Data Mesh

For Suadeo Data Mesh is not just a concept, it is our very architecture!

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Before it became a trend, we were already practicing Data Mesh, having deployed it for our customers for several years!

Suadeo’s platform is the only solution on the market that fully implements the Data Mesh architecture.

Our Research and Development has produced the Suadeo Self Data Services® platform in its design and operation in an inherently Data Mesh architecture.

Data Mesh, Kézako?

Data Mesh is an innovative approach to data management that revolutionizes the way companies structure and exploit their information assets.

Unlike traditional centralized architectures, Data Mesh proposes a decentralization of data management responsibilities, aligned with the operational areas of the company.

By adopting the principles of decentralized governance, domain ownership and Self-BI, Data Mesh allows local teams to manage, control and consume data autonomously.

This approach promotes greater agility, increased scalability and optimized responsiveness to evolving business needs.

For companies seeking competitiveness and innovation, Data Mesh represents a real cultural and technological transformation, paving the way for a more efficient and democratized exploitation of data.

Suadeo’s platform is natively Data Mesh.

Introduced in 2019 by Zhamak Dehghani when she was Chief Technology Officer at ThoughtWorks, the Data Mesh concept has been the foundation of the Suadeo platform for 20 years.
An essential prerequisite for true BI-self.

What defines a Data Mesh architecture within the market context?

The visual representation below illustrates the concept of Data Mesh, emphasizing the specific accountable stakeholders and thematic domains that demand focus, as detailed on the website www.datamesh-architecture.com.

Data Mesh Architecture

Every Data Mesh architecture need can be met through a service provided by the Suadeo Self Data Services platform

The Suadeo Self Data Services® platform stands out as the sole solution in the market that fully embodies the Data Mesh architecture.
Data Mesh Self Data Platform Suadeo
Suadei Self Data Services® (SDS) Platform

The Suadeo Data Mesh concept is more than just a theoretical idea; it’s a concrete representation of our architecture

Since 2004, our guiding principle has been to simplify the extraction of value from data and its practical application by end users.

The services within our platform have been developed in close collaboration with these end users, providing them with autonomy and agility by reducing their dependence on IT interactions.

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Addressing the Data Mesh challenge within your organization may appear complex

However, our integrated services provide the means for an immediate deployment of Data Mesh

Data Mesh requêtes SQL

Virtualized Querying:

Users can directly execute SQL queries on various data sources, including Excel, databases, and other non-relational systems, and navigate through the data, replicating the familiar interaction they have with tools like DBeaver.

Data Sources:

Data sources required to enable business autonomy are virtually consolidated within the platform, and they can be accessed via SQL regardless of their original technology. This configuration allows organizations to rapidly access their sources, even within a complex and distributed architecture.

Model:

Technical teams develop business models to improve data comprehension for end users. This abstraction layer enhances end-user autonomy and simplifies maintenance for technical teams.

Usage Data:

These are virtualized datasets curated by business units to address specific use cases. Secure and adaptable, these usage datasets can be contextualized based on user profiles and permissions.

Supervisor:

Technical teams can easily monitor the data usage carried out by business units. The actions of individual users are traceable and auditable as needed .

Catalog:

Users can organize their datasets within private or public catalogs and define various authorizations.

Data Catalogs:

Technical teams work alongside users to improve the catalog of their data sources by adding business-oriented descriptions and easily understandable aliases.

Business Glossary:

A business glossary serves as a business-focused dictionary that aims to align internal terminology and ensure consistent understanding among diverse stakeholders. It empowers business units to search for specific business terms and understand the underlying data intended for use.

Lineage:

Technical teams can easily access a graphical representation of how users utilize fields and tables within the platform with a single click. This simplifies maintenance, as business units can quickly identify the impact of any structural changes to a data source.

Dashboards and Reports:

As part of data enrichment efforts, an organization can present and share its data product in dynamic graphical formats to improve understanding among all platform stakeholders.

