other domains, creating a visualization that highlights shared entities and attributes. Don’t worry too much about technology at this point. Instead, focus on where domains interoperate. And remember, domains are fluid. They can – and will – evolve as business needs change.
• Next, you’ll want to define the responsibilities of the central governance team versus the role of individual domain teams. Establishing clear boundaries will not only clarify roles and responsibilities but it will also prevent conflict or oversights.Document the processes and workflows you’ll use to coordinate team activities. And implement a communication process that increases visibility across teams and fosters trust between them.

Remember, the goal is to create an efficient, effective operating model that increases the quality, consistency, and interoperability of data products across domain teams. Done right, this operating model will provide a solid foundation for the implementation and upkeep of the data catalog, as every team will understand their specific responsibilities as it relates to keeping it up to date.

6 essential data governance roles in a data mesh

Data governance is a critical aspect of data mesh as it helps to ensure the proper management, quality, and security of the data across data domains. Common data governance roles within a data mesh include:

1. Data Governance Manager

The Data Governance Manager plays a crucial role in the implementation of data mesh: Their job is to set overarching governance policies and define standards that domains can adopt in order to ensure the interoperability of data products across the organization. By defining governance policies and standards centrally, organizations can ensure that distributed teams all adhere to the same set of rules, making it easier to drive greater value from data throughout the organization.

2. Domain data owner

The domain data owner is responsible for the data within a specific domain. Their job? To ensure its quality, integrity, and compliance with relevant regulations. This individual also collaborates with other domain data owners across the organization to define data standards and best practices.

3. Data Product Manager

The Data Product Manager is an emerging role. Similar to traditional product managers, their role is to define and manage the roadmap, features, and priorities for the data products within their domain. Part technical, part business-savvy, these individuals must possess a level of technical know-how while also understanding the challenges their domain is looking to solve.

To ensure the success of their respective data products, data product owners work closely with other data-related roles, including data engineers and data scientists, as well as business stakeholders to develop data products that best address their needs.

4. Data platform owner

A data platform owner is responsible for the infrastructure that supports the development, deployment, and ongoing maintenance of data products. Their job is to ensure the platform meets the needs of various stakeholders and aligns with defined standards and policies. A key element of the data platform is the data catalog, which provides clarity about data definitions, lineage, and other essential business attributes so all users can understand and leverage their data as an asset.

5. Data stewards

Data stewards play a critical role when it comes to supporting the data catalog. Not only do they help to ensure their domain data is of high quality, but they also work across the domain to spot and correct data quality issues as they arise. Data stewards also help to maintain metadata and collaborate with others across domains so their data sets are accessible and easy to understand. Finally, data stewards are instrumental in enforcing governance policies and standards, helping safeguard the domain from issues related to data security, data privacy, and compliance.

6. Data governance board

The data governance board plays a critical role when it comes to overseeing and enforcing data governance policies and standards across disparate data domains. Made up of representatives from each of the data domains, as well as data product managers and other relevant stakeholders, the data governance board tackles data governance-related issues and decisions that cross the various domains within the business.

Implementing roles within a data mesh

For many organizations, data catalogs form the foundation for successful data governance in a data mesh. However, to ensure effective data governance, data discovery, and data collaboration, organizations must consider how they implement data governance roles within the data catalog.

Defining clear roles and responsibilities for centralized and domain-specific teams is a good place to start. For each role, specify the responsibilities as well as the permissions needed within the data catalog to fulfill those responsibilities. That way, you can ensure clarity and accountability for the various tasks that need to occur to keep data accurate, up-to-date, and secure.

It’s also important to involve the data governance board as you define roles and policies within the data catalog, as they can help resolve governance issues and decisions as they relate to metadata management and data catalog access and usage.

Finally, each role must understand the importance of collaboration and knowledge sharing across the business. Encourage users within each domain to tag and annotate datasets within the catalog with relevant information. Not only will this enrich the data catalog, but it will also provide other users across the organization with relevant context about the data they’re looking to use in their decision-making process.

