data analytics
We analyze and catalog your data sources to provide a clear view of what information you have, where it is located, and how it relates across the organization.
We also assess its readiness for AI initiatives by evaluating data quality, lineage, access, and governance, delivering an actionable assessment to support modernization and AI adoption.
Our proprietary methodology. A scorecard for each data domain that measures exactly what separates having data from being able to use it for AI, regardless of the tools you use.
A clear inventory of systems, owners, tables, files, APIs, and data flows.
Identify disconnected information and improve data sharing across teams.
Understand where sensitive data resides and how it is accessed.
Reduce discovery time and improve data readiness.
Eliminate duplication, inefficiencies, and unnecessary complexity.
Build trustworthy AI initiatives on governed and well-understood data.
Data sources, integrations, tools, and consumers.
Technical and business metadata, tags, domains, and data owners.
Track where data comes from, how it is transformed, and where it is consumed.
Identification of personal, financial, and regulated data, with control recommendations.
Structured evaluation of AI data maturity by domain.
Definition of key metrics, entities, and business concepts.
Immediate improvement opportunities + governance and evolution plan.
The methodology is always the same; the cataloging tool changes depending on your platform and maturity level.
We use Unity Catalog as the foundation for inventory, lineage, and data governance.
We use Microsoft Purview (Data Map, Data Catalog, Unified Catalog) for cataloging, automatic classification, and lineage.
We apply a guided methodology that generates the deliverables required to move forward with data governance initiatives, Purview, or Unity Catalog.
In all scenarios, the AI Readiness Scorecard and Business Glossary are standard deliverables and do not depend on any specific tool.
Concrete results from day one
Actionable deliverables
- Data source map: Inventory of systems, owners, and business criticality.
- Key asset catalog: Documented and classified tables, datasets, and reports.
- AI Readiness Assessment: Analysis of data quality, lineage, access, and governance.
- Findings report: Identification of silos, duplication, and improvement opportunities.
- Business glossary: Shared definition of key metrics and concepts.
- Prioritized backlog: Recommended initiatives and next steps.
We assess your data sources, quality, and governance and provide a roadmap prioritized by business impact.
No. If you do not have a cataloging tool, we work with a guided approach based on interviews, documentation, and a structured inventory. The key deliverables, including the data source map, scorecard, and business glossary, are independent of the technology used. If you decide to implement Purview or Unity Catalog later, the work already completed can be integrated directly.
It is a structured assessment of whether your data is ready to support AI models and initiatives. We evaluate six dimensions across each data domain: data quality, coverage, lineage, governance, access, and sensitivity classification. The outcome is a scorecard with prioritized recommendations.
It depends on the number of data sources and the scope of the assessment. We can start with a focused pilot on critical domains and then scale progressively in phases.
IT and Data teams (data engineering, BI, and architecture), along with business process owners. We facilitate alignment across stakeholders and provide the necessary documentation throughout the project.