APPLY DATA MESH
In the digital economy, agility is a matter of survival. The faster an organization can react to events with AI-applied intelligence in business domains, the better it can compete. Transforming your data architecture into a Data Mesh is essential.
Data Mesh is a pattern for organizing data management within an organization that emphasizes decentralization, governance, and literacy, and seeks to democratize access to data as APIs and build a data-driven culture with defined domains. It requires focus on data integration, modeling, quality, security, culture, domain development driven, building robust data catalogs, data pipelines, and data integration architectures.
APPLY DDD,
BE DOMAIN-ORIENTED
Organizing and managing data within an organization with well-defined contexts and boundaries, with interoperability between domains, is very important for a Data Mesh architecture. Within our DDD work, domain teams will be able to better understand the data and the business, apply AI in a modern IT architecture, make the data available as a product, create a ubiquitous language, understand how to create single sources of truth, and model with context maps.
IMPLEMENT MODERN DATA GOVERNANCE
We set up clear, modern governance processes and policies to ensure that data is used ethically and responsibly within a work process. This includes defining roles and responsibilities, establishing data lineage, and ensuring its quality.
Data lineage aims to track the data flow process over time, providing a clear view of where the data originated, how it changed, and its final destination within the data pipeline.
PROVIDE A DATA CATALOG
We organize the data catalog for each domain, with detailed metadata about each data asset, including its definition, patterns, usage, and lineage.
A data catalog can provide a single source of truth for data assets, allowing users to make more informed and precise decisions based on a full and accurate understanding of the available data.
COMPOSE YOUR DATA
We set up the combination of data from various sources and systems in a consistent and coherent manner for your domains. This includes establishing common data definitions and standards, as well as building robust data pipelines and integration architectures.
DATA DEMOCRATIZATION
We democratize access to data within your organization, for all those who need the information. This requires the establishment of clear access controls and permission levels, as well as the creation of robust APIs and other mechanisms for self-service access.
This activity is important to define how internal or external users of a domain or even of the corporation can use the data. Data democratization makes it possible to have data as a product, without losing control of its owners.
DATA LITERACY
We develop data literacy within your organization, this includes training and mentoring in areas such as data modeling, data governance, and data visualization.
In this way, everyone will have the knowledge and skills necessary to work with data effectively, creating datasets, metadata, products, and more.
APPLY AI IN THE DOMAIN
We apply AI\ML to the data of your domain, with the most appropriate models, whether they are static or dynamic. In this way, data scientists will no longer be business generalists and will become domain data experts. With streaming data tools and ML applications, your company can have the intelligence necessary and focused by applying AI.
DATA SECURITY
Ensure the security of domain data, which means protecting against unauthorized access or misuse. This includes setting up robust access controls, implementing processes, and applying security technology on dynamic and static data, code generated by developers, and creating robust monitoring and auditing resources.
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Our experience in setting up secure architectures for the financial sector and the implementation of systems is significant.
DATA CULTURE
Build a data-driven culture within your organization. Promote the appreciation of data as a strategic asset and encourage its use both for informing and for decision-making. Establish clear data governance processes and principles, as well as building a data-literate workforce.