Strategies for collaboration

Prioritizing cross-domain training and communication ensures users have a clear understanding of their roles and responsibilities as they relate to the usage and maintenance of the data catalog. In fact, effective communication and collaboration are key to successfully implementing roles and responsibilities within the data catalog.

Creating and sharing documentation about the data products within a data catalog helps users trust the data they are accessing. By providing valuable information about the semantics, syntax, and schema, users can gauge if the data product meets their needs. Providing additional information such as the data’s lineage and provenance further defines the data product’s trustworthiness, giving users insight into how the data has changed over time.

Further, organizations should establish clear channels for communication. Doing so fosters an environment where users from different domains can share, discover, and understand data assets curated from across the business. Metadata annotations within the data catalog are an easy place for users to document relevant, contextual information about the dataset.

Taking a collaborative approach helps to establish well-documented and accessible data products that align with the needs of each domain, resulting in more efficient workflows, higher data quality, and a more integrated data landscape.

Measuring role success within a data mesh

To gauge the success of the roles within a data mesh, organizations can review several different metrics, including:

• Data catalog engagement: Assess usage, searches, and contributions to understand the value the data catalog provides to the domains.
• Data product usage: Determine how frequently – or not – domains are using data products within the data catalog. High usage equals high value!
• Data product quality: Measure the accuracy, completeness, and timeliness of the data product to understand its reliability.
• Alignment with business objectives: Understand how well – or not! – the data products within your data catalog contribute to achieving business outcomes.
• Adherence to data governance policies and standards: Gauge the effectiveness of roles by determining how well they align with governance principles as well as regulatory and compliance standards.
• User feedback: Gather feedback from domain users to understand how the data products and data catalog supports decision-making. Use surveys and other mechanisms to identify opportunities for improvement

Adapting roles as data mesh evolves and changes is crucial to its success. As the needs of the organization change, it’s likely that you’ll need to make changes to domains as well as the roles that support them Design roles with flexibility in mind. It’s inevitable that your data landscape will change over time, and that responsibilities will follow suit. Offer individuals within your organization the opportunity to learn new skills and explore emerging technologies. That way, when the time comes for roles within your data mesh to change, your team will be prepared with the skill sets needed to adapt.

Further, develop data governance practices so that they can scale as the organization grows and changes. New domains, new data products, and new users will all require your data governance practices to evolve so you can remain consistent and compliant.

Agility and adaptability define successful data mesh implementations. By embracing a culture of flexibility, collaboration, and continuous improvement, you can ensure the roles within your data mesh will remain agile and can flex to meet the evolving needs of the business.

In conclusion

Data mesh provides organizations with a way to decentralize data ownership, promoting greater accountability within each business domain. To be successful with data mesh, organizations must not only establish a data governance framework, but also clearly define the roles and responsibilities needed to manage the data catalog and the data products within it. Doing so will foster an environment of effective and efficient communication and collaboration that drives better business decision-making.

As organizations embrace decentralized approaches to data management, the data catalog like DataGalaxy’s Data Knowledge Catalog emerges as a critical tool to foster greater collaboration across domains. Using DataGalaxy’s Data Knowledge Catalog, organizations can foster greater collaboration across diverse domain teams and data sets.

By providing greater clarity into data definitions and data lineage, DataGalaxy makes it easy for organizations using data mesh to better understand and use their data as a strategic asset. With user-friendly features including a Business Glossary, intuitive visualization tools, detailed data lineage, and natural language search, DataGalaxy’s Data Knowledge Catalog is instrumental in enabling domain-specific teams within a data mesh to share knowledge, adhere to governance policies, and promote a culture of data-driven decision-making.

Still have questions about data governance? Turn to DataGalaxy to create your company’s data lineage mapping, develop a standardized business glossary, and much more! Check our calendar and select a date that works for you. Jumpstart your free 15-day platform trial access & start making the most of your data today!