Data integration capabilities are therefore influenced with analytics uses, and business intelligence is the thing which SAS-DataFlux data integration solutions are most often chosen for, while other cases are rather a rarity. DataFlux Data Management Server 2. For most of typical uses, SAS-DataFLux system is therefore one of the first choices made by customers from all over the world, where the company is present. Overview Documentation Training Samples Conversations. Training Learning path for Data Management Need help?
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Furthermore, it is obvious that some data inconsistencies exist such as name prefixes and suffixes, inconsistent casing, incomplete address data, etc. This interface is new in version 8 and helps provide quick access to the functions one would use most often. This change is actually helpful as opposed to some GUI changes made by companies by combining a lot of the settings into a central place.
Profiling — Most nodes here help provide a synopsis of the data being processed. Here the output of profiling can be linked to other actions. Enrichment — As the name suggests, these nodes help enrich data, i. Monitoring — Allows for action to take place on a data trigger, e. Either way, the data will appear in my preview window instant gratification is one of the great things about Architect. I want to point out just two things here: 1. This is likely to have happened because too many fields are wrong and the USPS data verification system is designed not to guess too much… 2.
If I would have used that, the correct Zip-4 would have been calculated. This is because the USPS system recognizes as an address within a correct range. Nonetheless, pretty neat, eh? For this reason you see this here. After that, I can preview as before. Note how well DataFlux picked out the first, middle and last names, not to mention the prefixes and suffixes. The answer here is yes. DataFlux utilizes several algorithms and known last names, first names, etc.
By that I mean that the placement of a comma in a name greatly enhances the parser ability to determine the location of the last name.
For example, often times perhaps most of the time , nothing can be done about data in a system once it is entered. This step is important because intelligent parsing, name lookups, etc. Here you can see that match codes ignore minor spelling differences, take into account abbreviations, nicknames, etc.
Why is this so significant? We now have an easy way to find duplicates! Match codes could be stored in a database and allow quick checks for duplicates!
Well because of the clustering conditions I set.