Data Compatibility Defined

Data Compatibility refers to datasets that work together, in contrast to the data silos that currently exist in typical data architectures. Compatible datasets have data integrity enforced between datasets, whereas data silos do not. As a result of data integrity enforcement between datasets, the data architecture:

• is a data fabric with end-to-end data integrity.
• is defragmented as compatible datasets may now be seamlessly joined and their data combined.
• has much improved data quality as data integrity enforcement eliminates data disparity across datasets.

The data architecture modernization delivered by data compatibility is needed to best support AI/ML-ready data.

Data Compatibility is a design-based approach that features Universal Dataset Interoperability as opposed to the ETL software-based approach that features data integration. Utilizing superior data design methods, in the form of Compatible Data Modeling, ensures that data integrity is enforced between the instantiated compatible datasets of a data architecture. Any data architecture that does not enforce data integrity between datasets is a corrupted data architecture, which results in a 'Garbage In' problem for your organization.

Is it time to modernize your data architecture?

If you are interested in modern technologies, such as AI/ML, a Modular Data Fabric is your only real choice. Data integration methods are too slow, and data transformations distort your data. The Modular Data Fabric is the only data architecture designed to fully support AI/ML. However, there are many other reasons to modernize your data architecture, including improved data quality, reduced costs, faster time-to-market, greater scalability, and enhanced usability. If you are dissatisfied with your current fragmented, disjoined, and siloed data ecosystem that is not trustworthy, it is time to modernize.