Headline:
When Properly Designed and Managed, all Data is Universally Interoperable!
Our Vision - Universal Dataset Interoperability
Just like the internet opened untold opportunities for connectivity and networking, data compatibility will open business opportunities beyond imagination. Our vision is to make data compatible within your organization and across multiple organizations, industries, and globally. Our vision is to support and extend the FAIR Data Principles by implementing a new data management paradigm grounded in Object-Oriented Data Principles.
Current Datasets have very poor data quality because:
- Data integrity is not enforced between datasets.
- Redundant and disparate metadata and data content are stored in multiple places across datasets.
- Unique identification of data records is not carried beyond each dataset.
Our vision is to address these data-quality deficiencies to deliver a high-quality Single Source of Truth.
Data works best when it can be utilized seamlessly to support business operations, decisions, AI/ML, and analytics while building information and knowledge. To achieve seamless dataset interoperability, data from each dataset must be compatible and shared so that the data functions as if it were never stored separately.
Our Solution - A Single Source of the Truth
Our solution is based on five major innovations across data management, data design, data architecture, and dataset implementation. These five innovations are:
- Compatible Data Modeling - an enhancement of traditional data modeling that results in modular plug-and-play data models.
- Object-Oriented Data Management - a new approach to data management that includes common object-oriented principles of abstraction, encapsulation, inheritance, and polymorphism.
- Data Compatibility Standards - Master data domain-specific metadata, data content, and data behaviors used to make datasets and Modular Data Fabrics universally interoperable.
- Universally Interoperable Datasets - Modularized plug-and-play datasets encapsulated within a set of Data Compatibility Standards.
- Modular Data Fabric - A data architecture composed of multiple Universally Interoperable Datasets.
Interestingly, none of these innovations are invasive to existing datasets or to existing data architectures. Data Compatibility Standards are added to any dataset, by reference, without changing existing metadata or data content. The added Data Compatibility Standards do not impact the dataset's application software or existing ETL software.
Maxxphase Data Compatibility is a modernization of existing data management methods. With data compatibility, we address many issues in typical data management, especially by eliminating data silos, improving data quality, and making data both analytics and AI-ready. Our goal is to provide your organization with a Single Source of Truth you can rely on.