It is important to ensure current, correct and complete field content in your database, as this forms the basis for professional customer data management and successful customer contact. Data quality within a database Unfortunately, no matter how careful you are. The importance of data quality in companies Both large companies and SMEs are struggling. With the issue and the problems that arise from it. These quality problems arise from: An inaccurate entry . The call center employee makes a typing error or the customer’s address.How to find and clean up duplicates in your customer basePoor data management can, among other things, lead to a large number of duplicates. A duplicate is a record in a database that occurs multiple times.
Information is not complete
New registrations in the web shop DB to Data The same customer registers multiple times with different email addresses in the same web shop or a customer fills out the registration incorrectly. He Change of address : A customer changes his address or his last name due to marriage and does not report this. Combination of several address databases. For better market processing. If an algorithm is only focused on the exact match of data. 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”.
This can lead to higher costs
Duplicates or incomplete data sets can Frist Database arise. The consequences resulting from poor data quality are problems with addressing. Personal customer contact and automated processes. Angry customers and distorted analysis results. In order to avoid making the wrong strategic decisions due to poor data quality. 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.