Organisations receive data from a wide variety of sources. But …

Collecting data is only the beginning, to make those data count, organisations need to transform the data from letters and numbers into names, addresses, water levels, forecasts, warnings, or even points on a map. 

To do this the data need to be understood by the system, and data can only be understood when the structure of the data is known. 
Datalign can verify that data structures are known at the point of entry, by validating the data against schemas. This means that errors are found and fixed immediately, and by the people best qualified to do it.
But validating data at the point of entry should not mean fitting a straitjacket. In the real world data are generated by many different devices and each device can potentially produce a different data structure. This is why Datalign allows administrators to maintain multiple schemas and to apply groups of schemas to incoming datasets. If the data conform to one of the schemas in the group, then it is accepted for further processing. In this way Datalign can accept data in multiple formats whilst ensuring that all of them are properly understood. 
Downstream processes allow the schemas to be mapped to standardised formats using a selection of adaptors. 
 Schemas and validations are a module for Datalign, which works together with tagging, workflows and pipelines and all the other data management tools you have come to rely on.