Data Mesh is a relatively new architectural approach to data management that is gaining popularity among organizations that want to break down silos and create a more agile and scalable approach to data.

At the core of Data Mesh are five key principles: Domain-oriented decentralized data ownership and architecture, Self-serve data infrastructure as a product, Federated governance, Data as a first-class citizen and Continous Feedback.

The 5 Principals of Data Mesh

The first principle, Domain-oriented decentralized data ownership and architecture, is about assigning ownership of data to the domain teams that create it. These teams are responsible for defining the data’s structure, quality, and use. By giving domain teams control over their data, organizations can break down silos and create a more agile approach to data management.

The second principle, Self-serve data infrastructure as a product, means treating data infrastructure as a product, just like software. This involves creating a platform that allows domain teams to easily access and use data infrastructure, including tools for data discovery, access, quality control, and processing. By empowering domain teams to manage their own data infrastructure, organizations can reduce bottlenecks and speed up the process of delivering insights from data.

The third principle, Federated governance, is about creating a governance framework that is distributed across the organization. This means establishing policies, standards, and best practices that are shared across the organization, but are also flexible enough to allow for local variations. By creating a federated governance model, organizations can ensure that data is managed consistently across the organization, while still allowing for local autonomy.

The fourth principle, Data as a first-class citizen, is about treating data as a strategic asset that is critical to the organization’s success. This involves creating a culture that values data-driven decision-making, as well as investing in the tools, processes, and people needed to manage data effectively. By prioritizing data as a first-class citizen, organizations can create a culture of data-driven decision-making that can drive innovation and growth.

The fifth principal, is promoting continuous improvement through feedback loops. This principle involves establishing feedback loops to continuously improve the data mesh architecture, data products, and data infrastructure. It involves gathering feedback from data consumers and producers to identify data friction*, areas for improvement, and using this feedback to refine the domain-oriented data ownership, data product management, and self-serve infrastructure. This principle enables the data mesh to evolve and adapt to changing business needs and technological advancements.

Note*: From the perspective of Data Mesh, data friction is an important concept because it helps identify the areas where data silos and bottlenecks exist in an organization. By understanding the sources of data friction, an organization can take steps to reduce or eliminate them and create a more efficient and effective data ecosystem.

By following these five key principles, organizations can create a more agile and scalable approach to data management that is better suited to the demands of today’s digital landscape. Data Mesh is not a one-size-fits-all solution, but it provides a framework that can be adapted to fit the unique needs of any organization. As such, it is becoming an increasingly popular approach to data management among organizations that are looking to break down silos and create a more data-driven culture.

Data Mesh and Security

While Data Mesh is an effective way to improve data management and access, it’s important to consider the potential security implications of this approach.

One of the main concerns when it comes to Data Mesh and data security is data access. With self-serve data access, it’s important to ensure that only authorized personnel can access sensitive data. Access control policies must be implemented to govern access to data across domains. It’s important to have a clear understanding of who is authorized to access what data and under what circumstances.

Another security concern when it comes to Data Mesh is data integrity. With a decentralized data ownership model, data quality and consistency can be a challenge. It’s important to have a robust data quality program in place to ensure that data is accurate, complete, and consistent across domains. Data Mesh also relies heavily on automation, which increases the risk of data errors and inconsistencies. It’s important to have proper checks and balances in place to ensure that data is not compromised.

Finally, federated governance is another area of concern when it comes to Data Mesh and data security. Federated governance allows individual domain teams to govern their own data, which can lead to inconsistencies in data management and security practices. It’s important to have a unified governance framework in place that ensures consistent data management and security practices across domains.

Implementing a Data Mesh

Implementing Data Mesh can be challenging, but it offers many benefits, such as faster time-to-market, improved data quality, better data governance, and increased innovation. It requires a cultural shift towards decentralization and collaboration, as well as a significant investment in technology and infrastructure.

To get started with Data Mesh, organizations should identify their data domains, assign data ownership, and define their data products. They should also create a self-serve data platform that allows data teams to work independently while ensuring compliance with organizational policies and regulations. Finally, they should implement federated governance to ensure that each domain has control over its data while still maintaining compliance with organizational policies and regulations.

Good luck 🙂

Introduction

Data is an essential resource for any organization. Without accurate and timely data, organizations cannot make informed decisions or optimize their processes. To ensure that the data within an organization remains relevant and useful, a Data Steering Group may be established to provide guidance and direction. In this post, we will discuss what a Data Steering Group is and why an organization may want to have one.

