Data Mesh

Nowadays, companies strive to move to more and more digital solutions, thus transforming into data-driven businesses at every chance they have. This gives them an edge, especially in a time of such considerable and continuous evolutions of technology.

Unfortunately, businesses sometimes overlook the data architecture structure and don’t bother scaling it as they probably should. Companies that take their digital transformation journey seriously adopt technology known as data mesh. However, most companies end up asking themselves “What is data mesh?” initially.

This modern data management strategy helps businesses improve their organization and productivity with discoverable, accessible, secure, and interoperable data.

Data Mesh 1

What Is Data Mesh?

Data mesh is a type of data platform architecture that allows users to directly access data without having to transport it to data lakes or data warehouses. It also doesn’t require the intervention of expert data teams.

This decentralized data management strategy directly connects data owners, data producers, and data consumers. It organizes data by specific business domains like marketing, sales, and customer service, for example. This means that each domain-specific group owns and manages its data as a product.

This method reduces bottlenecks and data silos, improves decision-making, and sometimes even helps detect fraud or alert the business to any changes in the supply chain conditions. It helps users think about data as a product that has a purpose inside the business.

Data mesh relies on cloud-native or cloud-platform technologies to scale and achieve data management goals. The main goal of this tech is to help a company obtain valuable and secure data products.

Data Mesh Architecture

The data mesh architecture comprises several components. To successfully implement and understand the technology, companies and their technology partners must fully understand how these technologies work and relate with one another.

  • Data Product – This is a published data set accessible to other domains, like an API, for instance. It can take the form of sales reports with KPIs, PDF files, or even machine learning models. Ownership of these products is usually described in the metadata.
  • Data Ingestion – This is the step in which tools insert raw data into the data platform. It requires specific tools that work according to the domain-driven design principles. Data is either ingested in batches or in a stream, in real time.
  • Clean Data – The raw data requires processing and “cleaning” before any analysis or usage. Domain teams are responsible for data cleaning and identifying how their domain data requires specific processing.
  • Analytical Data – This processed type of data is what allows domains to gain business insights. Members can transform this data into visual presentations or apply data science and machine learning methods to better understand the data and identify trends and anomalies.
  • Federated Governance – This body consists of representatives from all domains that must agree on global policies and other rules regarding the creation and operation of data products. Common discussions include interoperability, privacy, compliance policies, documentation, and accessibility processes.
  • Data Platform – This infrastructure is accessible to every existing domain within the organization. It possesses every tool necessary to ingest, store, query, and visualize data. More advanced versions of data platforms directly allow users to create, monitor, discover, and access complete data products.
  • Enabling Team – The enabling team is the very first piece of the data mesh architecture. Their responsibility is to disseminate the idea of data mesh within the company. They help domain teams become true experts in data mesh by serving as consultants.

    Benefits of Using Data Mesh Architecture in Your Company

    Using data mesh architecture in a company comes with a wide variety of benefits. The first benefit of data mesh is increased organizational agility. Decentralized data operations are the bases of this mode, as teams operate independently, reducing deployment time and operational bottlenecks.

    Data is more discoverable and accessible to multiple domains. This means that there’s more clarity into the value that all data products provide. Each domain has greater autonomy and flexibility and is able to freely experiment and innovate without burdening data teams.

    Using a self-serve data platform comes with automated data standardization, product lineage, monitoring, alerting, and many other benefits. This provides a competitive edge in comparison with traditional data architecture.

    Data mesh is also extremely cost efficient. It moves away from batch data processing and enables companies to adopt cloud data platforms and real-time data collection. Using cloud storage allows the data teams to work with large clusters of data while only paying for the specific amount of storage they need.

    When teams require additional space for a limited period of time, they can easily purchase additional compute nodes and then cancel the extra storage usage whenever they need to.

    Adhering to federated computational governance enhances data interoperability. Domains agree on how to standardize any data-related procedure, which makes it easier for them to access each other’s data products. This also allows for better quality control.

