If you’re in the data industry, you’ve probably heard of data mesh. This emerging technology is becoming increasingly popular due to its use cases and applications. In the simplest terms, a data mesh is a data architecture that helps decentralize and democratize data in a company. A data mesh helps connect data with the users who produce it and understand it best. This allows organizations to unlock the power of their data and utilize it to its fullest potential while also empowering self-service analytics across teams.
In this blog post, we’ll be diving into everything you need to know about data mesh. We’ll explain how it works, how to build it, and how it differs from other data storage models. Read on to learn more.
What Is a Data Mesh?
A data mesh is a data architecture that organizes data by specific business practices. This decentralizes control of the data and gives more ownership to the users who produced the data. For instance, departments like marketing, customer service and sales can more easily access and utilize the data that matters most to their departments.
Centralized data architectures can make it difficult to access data and democratize it. Democratized data is important because it empowers a data-driven culture, with employees who can access and understand data without having to continually rely on data teams.
In other words, data mesh storage models can help teams remove the middle-man from the data accessibility equation. This allows for faster data-driven decisions, fewer bottlenecks and a more confident workforce.
How Does It Work?
Data mesh works by implementing a model with three components – data sources, data mesh infrastructure and data owners. Let’s break down each of these components and how they contribute to a data mesh model:
Data sources - The data source component is as advertised. A data mesh requires various data sources such as internal databases, feedback forms, data analytics, APIs and any other platform your teams use to collect and store data.
Data mesh infrastructure - The data mesh infrastructure allows data to flow seamlessly between departments. The enterprise network ensures data can always reach the users that need it. Also, since your data is decentralized, your governance protocols can be easily applied organization wide.
Data owners - Each team will be responsible for managing their own data sets. This eases the burden on IT teams while also making sure the people who understand the data best are in control of its management, governance, compliance and security.
By utilizing this three-step process, companies can ensure that the data they collect is secure, accurate, and always up-to-date. This makes it easier for them to access the data they need and use it in meaningful ways that benefit their organization.
What Are the Benefits?
If you’re thinking of building and implementing a data mesh, you may want a more comprehensive breakdown of the benefits. A data mesh can be an incredibly beneficial architecture if implemented properly. Let’s take a look at some of the top benefits of using a data mesh in your organization:
Data decentralization for self-service analytics - A data mesh makes your company data more decentralized, increasing access and availability. This way everyone in your organization can access the data they need and harness the power of self-service analytics to make the most of that data.
Optimize productivity - A data mesh can help optimize productivity. When data is easy to access and understand, less time is spent trying to parse data and collect the necessary insights to make decisions.
Encourages a data-centric culture - Data analytics can be intimidating, especially for users with limited technical experience. However, data democratization and decentralization can enable self-service solutions that make data easy to understand and access. This fosters a data-centric culture that values data assets and the potential of data insights.
Frees up IT time and resources - A data mesh can enable greater collaboration between departments while also reducing data requests to the IT teams. This frees up the IT teams to work on data governance, policy, strategy and other important goals. Other teams also won’t run into bottlenecks from IT being inundated with data requests.
More data-driven decisions - When the process to access and analyze data is less complex, more team members can make data-driven decisions. Not only will teams be able to work faster, but they’ll be able to work smarter as well.
Stronger governance - A data mesh enables strong governance. This makes it easier to stay compliant and keep data secure.
Reduce costs - Data-driven decisions can lead to reduced costs and higher ROI. Additionally, the increased productivity and the saved time will reduce cost as well.
Improve the customer experience - Finally, data mesh can improve customer experience by giving teams access to timely and accurate data.
These are just some of the primary benefits of a data mesh.
How To Build One
Building a data mesh requires following a specific architecture model that is based on four key principles. These principles include domain-driven data ownership, data-as-a-product, self-service data infrastructure, and federated computational governance. Let’s take a look at each principle:
Domain-driven Data Ownership - Domain-driven Data Ownership is a principle that says the owners of data have control over that data. That means they’re responsible for deciding how data is used, shared and secured – among other responsibilities.