Section 1: What is a Data Steering Group?

Definition: What is a Steering Group

A Steering Group, also known as a Steering Committee, is a group of individuals responsible for providing strategic direction, oversight, and decision-making for a specific project, initiative, or organization. The group typically includes representatives from various stakeholder groups and serves as a central point of communication and coordination to ensure the success of the project or initiative.

A “Data Steering Group” is a team of individuals within an organization responsible for setting and governing the data strategy, ensuring that data is used as effectively as possible. The group typically consists of representatives from across the business and will include roles such as Chief Information Officer (CIOs), Chief Technology Officers (CTOs), line-of-business heads, or departmental heads. In addition to technology experts, members of the Data Steering Group should also have broad business knowledge and experience. This ensures that decisions on data usage are taken with the wider context in mind.

The primary role of a Data Steering Group is to provide guidance on data initiatives, taking into consideration both short-term needs and long-term goals. It is responsible for setting data strategies and policies, including developing standards that ensure the quality of data. It also works to ensure compliance with relevant data security and information privacy regulations, such as GDPR and CCPA. In addition to this, the Data Steering Group is tasked with identifying opportunities for data-driven innovation, developing plans to implement them, and ultimately determining which initiatives should be pursued and which should be abandoned.

The Data Steering Group is typically chaired by a senior executive in the organization (such as a CIO or CTO), who will set the agenda for each meeting. Members of the group bring different skillsets and expertise to bear on decisions about how best to use data within an organization. Working together, they can create effective data strategies that benefit the organization as a whole.

In summary, a Data Steering Group is an important part of any organization and can be invaluable in helping to set data strategies that are both effective and compliant. By bringing together individuals with different skillsets from across the business, it can provide valuable guidance on how best to use data for the benefit of the organization.

Section 2: Why have a Data Steering Group?

A Data Steering Group (DSG) is an important organizational tool for ensuring data quality and security. It provides a forum for stakeholders from across the enterprise to come together and make informed decisions about data management issues. The DSG is responsible for setting the data governance strategy and ensuring it is aligned with the organization’s overall business objectives.

The benefits of having a Data Steering Group are numerous. By providing a forum for stakeholders to collaborate, the DSG can help ensure that all data initiatives, or business cases, are compliant with applicable regulations while also improving data quality. This improves trust in data-driven decision making and helps teams produce more accurate results. Additionally, the presence of an oversight body like a Data Steering Group leads to greater accountability for mistakes and ensures that important issues are addressed quickly and effectively.

Organizations such as Google, IBM, GE, Microsoft and Intel have implemented successful Data Steering Groups with positive outcomes. These organizations have seen improved data quality, more effective processes for data governance and compliance, and better alignment of data initiatives with business objectives.

In summary, having a Data Steering Group can provide significant benefits in terms of data quality, compliance, and alignment with business objectives. Organizations that have implemented DSGs have seen successful outcomes and results from their efforts. With the right stakeholders and commitment to collaboration, a Data Steering Group could be highly beneficial for any organization looking to optimize the management of its data resources.

Therefore, creating a Data Steering Group is an important step in ensuring proper data management in any organization or company. The DSG provides an oversight body that ensures data initiatives are compliant with applicable regulations and that data quality is never compromised. With the right stakeholders in place, a Data Steering Group can be an invaluable tool for improving data governance and achieving better alignment with business objectives.

Section 3: How to establish a Data Steering Group

The first step to establishing a Data Steering Group is to identify the stakeholders from within the organization who should be part of it. The group should include leadership from IT, operations, finance, marketing, and any other departments that are heavily reliant on data. Additionally, stakeholders outside the organization such as customers or vendors may need to be involved depending on the scope of the data initiatives.

Once all necessary stakeholders have been identified, a charter can be created which outlines the governance structure and objectives of the Data Steering Group. This document should clearly establish roles and responsibilities for each member as well as objectives for guiding data projects through their lifecycle. It should also include metrics that will measure progress towards these objectives in order to ensure accountability across the board.

Finally, the Data Steering Group needs to be effective and remain relevant over time. This can be done by regularly reviewing the charter and metrics to ensure they are still aligned with the organization’s objectives, addressing any changes that may need to be made. Additionally, ensuring open communication among all stakeholders is key for a successful Data Steering Group. Regular meetings should be held in order for members to share updates on their respective initiatives as well as discuss any potential obstacles or opportunities that have arisen. By doing this, the Data Steering Group will continue to make meaningful contributions in guiding data projects through their lifecycle and helping shape the future of an organization’s data-driven decisions.