Customized Application:

Going beyond dashboards and reports, it’s essential to enable the creation of business applications directly from the platform. These applications should incorporate features like validations and data input, similar to a budget validation solution, for instance.

API:

Users can effortlessly share their data products with a single click using APIs. These APIs are accessible via URL or external code integration, allowing business units to centralize expertise within a unified platform. This streamlines sharing with other applications, ensuring data security and consistency throughout the entire lifecycle.

Scheduler:

To simplify auditing requirements and data traceability, the scheduler enables business units to extract files at regular intervals and maintain a historical record.
Data Mesh business searches
Data Mesh quality and transparency

Data Quality Rules:

Entities define data quality standards to monitor the evolution of data quality throughout its lifecycle. These quality insights are made available and shared with other business units to promote greater transparency.

Management Rules:

Business units establish management rules specific to their key performance indicators (KPIs). This automation allows for scheduled KPI calculations, facilitating ongoing tracking. Consequently, KPIs are documented and governed more effectively.

Directory / Groups:

Data is securely shared with user groups across various elements within the platform. Each entity can specify which individuals have access to these elements and control the data that each of them can access.

User Roles:

Every platform user is assigned a specific role. Administrators can configure the services accessible to each role for creation, editing, or viewing purposes. Roles streamline the deployment of a decentralized architecture in harmony with the Data Mesh concept.

Domains:

Business units can add business domains to various objects to enhance comprehension and grouping by theme. An entity can, therefore, manage multiple business domains and define validation workflows before publication.

Tags:

Fields within the sources can be tagged to identify sensitive data, obsolete fields, or other elements essential for maintenance or tracking throughout the lifecycle.

Supervision:

Every action performed by users is logged. Administrators can track all operations that have occurred on objects, data extractions, or data consultations.

Documentation:

Effective data governance and transparency rely on the ability of business units to comprehend the meaning of data. Within Suadeo, every object can be documented either manually or through the import of Word files.

Data Anonymization:

Business functions, in coordination with governance bodies, can either restrict access to sensitive data or anonymize such data for specific users or user groups.

Audit:

Because all object changes are tracked, users with the right permissions can revert to a previous version of their object, thereby ensuring business continuity in the event of errors.

Profiling:

Each data source table can be profiled to enable business units to obtain data statistics and identify the most prominent elements.
Data Mesh data monitoring

10 reasons to adopt a Data Mesh architecture

Adopting Data Mesh is transforming data management and corporate culture. By empowering teams and enriching collective knowledge, it improves responsiveness, security and innovation. Discover how this approach at the heart of BI unlocks the potential of data for sustainable value.

Download the document DATA MESH
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Embrace a data-centric architecture to maximize the value and optimization of your data

Don’t miss the cultural shift in how companies perceive their data

Autonomy

Empower business units to govern their own data domains, giving them the control, they need

Shared Knowledge

Your business functions grow together as they integrate and exchange data

Flexibility

Implementing your projects becomes more agile and resource management more flexible

Responsiveness

Each business unit addresses its use cases more swiftly, without relying on IT, and in an iterative manner

Quality

Each business unit takes responsibility for the quality of its own data, leading to a positive impact across all teams

Centralized

Your governance remains centralized, ensuring interoperability and security
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Communication

Enhanced data management communication through shared vocabulary and language

Scalability

Enable more straightforward evolution of your data architectures by dividing responsibilities among multiple teams

Architecture data Mesh

All our services to ensure data governance

The French Data Mesh platform S.D.S. serving business autonomy

Connector

Integration

Data Models

Usage Data

Data Catalog

Query

Dashboard

Report

Low code app

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Chatbot

Suadeo, 19 years of research and development in data processing

Suadeo: 20 years of RD

We were pioneers in Data Mesh before it became a trend!

With Suadeo, the concept of constructing your own platform and restoring autonomy to business units is not merely an option; it has become the reality for all those who place their trust in us.

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