Direct Dataset Interoperability
Direct Dataset Interoperability is an advanced and very innovative method of data management that promotes collaboration among compatible datasets. With Direct Dataset interoperability, multiple compatible datasets will collectively function in a manner indistinguishable from a single consistent dataset. Therefore, you can combine data across compatible datasets on demand for whatever business need you have.
Dynamic or Plug-and-Play Direct Dataset Interoperability was also developed and patented at Maxxphase. Now, each compatible dataset becomes a Modular Plug-and-Play Dataset that can be combined with any other modular datasets on demand as required to form a flexible aggregate dataset. Compatible aggregate datasets can remain virtual or can be materialized as part of the information-building process normally supporting business intelligence and analytics.
The focus of current Disparate Data Management is the consolidation of source datasets into a target dataset using hand-crafted data transformations. On the other hand, Compatible Data Management focuses on the noninvasive standardization of datasets in place to provide Direct Dataset Interoperability. Compatible data management eliminates the need for very expensive and time-consuming data integration, data warehouse, and data fabric projects that are prevalent in disparate data management. Compatible data management is more efficient, reliable, and scalable than disparate data management while delivering far superior dataset functionality.
Compatible Directly Interoperable Dataset Features
All compatible datasets are encapsulated within a set of Maxxphase Data Compatibility Standards which delivers plug-and-play direct dataset interoperability. These standards were designed to deliver several important data functionality enhancements to the original datasets.
First, these Data Compatibility Standards provide a common foundation of master data and metadata for each compatible dataset. The data in a compatible dataset is data integrated with any other compatible datasets' data through their copies of the common foundation. Therefore, once datasets are made compatible, expensive and time-consuming data integration projects are no longer needed.
Second, the Data Compatibility Standards support extensive direct dataset interoperability through their copies of the common foundation. Compatible datasets form direct and dynamic data access paths among them. These data access paths are the "threads" that weave the compatible datasets into a Compatible Data Fabric. Since the data is integrated across compatible datasets and the datasets are directly interoperable, you now have a 360ᴼ view of all your data.
Third, we designed the Data Compatibility Standards to support business intelligence and analytics functionality. Each compatible dataset is analytics-ready as it supports multidimensional analysis. Since these datasets are extensively directly interoperable, and the data from multiple compatible datasets are integrated, these compatible datasets can be blended or harmonized.
Finally, a compatible dataset interacts with any other compatible dataset through its common foundation of master data and metadata. These direct and dynamic interactions give each compatible dataset its plug-and-play abilities within the Compatible Data Fabric. A new compatible dataset can be added to the Compatible Data Fabric and immediately directly interacts with all other compatible datasets within the data fabric.
Datasets that Collaborate
Siloed datasets are disparate and do not directly collaborate. Each siloed dataset represents a set of compartmentalized data that is incompatible, and each dataset offers a conflicting view of information, especially master data. These conflicting views of information are a huge problem for business users; hence, the prominence of data transformation-based data integration to resolve the conflicts. However, transformation-based data integration does not remove the conflicting data from the source, and in fact, further instantiates the isolation of your datasets from each other.
Maxxphase Data Compatibility delivers standardized data and metadata commonality to each dataset needed to dynamically and directly connect these datasets. This data and metadata commonality resolves conflicting master data views across previously siloed datasets. Direct connections across datasets do not need data transformations. Since no data transformations are used in these connections, the datasets become directly interoperable. Direct interoperability means the data from one dataset can be seamlessly joined with the data from other datasets.
The first-ever directly interoperable datasets were designed and implemented by Maxxphase. Our patented data design and management methods produce these interoperable compatible datasets. Because of the design for extensive dataset interoperability, a consistent Compatible Data Fabric is spontaneously formed from any group of compatible datasets. With Maxxphase Data Compatibility, all your datasets now collaborate to provide a single trusted view of your business data and information whenever or wherever needed.