Conclusion

Having a Data Steering Group is an effective way to ensure data quality and governance are managed in an organization. The group provides the oversight needed to manage data, identify issues, and make decisions that will improve data management practices. With the right resources in place, such as a Data Steering Group, organizations can have confidence that their data is well-managed and secure. Furthermore, having a Data Steering Group can lead to improved decision-making and greater efficiency within an organization. Organizations should consider establishing a Data Steering Group in order to reap the many benefits it has to offer.

Data Cloning, sometimes called Database Virtualization, is a method of snapshotting real data and creating tiny “fully functional” copies for the purpose of rapid provisioning into your Development & Test Environments.

The Cloning Workflow

There are four primary Steps

1. Load / Ingest the Source Data
2. Snapshot the Data
3. Clone / Replicate the Data
4. Provision of the Data to DevTest Environments

Under the Hood

Cloning is typically achieved/built using ZFS or HyperV technologies and allows you to move away from the traditional backup & restore methods, which can take hours.

By using ZFS or HyperV you can provision databases x100 quicker and x10 smaller.

What is ZFS?

• ZFS is a file system that provides for data integrity and Snapshotting. It is available for most if not all major OS platforms.

What is HyperV?

• HyperV is a Microsoft virtualization platform that can be used to create and manage virtual machines. It supports Snapshotting as well.

Problem Statement

Backups are often taken manually and can take hours or days to complete. This means that the data isn’t available for use during this time period, which can be problematic if you need access to your data immediately.

There is also a secondary issue with storage. A backup & restore is, by its nature, a 100% copy of the original source. So if you started with a 5 TB database and wanted x3 restores then you are up for another 15 TB in disk space.

What are the Benefits of Data Cloning?

Data cloning is the process of creating a copy, or snapshot, of data for backup, analysis, or engineering purposes. This can be done in real-time or as part of a scheduled routine. Data clones can be used to provision new databases and test changes to production systems without affecting the live dataset.

Advantages

– Clones can be used for development and testing without affecting production data

– Clones use little storage, on average about 40 MB, even if the source was 1 TB

– The Snapshot & Cloning process takes seconds, not hours

– You can restore a Clone to any point in time by bookmarking

– Simplifies your End to End Data Management

Disadvantages

– The underlying technology to achieve cloning can be complex.

However, there are various cool tools on the market that remove this complexity.

What Tools are available to support Data Cloning?

In addition to building your own from scratch, commercial cloning solutions include:

• Delphix
• RedGate SQL Clone
• VirtualizeMe (vME)
• Windocks

Each is powerful and has its own set of features and benefits. The key is to understand your data environment and what you’re trying to achieve before making that final decision.

Common Use Cases for Data Cloning

• DevOps: Data cloning is the process of creating an exact copy of a dataset. This can be useful for several reasons, such as creating backups or replicating test data, into Test Environments, for development and testing purposes.
• Cloud Migration: Data cloning provides a secure and efficient way to move TB-size datasets from on-premises to the cloud. This technology can create space-efficient data environments needed for testing and cutover rehearsal.
• Platform Upgrades: A large majority of projects end up going over the set schedule and budget. The primary reason for this is because setting up and refreshing project environments is slow and complicated. Database virtualization can cut down on complexity, lower the total cost of ownership, and accelerate projects by delivering virtual data copies to platform teams more efficiently than legacy processes allow.
• Analytics: Data clones can provide a space for designing queries and reports, as well as on-demand access to data across sources for BI projects that require data integration. This makes it easier to work with large amounts of data without damaging the original dataset.
• Production Support: Data cloning can help teams identify and resolve production issues by providing complete virtual data environments. This allows for root cause analysis and validation of changes to ensure that they do not cause further problems.

To Conclude

Data cloning is the process of creating an exact copy of a dataset (database). This can be useful for many reasons, such as creating backups or replicating data for development and testing purposes. Data clones can be used to quickly provision new databases and test changes to production systems without affecting the live dataset.

This article provides a brief overview of data cloning, including its advantages, disadvantages, common use cases, and available tools. It is intended as a starting point for those who are new to the topic. Further research is recommended to identify the best solution for your specific needs. Thanks for reading!