The bad old days of mailing list management are thankfully far behind us. Dealing with databases full of customer addresses and other details used to be a tedious, time-consuming task, but modern software tools can make it an almost-effortless one instead. High-quality merge purge software, for example, can automatically and efficiently bring together data sources of various formats, rooting out matches and getting rid of duplicate information in the process.
Many businesses find themselves in possession of client lists and other data stores which inevitably contain some overlapping and redundant information. Oftentimes, each of these collections was developed through use of a particular piece of software, and sometimes the software which created a particular database is no longer in use or even maintained. Well-run businesses justifiably hold onto all of these assets regardless, however, thinking about a future time when the valuable information they contain can be extracted and put to use.
Flexible, powerful merge purge software is just what’s needed to recover the value latent in such idle collections, and to bring together actively used databases in a way that will make them even more worthy. The most basic software of this sort can easily strip out identical records from databases of different formats, ensuring that the end product does not contain overtly redundant, efficiency-sapping information.
More sophisticated packages take things a step further, being capable of using fuzzy matching and other advanced techniques to discover redundancies that would have evaded a less thorough search. Such packages, for example, can figure out when “John A. Smith” and “John Alan Smith” are the same person, and won’t rely solely on clues such as initials and spelling variants. Instead, they will make use of further evidence, including business-related records, to establish when what at first glance looks like a match really is so.
Many such packages will also make it easy for manual intervention into the process to easily improve the quality of the results; to figure out whether a particular vendor’s does, it is often enough to visit their website. On top of producing a provisional result set, software of this sort will also highlight the merging and purging steps it took in the process in useful ways, for example by assigning confidence scores to the judgments that were made. This can make it easier for users to later fine-tune the results, to ensure that everything turned out as it should have.