    Data Mesh vs. Data Fabric

    Data fabric is a data architecture model that focuses on collecting different technologies used to collect and distribute data efficiently. It uses the automation of data integration, engineering, and governance to create an interface between data providers and consumers.

    While data mesh is data-centric and decentralized, data fabric is tech-centric and centralized. It focuses on combining the right technologies and bringing data to a unified location.

    Data fabric and data mesh aren’t mutually exclusive and can actually be complementary to each other. A few strategic parts of the data mesh sometimes improve with data fabric through automation. This would result in faster data product creation, global governance enforcement, and easy data product combination.

    Data Mesh vs. Data Lake

    A data lake works as a central repository that houses data. This low-cost storage environment takes data in a simple manner and depends on a central team to manage it. The type of data usually found in data lakes is the kind that immediately results from ingestion. Essentially, data lakes serve as containers for raw data without a defined purpose.

    While this tech-based approach might be of value for some businesses, a few issues often arise from it. Once teams move data to a data lake, it automatically loses context. Users have access to many files but won’t necessarily know which ones they should use.

    Because the data in the data lake is raw, data consumers often need help from the data lake team to understand the meaning of the data and solve issues. This causes significant IT bottlenecks. 

    How to Migrate to a Data Mesh Architecture

    Migrating to a data mesh architecture requires many organizational changes and adjustments. Companies need to prepare for this shift at various levels including working with the teams, changing data-related processes, and upgrading their technology. Luckily, companies have the ability to migrate to a data mesh architecture in four steps for improved datafication:

    1. Treat data as a product This requires standardizing a data set and dashboard documentation, while still guaranteeing interoperability. Domains must catalog their data in credible and trustworthy ways to ensure data discoverability, security, and integrity.
    2. Map domain ownership distribution – The second step is to address data product distribution. Using domain-driven design tools, companies can easily group data sets into different domains. Each domain has its data sets split into different categories (orders, traffic, etc.).
    3. Build a self-serve data infrastructure To access and manage the newly available data products, teams need a self-serve data infrastructure. All domains must agree on the technology used to build this platform so that building and handling datasets is equal in every sector.
    4. Ensure federated governance – At this stage, representatives from each domain work on agreements and shared nomenclature. They must agree on implemented policies, documentation rules, procedures for fixing issues, and more.

    As previously mentioned, adopting a data mesh architecture requires a company to change at different levels. It’s important for business leaders to work closely with their team members to help them adjust to their new roles. Moving from a centralized data ownership model to decentralized domains requires a shift in the employees’ focus.

    Work With BairesDev on Your Data Mesh Project

    Companies that want to embrace data mesh architecture but are unsure about where to begin and don’t have the time to dedicate fully to this change can always try outsourcing their data mesh projects to reliable providers.

    Outsourcing providers can easily understand the needs of the company and assign different experts to assist throughout the different stages of the data mesh project. Outsourced data mesh experts can help a company set up data mesh by working as consultants. 

    For instance, an outsourced data mesh expert can help a company determine the changes it needs to make before adopting the data mesh architecture. They could assist in preparing the domain teams for their new roles. Outsourced data mesh specialists could also help determine the best technology to build the self-serve data infrastructure and how to implement federated computational governance policies.

    Work With BairesDev on Your Data Mesh Project

    Working with outsourcing providers can be extremely beneficial for companies. They offer access to top industry specialists located in areas where average salary expectations are lower. This means that businesses have access to talent that’s skilled, experienced, and knowledgeable but also affordable.

    Outsourcing providers such as BairesDev also allow businesses to save time in learning about the requirements for a data mesh shift, preparing for them, and even recruiting external help. As outsourced experts work on implementing data mesh, business leaders and teams can focus on core operations.

    As companies want to accelerate their digital transformation and become more data-driven, they begin to implement changes in their data architecture. Most of them aim to evolve from centralized data warehouses and data lakes to decentralized, interoperable domains by adopting data mesh architecture.