Data-as-a-Product - Data-as-a-Product is a principle that says data has value and that data owners should capitalize on this value when making the data readable for the end user. In other words, they can monetize the data and make it a product for a business.
Self-Service Data Infrastructure - Self-Service Data Infrastructure is a principle that allows users to access data directly without needing to go through IT administrators or wait for external sources. Users can get the data they need when they need it.
Federated Computational Governance - Federated Computational Governance is a principle that says data governance should be integrated into the data management process, so the policies can be standardized across the organization. This ensures team members understand how data should be used and managed.
By following these four principles when building a data mesh, organizations can create an architecture that allows them to capitalize on their data, while also ensuring that it is secure and properly managed.
How Is a Data Mesh Different From a Data Lake vs. Data Fabric?
To understand the difference between a data mesh and a data lake, you can begin by asking the following questions:
What Does It Mean?
First, let’s define each of these terms:
Data Mesh Defined: We’ve talked a lot about what a data mesh is, but let’s recap. Essentially, a data mesh is a data architecture that can help businesses decentralize and democratize their data. Data meshes are flexible, scalable, and highly secure. They also empower team members in an organization to utilize and understand data to a greater degree. Companies favor data mesh architecture because it helps their team members take ownership of data and leverage its full potential.
Data Lake Defined: Data lakes are data repositories that can store large amounts of data in a raw format. Data lakes are typically unstructured or semi-structured and can be used for BI, machine learning, AI, analytics and other applications. Data lakes are useful for storing big data, but they can sometimes lack data integrity and security. It can also be difficult for non-technical users to utilize data stored in a data lake without IT assistance.
Data Fabric Defined: Data fabric is a data architecture that seeks to solve many of the same problems as data meshes. However, data fabric uses a centralized system to govern and manage data. It also determines governance from a top-down central authority, while data mesh focuses on each team determining governance for their data sets.
What Is The Goal?
Fundamentally, both data mesh and data fabric architectures seek to achieve the same goal. For comparison’s sake, we won’t be looking at data lakes as part of the equation. While data lakes are useful as a large data storage solution, data mesh and data fabric specifically seek to provide organizations with an architecture to solve common data obstacles. Let’s focus on comparing these two.
Both data mesh and data fabric architectures seek to help democratize data and make it more accessible to all members of an organization. They also seek to help organizations more easily manage and govern data. The difference is how they achieve these goals. As mentioned, data mesh is decentralized while data fabric is centralized. The way these two handle governance, data usage and data access differ.
Who Owns The Data?
Next, let’s take a look at who owns the data in data fabric and data mesh structures. Generally, the organization as a whole owns the data contained in these systems. What differs is who owns the governance, management and security of the data contained.
In data mesh, ownership is the purview of each department. Departments need to take charge of the data they produce and they determine how that data is managed and governed. This is how data is decentralized in a data mesh system. Data fabric, on the other hand, is centralized and data governance and ownership is handled by a top-down central authority. Partner organizations in the data fabric who access the data through network endpoints can also have ownership of the data they produce. All members of the fabric can have access to the data fabric, but they may not be in charge of determining data policy.
How To Scale?
Scaling a data mesh can be simpler than scaling other types of data architecture, since the data management is decentralized and governed by each domain. As your organization grows, you can scale your data mesh by increasing the number of domains as your data management needs increase.
Since data fabric uses a more traditional centralized structure, you may run into the same scaling problems you would run into with data lakes. Siloing and other data integrity issues may cause problems for you in the future. While a data fabric system may generally be easier to scale than data lakes, a data mesh will usually be the most scalable solution.
Ultimately, the best way to scale any of these technologies is to understand the company’s current needs and plan ahead for future growth. By doing so, businesses can ensure they are prepared for whatever comes their way.
Learn More With Secoda
In short, data mesh enables self-servicing empowering teams to utilize their respective datasets for innovation. If you’re looking for the best data management solution to take advantage of self-service analytics, Secoda is your solution. Secoda is an all-in-one data management workspace that makes it much easier for teams to access self-service analytics and drive data-centric decision making. Try Secoda for free today.