How To Merge Purge Large Databases

An average enterprise uses 464 custom applications to digitize its business processes. But when it comes to generating useful insights, the data residing at disparate sources must be combined and merged together. Depending on the number of sources involved and the structure of data stored in these databases, this can be quite a complex task. For this reason, it is imperative that companies understand the challenges and process of merging large databases.  

In this article, we will discuss what the merge purge process is and see how you can merge purge large databases. Let’s begin. 

What Is A Merge Purge?

Merge purge is a systematic process that screens all records residing at different sources and implements multiple algorithms that clean, standardize, and deduplicate data to create a single, comprehensive view of your entities, such as customers, products, employees, etc. It is a very useful process, especially for data-driven organizations.  

Example: Merge purge customer records 

Let’s consider a company’s customer dataset. Customer information is captured at multiple places, including web forms on landing pages, marketing automation tools, payment channels, activity tracking tools, and so on. If you wanted to perform lead attribution to understand the exact path that led to lead conversion, you would need all these details in one place. Merging and purging large customer datasets to get a 360 view of your customer base can open big doors for your business, such as making inferences about customer behavior, competitive pricing strategies, market analysis, and much more. 

How To Merge Purge Large Databases? 

The merge purge process can be a bit complex since you don’t want to lose information or end up with incorrect information in your resulting dataset. For this reason, we perform some processes before the actual merge purge process. Let’s take a look at all the steps involved during this process. 

  1. Connecting all databases to a central source – The first step in this process is to connect the databases to a central source. This is done to bring data together in one place so that the merge process can be better planned by considering all sources and data involved. This may require you to pull data from a number of places, such as local files, databases, cloud storage, or other third-party applications. 
  2. Profiling data to uncover structural details – Data profiling means running aggregational and statistical analysis on your imported data to uncover its structural details and identify potential cleansing and transforming opportunities. For example, a data profile will show you a list of all attributes present in each database, as well as their fill rate, data type, maximum character length, common pattern, format, and other such details. With this information, you can understand the differences present in the connected datasets and what you need to consider and fix before merging data. 
  3. Eliminating data heterogeneity – structural and lexical Data heterogeneity refers to the structural and lexical differences present between two or more datasets. An example of structural heterogeneity is when one dataset contains three columns for a name (First, Middle, and Last Name), while the other just contains one (Full Name). On the contrary, lexical heterogeneity has to do with the contents present within a column, for example, the Full Name column in one database stores the name as Jane Doe, while the other dataset stores it as Doe, Jane
  4. Cleaning, parsing, and filtering data – Once you have the data profile reports and are aware of the differences present between your datasets, you can now begin to fix things that may cause issues during the merge purge process. This can include:
    • Filling in empty values, 
    • Transforming data types of certain attributes, 
    • Eliminating or replacing incorrect values, 
    • Parsing an attribute to identify smaller subcomponents, or merging two or more attributes together to form one column, 
    • Filtering attributes based on the requirements of the resulting dataset, and so on. 
  5. Matching data to uncover entities and deduplicate – This is probably the main part of your data merge purge process: matching records to find out which records belong to the same entity and which ones are a complete duplicate of an existing record. Records usually contain uniquely identifying attributes for customers. But in some cases, these attributes may be missing. Before you can effectively merge data to get a single view of your entities, you must perform data matching to find duplicate records or the ones that belong to an entity. In case of missing identifiers, you can perform fuzzy matching algorithm that selects a combination of attributes from both records, and computes the likelihood of them belonging to the same entity. 
  6. Designing merge purge rules – When you have identified the matching records, it can be difficult to select the master record and label others as duplicate. For this, you can design a set of data merge purge rules that compare records according to the defined criteria and conditionally select master record, deduplicate, or in some cases, overwrite data in records. For example, you might want to automate the following:
    • Retain the record having the longest Address,  
    • Delete duplicate records coming from a specific data source, and 
    • Overwrite the Phone Number from a specific source to the master record. 
  7. Merging and purging data to get the golden record – This is the final step of the process where the execution of merge purge process happens. All the prior steps were taken to ensure successful process implementation and reliable result production. If you are using advanced merge purge software, you can perform the previous processes as well as the merge purge process within the same tool in a matter of minutes. 

And there you have it – merging large databases to get a single view of your entities. The process may be straightforward but a number of challenges are encountered during its execution, such as overcoming integration, heterogeneity, and scalability issues, as well as dealing with unrealistic expectations of other parties involved. Utilizing a software tool that makes automation and repeatability of certain processes easier can definitely help your teams in merging large databases quickly, effectively, and accurately. 

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