    Data mesh is a type of data platform architecture that allows different domains to easily access the same data sets without having to transport them to data warehouses and without depending on expert data teams.

    The technology serving as the foundation for data mesh are four core principles that include domain-driven data ownership, data as a product, self-serve data platform, and federated computational governance. 

    Basically, these principles mean that when using data mesh architecture, the different domains have ownership over the data they produce. This data is meaningful and can serve a wide variety of purposes, like a product. 

    To access and manage this data, companies have to build a self-serve data platform that’s accessible to every domain member. This useability relies on a federated governance model where domain representatives agree on policies that’ll determine how everyone handles data within the company.

    Using data mesh comes with many benefits including increased organizational agility, data discoverability and accessibility, greater domain autonomy, and cost efficiency. It also guarantees increased interoperability and better quality control.

    Adhering to a data mesh architecture might not be necessary for every company, especially if they’re not dealing with large amounts of data. However, bigger companies that rely on quality data to compete and are looking to scale quickly should consider shifting to data mesh.

    If this paradigm shift seems too overwhelming, business leaders should think about working with an outsourcing provider. These can provide talented data mesh consultants that can guide the company throughout the entire process.

    Outsourcing data mesh specialists can help companies save time and money while still having access to the top experts in the field. As leaders don’t need to focus on their data mesh projects, they’re able to focus their attention on core business operations.

    The Core Principles of Data Mesh

    There are four core principles behind the concept of data mesh. These include domain-driven data ownership, data as a product, self-serve data platforms, and federated computational governance. 

    • Domain-Driven Data Ownership
      A domain is a group of people organized in a common functional business department. The domain-driven data ownership principle dictates that these domain teams take responsibility for their data.

      They’re responsible for incorporating, transforming, managing, and providing data to end users. This means that analytical and operational data ownership is now decentralized and that each domain owns the entire life cycle of its data products.
    • Data as a Product
      The data as a product principle changes the way people think about data. Teams create data products via the different domains for downstream consumers or users outside of the team. These consumers then use the data products to create business value.

      Data products serve different purposes inside a business. They can be responsible for security, provenance, and infrastructure concerns, for example. They also have the duty of ensuring that data is always kept up to date.

      Domain teams keep up with the needs of other domains by providing them with high-quality data in the form of data products.
    • Self-Serve Data Platform
      The idea behind a self-serve data platform is that it is easily accessible and intuitive, allowing every member of each domain to create and manage their data products. The main goal of a self-serve data infrastructure is to provide autonomy.

      These platforms have a dedicated data platform engineering team that manages and operates the wide range of technologies used. Domains need only worry about consuming and creating data products while the engineering team ensures the functionality of the platform at all times.
    • Federated Computational Governance
      Federated computation governance allows for the creation of a data ecosystem in which all data products are interoperable. Unlike traditional data governance, this method allows the production of value through data.

      Embedding governance concerns into the workflow of each domain leads to data standardization. The introduction of usage metrics and reporting is also imperative to help understand the individual value of data products.

    When Should a Company Adopt Data Mesh Technology?

    Adopting data mesh technology requires a major shift in the data management paradigm. During this process, teams must change their data management strategies, processes, and ultimately the way they work. But doing so might lead them toward innovation.

    Data mesh primarily benefits larger organizations or companies that want to scale quickly, working with large, diverse, and changing data sets. It’s also an attractive idea for organizations that compete based on the overall strength of their data.

    Embracing data mesh technology might also be a good idea for companies whose teams are already decentralized. If data teams are slowing down innovation efforts, they will also benefit from data mesh.

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    Deciding whether to adopt a data mesh process flow requires companies to look inward and reflect on the current state of the accessibility, manageability, actionability, and ability to consume their data.

    However, not every company will benefit from data mesh technology. Smaller businesses that don’t deal with larger volumes of data and that don’t rely on it to remain competitive might not necessarily benefit from data mesh architecture.

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