The data compatibility blog,
Join the discussion

Streamline IT

0 April 11, 2024

Most data management IT tasks are complex and tedious because the data foundation is disparate. Data commonality between datasets does not exist which isolates the data content of each dataset. Complex data transformation software abounds along with the data pipelines and data lineages that only contribute to the data integration ‘hairball’.

The Maxxphase Data Compatibility method streamlines IT by eliminating these complexities and simplifying the work effort. Data Compatibility methods enrich disparate datasets noninvasively to produce modular plug-and-play datasets. Data Compatibility emphasizes storing the dataset once in a form compatible with all other datasets. There is no need for data transformation software, pipelines, or data lineage.


Streamline IT by eliminating Disparate Data Management Efforts

A majority of the data management work done in IT today is an attempt to fix the disparity among datasets. These data management efforts typically require data transformation software and data pipelines. However, as designed, compatible datasets are directly interoperable. With compatible datasets, there is no longer a need for expensive, time-consuming, and fragile disparate data management practices such as data integration. Eliminating unnecessary disparate data management efforts greatly streamlines IT and reduces your time to market for data usage.


Streamline IT by Eliminating Data Redundancies

Since data is not shared across disparate datasets, each disparate dataset needs to be comprehensive. Therefore, data redundancy is very abundant in any collection of disparate datasets. In contrast, with compatible datasets the data content is stored once and can be shared with other compatible datasets. For example, if golden data content is maintained in one compatible dataset, that golden data content can be shared with any other compatible dataset. Golden data content for various master data domains such as products and locations, for example, can be shared by many other compatible datasets. Eliminating data redundancy streamlines IT and greatly improves the quality of your data.


The compatible data architecture is very agile which contributes to streamlining IT. If your data is a mess, or the volume of data is too great to handle or too costly, or if you need a directly interoperable data foundation with high-quality and accurate data to support advanced technologies, the compatible data foundation is your only logical solution.

Contact us

Blog Contact