What is Merge Purge Software
Merge purging is the process of combining two or more lists or files, identifying and/or combining duplicates and eliminating (purging) unwanted records. Through phonetic and fuzzy matching algorithms, human errors such as typos will be located and removed from data. The task of merge purging a database can be expensive and time-consuming but with data quality tools like merge purge software cleaning up dirty data is easy.
Merge Purge Product Comparison
Major merge purge software tools on the market provide users an efficient way to combine and remove data within large data sets. In a recent independent study, done by Curtin University Centre for Data Linkage, Data Ladder’s merge purge software DataMatch Enterprise outperformed major companies on both accuracy and speed:
Introducing DataMatch Enterprise
The complete data quality, cleansing, matching, and deduplication software in one easy to use software suite.
Can Be Used By Anyone in Any Business
The right data matching software should be easy to use by anyone and in any situation. You don’t need to be a data scientist, data analyst, IT professional or be in the Big Data industry to benefit from clean data. Learn how these companies made their data work better for them using data matching software:
Marketing & Sales
|Finance & Insurance||Retail|
How Data Matching Software Can Benefit Your Business
From merging records from multiple lists to purging addresses from others, merge purging data helps clean the underlying data set to achieve several goals, including:
Enjoy Increased ROI: Your marketing and sales efforts are more effective with accurate customer data
Make Confident Decisions: When making to make strategic decisions, you can rely on your data to point you in the right direction every single time
Keep Your Reputation Intact: Avoid embarrassing situations when you are dealing with customers
Flexibility Where You Need It: Data matching software can be used to clean and optimize any data type, perfect for any type of business or organization
Cut Costs: Reduce postage and mailing costs by eliminating duplicates from your database using advanced data matching technology
Keep An Eye On The Competition: Compare your product catalog to the competition, quickly determining where the gaps and opportunities are
Efficiently Manage Product hierarchies: Use the extensive data parsing feature to scan product descriptions and create new product hierarchies rapidly and easily
Save Time: Skip the manual process when combing legacy systems and cleaning old data and cut months off implementing a new system
Be Proactive Not Reactive: Avoid data quality issues even before they start – and you find out too late
Know Your Data: Achieve a deep understanding of your data within minutes using our instant data profiler…or….
Find data you haven’t seen in years or even know you had within minutes using our data profiler
We Offer Even More with DataMatch Enterprise
Updated, easy to use interface
Complete set of data cleansing tools
Link in varied data sources to unify records
Semantic matching for unstructured data
Process millions of records
World-class customer support
Costs 95% less than comparable solutions
Understanding best practices in the merge purge process is often the first step in achieving success with this data cleansing process. One of the first steps in the merge purge process is combining different databases with different sources (such as an SQL server, MySQL, Excel, ODBC). DataMatch will import, combine, and export to the most common database formats. DataMatch will automap similar fields from different data sources together. Here are a few other best practices for merge purging data:
Fuzzy logic identification of percent matches between records and setting minimum percent match thresholds by field
Acronym identification for matching
Cleaning and standardizing data prior to matching (street to street, eliminating unnecessary syntax in phone numbers, etc.)
Applying libraries for standardization, especially for first names (such as Jon, Jonathan, and John
The Importance of Data Survivorship
Survivorship is critical to a successful merge purge. While removing duplicate records it needs to be decided which piece of data should stay (survive) and which one should be deleted.
Here you have two examples of the same data, with small differences. A single master record must be chosen to maintain data quality. With DataMatch, you can select which field of data survives, which field to merge on (in our example, it would be the customer number) and in which order (ascending or descending).
While standard merge purge software can remove important business data, DataMatch will retain and keep all pieces of information from the same master record — in a new data field.
All of the alternate information is captured in a brand new field, so the user has the benefit of a single master record without data loss. DataMatch never deletes any information from the source files; all information is kept temporarily in memory, so the user can test various merge purge settings. The final result would look like this: