There are three key thoughts before I go on. Your developers and admins will thank you for buying a dupe preventer that is more forgiving and/or flexible in error handling. Sometimes, the only choice is to temporarily disable the dupe blocker, do the push, and then remember to re-enable the blocking system. Ability to configure the fuzzy parameters (hopefully, for each field)įor records created by real-time processes, there’s one additional attribute that has to be mentioned: how does the tool behave when a dupe is created by code or via a web-service call (e.g., SOAP or ReST)? Most tools block creation of such records, which might seem OK…that is, until you try to deploy a bug-fix and discover that you can’t because some piece of test code creates a duplicate record and prevents you from pushing anything into production.Matching on more than just a couple of fields.Compatibility with the object/table you’re working with (there’s more to life than just leads and contacts).Compatibility with your file formats (mostly, this is just XLS and CSV). ![]() For imports, you’re looking for five main attributes: There are tools that help prevent dupes, but they are absolutely not created equal. The longer a dupe exists, the more the data quality issues metastasize, making rectification ever more costly. The big deal about dupesĭuplicate records are dangerous to system credibility because users can’t find the updates that they’ve made (they’re looking at the wrong copy of the record) and each of the dupes represents an incomplete record with multiple data quality issues. We’ll show you how to avoid the main pitfalls here. When it comes to deduping, though, even the best tools out there need to be used with careful attention to detail and the prep work that can take days. Fortunately, a number of services and tools have arisen to automagically improve data quality (in particular, Salesforce’s ). Data comes from too many flaky sources, and there aren’t enough incentives for organizations and people to really follow standards. Every enterprise system has the risk of data quality problems, but customer relationship management (CRM) and marketing automation systems are incredibly vulnerable to incomplete records, bad data, and duplicate records.
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