To be competitive in the business world, companies continue to focus on customer experience. Many companies believe they have met or exceeded their customer’s expectation/experience. However, without realizing it, they end up sending emails to the wrong addresses or incorrect email addresses or worst it’s sent to a completely different person. You can only provide the best customer experience when you are able to tell what your customer wants before they even know they want it. That can not be achieved with bad data.
DataMatch Enterprise has so many features that make Data Matching so easy that you can finish a task at a fraction of a time versus doing it the conventional way.
One of the many useful features is Data Parsing.

What is Parsing?

Parsing is defined as breaking a data block into smaller chunks by following a set of rules, so that it can be more easily interpreted, managed, or transmitted by a computer. Spreadsheet programs, for example, parse a data block to fit it into a cell of certain size.

Why do we need to parse data?

In data parsing, we are organizing unstructured or misfielded data. We copy or extract attributes of interest, such as city name from a longer address or a product attribute from a longer product description; parsing email addresses to compare or match domains. Parsing dates to compare or match a year for example, database A only has the year, and database B has MM/DD/YYYY. Fuzzy matching by itself isn’t usually enough to get the most accurate record linkage results which is why we offer a much more comprehensive approach.
Pattern Builder is another feature of DataMatch Enterprise which is very useful in improving our matching results.  It is used to Parse data as described above leading to more accurate match results.
Companies use the Pattern Builder anytime they need custom parsing or to make the data more usable and/or to improve the quality and accuracy of the match results. The pattern builder allows us to build regular expressions which allows us to set custom parsing rules.

Example Scenario:

A business user needs to match last name and the year from the DOB column. If the DOB column contains a MM/DD/YYYY type of format for example, we can use the pattern builder to parse the one column with MM/DD/YYYY into three new columns, one for the MM, one for the DD, and one for the YYYY, which will allow the user to match with the YYYY.
Pattern Builder Screenshot:

Original data [Before using Pattern Builder]:

After using Pattern Builder:
Take note of the 3 new columns

With Pattern Builder, built into DataMatch Enterprise, you can be sure that parsing data is as easy as 123. Click here to schedule a DEMO.

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Merging Data from Multiple Sources – Challenges and Solutions

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