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Deduplication of
data is one of the most critical components in the data cleansing process.
The true value of any database is determined by one fundamental component: the
quality of the data. Without data that is reliable, accurate, and updated,
organizations can’t deliver trusted customer, product, and other vital data
throughout the enterprise. One hidden culprit of bad data is duplicate records,
which results in waste, operational inefficiencies, lost revenue, less effective
sales and marketing, and poor customer service.
Consequently, the deduplication of data is one of the most critical components
in the data cleansing process. But before adopting these deduplication methods,
an organization must also integrate a data standardization process first and
foremost. Identifying duplicate records is challenging in and of itself. The
biggest roadblock to identifying duplicates lies in detecting non-exact matching
duplicate records – the data that appears to be multiple sets of information,
but are actually duplicate records. There are ways to overcome these challenges
– the most successful method is by employing fuzzy matching algorithms as part
of a merge/purge process. Detecting the most duplicate records helps streamline
databases, improve marketing efficiency, and achieve a unified, accurate view of
the customer.
When looking for a solution for
data matching and deduplication, look for a
program that can perform both exact matching and the latest fuzzy matching
techniques to match across multiple columns or, across multiple data sources, to
identify duplicates and manage your master data with incremental comparisons.
Next Article: Are Duplicate Records Eroding Your Bottom Line?
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