Have a view on your data
The aim of Data Architecture is to provide the best data in the best way to the appropriate user at the right time. The Data Architect is the point of contact regarding the data organization for the Enterprise Architecture. The provided advice, support and approval around the available choices keeping in mind the level of automation and agility required. He targets an evolutive and composite platform whatever could be your paradigm with both Data Fabric and Data Mesh, that could adapt with new usage or technology.
How does Data Architecture enable better data management ?
How can we help ?
- Organize and support the Data Architecture team to answer with the right information to the right person
- Initiate the data master plan that scopes the data usage and the administration of your data
- Assess the strengths and weakness of your existing architecture through Audit
- Define the best breed of Architecture pattern either Data Fabric or Data Mesh based on value to the organization
- Define the role and the Job Description of the Data Platform Product Owner
- Support the foundation of your scoping methodology around data
- Provide Data Urbanism far from the reality of the ground
- Avoid the big bang change in the tooling
- Reuse of selected tools or platform
- Measure Invest versus build-in
- Become the sticking point or bottleneck of the Knowledge of your Data
“Getting the right data to the right person at the right time in a efficient way is becoming the key challenge of data management for every company aiming to become data driven."
Ensuring the best of breeds Data Platform to support your information management.
A composite data platform to support your actual and future needs
The advisory team can support your journey to organize the Data Architecture team and initiate a Data Platform roadmap, design and select the good component for your usage of data.
- Initiate the Data Master Plan that scope the Data Usage and the administration of your data
- Prepare component selection between on-shelve and build
- Initiate capabilities enablement across the organization
- Work out the automation and DataOps definition
- Revise and validate Enterprise data model
- Select the best approach between Data Mesh & Data Fabric based on your maturity