DATA GOVERNANCE AND DATA MESH: OPPORTUNITIES AND CHALLENGES

In today's data-driven world, Data Governance has become an increasingly critical issue for organizations. Data Governance is managing data availability, usability, integrity, and security. As companies collect, analyze, and use large amounts of data, it is essential to ensure that it is accurate, consistent, and available to the right people at the right time.

One of the latest approaches to managing data is Data Mesh, which has been gaining attention as a way to address some of the challenges of traditional data management. In Data Mesh, data products are managed as independent, self-contained units, with each product responsible for its own data quality, governance, and accessibility.

As Data Mesh gains popularity, it is essential to consider the role of Data Governance in this new approach. This article will explore the opportunities and challenges of implementing Data Governance in Data Mesh.

What is Data Mesh

Data Mesh is a relatively new way to manage analytical data in large, complex environments within or between organizations. This method is a big change in how organizations find, manage, and get access to data for large-scale analytical use cases. Analytical data is important for use cases that are predictive or diagnostic, and it is the basis for visualizations and reports that give business insights. Because of this, it is becoming a more critical part of the technology landscape.

One of the main differences between Data Mesh and earlier ways of managing analytical data is that Data Mesh makes technical and organizational changes in many different ways. Figure 1-1 gives a good overview of these changes, which are:

1. Data Mesh moves ownership and accountability back to the business domains where the data is made or used. In the past, specialists who ran the data platform technologies were in charge of all the data, which was centralized.
2. Distributed mesh of data products: Instead of collecting data in huge warehouses and lakes, Data Mesh connects data through a distributed mesh of data products that can be accessed using standard protocols. This architecture makes it easier for data to move between systems and makes it easier to manage and get to large amounts of data.
3. Lively, self-sufficient units: Data Mesh treats the data and the code that keeps it running as one living, self-sufficient unit. This is a change from older tech solutions that thought of data as something that happened when pipeline code ran.
4. Federated model for data governance: Data governance moves from a top-down, centralized operational model with human interventions to a federated model with computational policies built into the nodes on the mesh. This model ensures policies are applied the same way to all data products.
5. Data as a product: With Data Mesh, our value system changes from seeing data as an asset to be collected to seeing data as a product to serve and please the people who use it (internal and external to the organization). This method puts the needs of data users first, which can help the business get better results.
6. Well-integrated infrastructure: Data Mesh moves from two sets of fragmented and point-to-point integrated infrastructure services to a well-integrated set of infrastructure for both operational and data systems. This infrastructure makes managing and accessing large amounts of data easier and helps data move between systems.

Overall, Data Mesh is a big change from how analytical data was managed in the past. This approach can help organizations better manage, use, and own their analytical data by making a number of technical and organizational changes. This can lead to better business results in the long run.

Embedding Policies as Code in Data Products

One of the key aspects of Data Mesh is the implementation of policies that govern data products as code, embedded within each data product. This approach has several benefits, such as enabling validation and enforcement throughout the data product's life cycle. Policies can be implemented and validated at different points in the data product's life cycle, ensuring that they are always adhered to.

For example, encryption policies can be validated at the build and deploy time, ensuring that data products have access to a secure enclave. During the access and transformation of the data, the secure enclave can be used, enforcing the policy right in the data flow.

Access control and identity policies are other areas where embedding policies as code can be beneficial. In a distributed architecture like Data Mesh, there must be universal agreement on defining and verifying identity and access control rules. Standardizing these policies removes unnecessary complexity, making sharing data across multiple data products easier.

Privacy and consent policies are also an essential part of Data Governance. Recent privacy laws aim to protect individuals' personally identifiable information, and these laws have led to some level of standardization in operating models and processes involved in managing data. However, due to the lack of standardization and incentives in data sharing, we find very limited effort behind the standardization of privacy and consent. Embedding these policies as code in data products can help ensure that they are consistently adhered to across all data products.

Design Characteristics of Successful Data Mesh Governance

To ensure successful Data Governance in Data Mesh, it is essential to follow specific design characteristics. One such characteristic is standardizing policies to remove unnecessary complexity. In Data Mesh, policies are an element of every data product and part of its interface. Hence, standardizing what they are and how they are expressed and enforced will remove unnecessary complexity.

