Post by rahim on Feb 1, 2024 3:27:51 GMT -8
In different tables and/or systems. For better market processing, the addresses are transferred to a common database. If this is not done properly, duplicates or incomplete data sets can arise.The consequences resulting from poor data quality are problems with addressing, personal customer contact and automated processes. This can lead to higher costs, angry customers and distorted analysis results. In order to avoid making the wrong strategic decisions due to poor data quality, awareness of the importance of data quality should be created in your company and among your employees.How to find and clean up duplicates in your customer.
BasePoor data management can, among other things, lead to a large DB to Data number of duplicates. A duplicate is a record in a database that occurs multiple times.You can identify duplicates using appropriate software and clean them up automatically or semi-automatically. When comparing duplicates, more or less sharp phonetic, pattern-related or associative algorithms are used. Standardizing notations can lead to even better results. If an algorithm is only focused on the exact match of data, the address “Paul Klee Strasse 111” will not be found as a duplicate of “Klee Strasse 111”. Which methods you should use when comparing duplicates depends largely on your data base.Once you have found duplicates through a comparison, you have to decide which data set should “live on”. You shouldn't arbitrarily choose one record and simply delete the others.
So you run the risk of losing important information.You must determine a so-called “master record”. You enrich this with the other data sets identified as duplicates. The aim is to create a current data set from the duplicates that contains all relevant information and has a unique customer ID. Depending on the complexity of your company structure, this can be a very complex process.What is householding?Depending on the company, it may also make sense to look at its customers at a household or company level. A private household can be formed based on the same address and with the same last name, telephone number or email. Moving together can also help identify a household.
BasePoor data management can, among other things, lead to a large DB to Data number of duplicates. A duplicate is a record in a database that occurs multiple times.You can identify duplicates using appropriate software and clean them up automatically or semi-automatically. When comparing duplicates, more or less sharp phonetic, pattern-related or associative algorithms are used. Standardizing notations can lead to even better results. If an algorithm is only focused on the exact match of data, the address “Paul Klee Strasse 111” will not be found as a duplicate of “Klee Strasse 111”. Which methods you should use when comparing duplicates depends largely on your data base.Once you have found duplicates through a comparison, you have to decide which data set should “live on”. You shouldn't arbitrarily choose one record and simply delete the others.
So you run the risk of losing important information.You must determine a so-called “master record”. You enrich this with the other data sets identified as duplicates. The aim is to create a current data set from the duplicates that contains all relevant information and has a unique customer ID. Depending on the complexity of your company structure, this can be a very complex process.What is householding?Depending on the company, it may also make sense to look at its customers at a household or company level. A private household can be formed based on the same address and with the same last name, telephone number or email. Moving together can also help identify a household.