When integrating data from legacy systems to SaaS applications or other enterprise software it is common that the data will have any combination of problems. Things like the appearance of bad/unexpected characters, inconsistent date or numeric formats, missing mandatory values, etc. When you are looking to automate business processes that rely on this data, it can be an ongoing maintenance problem. Many companies end up dealing with ongoing manual intervention of their integration processes by fixing this data by hand and then reprocessing it. In fact, analyst groups like the Yankee Group estimate that companies end up spending more than twice as much maintaining the integrations versus the initial purchase of the integration software.
To help solve this problem, we have added Cleanse capabilities to Boomi On Demand. You now have the ability to set up validation rules for individual fields in individual records. Things like "is the field empty", "is it a date", "is it in dollars", etc. When a validation is failed, you can then explicitly handle this failure. You can choose to reject just the failed record, halt processing of the data set altogether, or define options to automatically repair the data so it can be processed. For example, imagine a scenario where you are retrieving a flat file containing several hundred thousand rows of data. You can now strip out just the rows that fail your validations, and continue processing the "clean" rows successfully. You can queue up any rejected data and handle it any number of ways; email someone, ftp that data somewhere, create log records in a database, etc.
You can try it out yourself for free here.
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