The Power of Plug-and-Play Datasets

As part of our research to eliminate siloed datasets, it was determined that disparate datasets are siloed because of conflicts in their master data and metadata. When these master data and metadata conflicts were resolved, these datasets became directly interoperable. But, that is only the beginning of this story.

Master data and metadata standardization were developed to enrich existing datasets without touching the native data. This enrichment was specifically designed to produce analytics-ready modular datasets that had dynamic plug-and-play functionality. Now, any disparate datasets can be enriched to be directly and dynamically interoperable.  On-demand, any compatible dataset can dynamically interact to blend with other compatible datasets delivering the data and information you require.

Maxxphase Data Compatibility is a very innovative foundational change for the IT industry. Transitioning from a foundation of disparate isolated siloed datasets to a foundation of modular plug-and-play datasets is a simple matter of non-invasive dataset enrichment.

 The Origins of Data Silos

Disparate datasets, often characterized as data silos, lack the means to connect with other datasets directly. This lack of direct connections between datasets prevents businesses from efficiently taking advantage of the processes and technologies that require collaboration from multiple data sources.


Siloed datasets have always been a problem for digital data representations. Computers require exact data content matches across datasets in order to efficiently data integrate the datasets. Unfortunately, conventional data design methods, such as data modeling, do not provide the needed dataset content matches. In fact, conventional data modeling methods are the origin of data silos as you can only design disparate datasets. For this reason, Maxxphase provides enhanced data design methods that solve your data silo problems by designing direct dataset interoperability into each dataset.


Data silo elimination

How Data Compatibility Eliminates Data Silos

Conventional disparate datasets lack the master data commonality required to make datasets directly interoperable. Our patented Data Compatibility Standards are used to simply enrich any disparate dataset by adding the master data commonality needed to support direct interoperability among datasets. Any existing dataset can be retrofitted to be compatible.

Maxxphase Data Compatibility Standards can be added to any dataset non-invasively. The original data and metadata of the dataset are not modified, therefore, this master data standardization is considered a simple dataset enrichment. However, this dataset enrichment imparts data functionality not currently available to conventional disparate datasets.  Each compatible dataset becomes a very dynamic and flexible analytics-ready modular plug-and-play dataset. Each modular dataset is multi-faceted and can plug into any other modular datasets on demand to be indistinguishable from a single consolidated dataset.

Compatible Data Architectures are very clean and simple compared to disparate data architectures often characterized as the data integration hairball. Unlike disparate, silo-based data architectures, the compatible data architect is composed of compatible modular components used for information building. Materialized compatible aggregate modules are also multi-faceted plug-and-play modules, so information building is also very efficient and productive. So just stop wasting the ridiculous amounts of time, effort, and money needed to build data transformations that just create more and more data silos. Please contact us today to start your  Data Compatibility journey!