From Data Silos to Universally Interoperable Datasets to Modular Data Fabrics

Object-Oriented Data ModelingMaxxphase Data Compatibility is an Object-Oriented Data Management Strategy that focuses on abstraction, encapsulation, inheritance, and polymorphism to efficiently and effectively deliver whatever is required in a data ecosystem. Data Compatibility is a design-based methodology rather than a software-based one. Data Objects are designed and implemented without requiring complex data transformations or layered data architectures. We focus on designing data objects that are standardized, reusable, modular, and universally interoperable. These data objects can be used within an organization, across organizations, and throughout an industry.

We use Compatible Data Modeling, an enhancement to traditional data modeling, to design and implement  these classes of data objects:

    1. Data Compatibility Standards, where each is a master data domain-specific data entity designed for reuse in many Compatible Data Models. The Data Compatibility Standard is our lowest-level data object.
    2. Universally Interoperable Datasets, where each dataset is modularized by encapsulation within a group of Data Compatibility Standards. Each modularized dataset, as designed, is self-contained and provides a universal plug-and-play interface for dynamic interaction with other datasets.  The dataset inherits the metadata, data content, and functionality from each Data Compatibility Standard that encapsulates the dataset. One reusable Data Compatibility Standard data entity is included in the dataset model for each master data domain in the Compatible Data Model.
    3. Modular Data Fabric is a seamless data fabric composed of multiple Universally Interoperable Datasets, where dataset's data access, security, and ownership are inherited from each Universally Interoperable Dataset. Specialty Universally Interoperable Datasets are added to the fabric to improve overall data quality and to create a Single Source of Truth for the organization.

Each Universally Interoperable Dataset can be added or removed from the Modular Data Fabric without impact to other datasets. Since the datasets are universally interoperable, data can be combined on demand from any combination of datasets. All your data is directly accessible, auditable, and your derived insights are justifiable. In the data fabric, all your datasets now work together because end-to-end data integrity is enforced.