Metaware provides a framework for simplified data
management
that is easy to use and empowers collaboration.
Metaware provides a framework for simplified data management that is easy to use and empowers collaboration.
Metaware transforms raw data into a clean, normalized business models — ready for analysis and AI in minutes using its patent-pending, blueprint-driven approach. We help you build fast—but also build it right, ensuring data clarity, consistency and governance from the start. Metaware fosters cross-department collaboration with a low-code platform and decentralized ownership, enabling active business participation.
The biggest challenge in our industry is building data platforms that
are resilient to change.
Many data projects fail when treated as purely technology-driven, project-driven efforts, often lacking alignment with business needs leading to unclear objectives and project-specific outcomes, neglecting the broader organizational vision.
Metaware's business glossary serves as a blueprint, defining the rules, terms, and relationships essential to your business. This approach aligns with business priorities, asking the right questions and enabling focused analysis.
Traditional data management tools often rely on complex code pipelines that are hard to maintain and scale. These pipelines create bottlenecks, obscure business logic, enforce vendor lock-in and drive inefficiencies across the organization.
Business teams can analyze source data, uphold essential business standards, define rules, and create glossaries—all within a user-friendly, low-code platform. No more complex pipelines, hidden business logic, or vendor lock-in.
In most data tools, data quality is poorly tracked, leading to bad data resulting in financial losses, damage to reputation, compliance failures, increased operational risks and ultimately hindering effective decision-making.
Metaware's rule engine simplifies data quality management with easy rule definition (including ISO standards) and integrates governance for accountability, delivering consistent, high-quality data.
Many data initiatives fail due to oversized, multimillion-dollar “big-bang” projects that lack proper business insight, rigid centralized architectures that create bottlenecks, and siloed, ad-hoc data management that duplicates effort.
Active participation by business teams empowers data ownership, while centralized teams gain transparency and a broader perspective—fostering seamless collaboration between business and technology.
Connecting Data to Decisions: Simplify, Empower, Achieve
Identify Data Domains, transformation, reports
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