Rather than leaving this to individual teams or projects to decide on, this is where a Data Governance comittee can be setup to manage those decisions.

Standardizing identity and access control rules is also crucial for successful Data Governance in Data Mesh. A standardized way to identify data users and manage their access is necessary to enable data sharing across multiple data products. Standardizing these policies removes complexity, making it easier to share data across different data products.

Managing privacy and consent consistently across all data products is another essential aspect of Data Governance in Data Mesh. Embedding these policies as code in data products can help ensure that they are consistently adhered to across all data products.

Integration of Data, Code, and Policy in Data Mesh

In Data Mesh, data products are managed as independent, self-contained units, with each product responsible for its own data quality, governance, and accessibility. This approach links data, code, and policy as one maintainable unit, liberating us from many governance issues.

For example, embedding privacy and consent policies as code in data products ensures that they are linked with the data they are trying to govern. This approach ensures that the policies are consistently enforced across all data products and overcomes the challenge of tracking or respecting user consent once data is shared beyond a particular technical system.

Linking policies across different data products is another aspect of integrating data, code, and policy in Data Mesh. When data leaves a particular data product to be processed by others, it maintains its link to the original policy governing it. Policy linking is helpful to multiple data products to retain access to the latest state of the policy, as maintained by the source data product.

Key considerations for applying data governance to Data Mesh

Here are some critical considerations for applying data governance to data mesh, as outlined by these organizations:

1. Establish clear data governance policies: To ensure that data is managed consistently and effectively, it is important to establish clear policies embedded in each data product. These policies should be validated and enforced at various points in a data product's life cycle.
2. Standardize data policies and practices: To facilitate data flow across different systems, it is important to standardize data policies and practices. This includes standardizing how data is expressed, configured, and enforced and how data is identified, accessed, and managed.
3. Implement data catalog and literacy: A data catalog is a critical tool for effectively managing data in a data mesh environment. By implementing a data catalog, organizations can improve data discovery and accessibility, and data literacy across different domains.
4. Enable self-service data access: To facilitate data flow between different systems and business domains, it is important to enable self-service data access. This can help to reduce the burden on IT and enable business users to access the data they need to drive better outcomes for the organization.
5. Use automation and machine learning: To improve data quality and consistency, it is essential to use automation and machine learning. This can help to enforce data policies and ensure that data is managed effectively across different systems and business domains.
6. Foster a data-driven culture: To ensure that data governance is effective, fostering a data-driven culture within the organization is important. This involves promoting data literacy, encouraging collaboration between different business domains, and ensuring that data is used effectively to drive better outcomes for the business.

Overall, data governance is a critical component of a successful data mesh implementation. Organizations can manage, use, and own analytical data more effectively, ultimately driving better business outcomes.

Challenges in Implementing Data Governance in Data Mesh

While Data Mesh offers many benefits, there are also some challenges in implementing Data Governance in this new approach. One of the main challenges is the lack of standardization and incentives in data sharing. Data management systems have not yet agreed on standardized identity and access control policies. Many storage and data management technologies have their own proprietary way of identifying consumers' accounts and defining and enforcing their access control. This lack of standardization makes sharing data across different vendors and technologies challenging.

Another challenge is the difficulty in tracking or respecting user consent once data is shared beyond a particular technical system. Separating consent policy from data makes tracking or respecting the user's consent difficult. Data Mesh links policy and its configuration with the data it is trying to govern, but further development is necessary in policy linking.

Conclusion

Data Governance is a critical issue for organizations, and Data Mesh is a modern approach to managing data that offers many benefits. Embedding policies as code in data products, standardizing policies, managing privacy and consent, and integrating data, code, and policy are all essential aspects of successful Data Governance in Data Mesh.

While there are challenges in implementing Data Governance in Data Mesh, such as the lack of standardization and incentives in data sharing, the benefits of this new approach make it worthwhile to address these challenges. With the right design characteristics, policies can be consistently adhered to across all data products, ensuring data accuracy, consistency, and availability.

In conclusion, Data Governance in the age of Data Mesh offers exciting opportunities, but it requires careful planning and execution to realize its potential fully. Organizations can benefit from a more efficient, secure, and effective approach to managing their data by addressing the challenges and taking advantage of the opportunities.