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Data Merging

Data merging is the process of combining two or more similar records into a single one. Merging is done to add variables to a dataset, append or add cases or observations to a dataset, or remove duplicates and other incorrect information Pre-merging Process Data Profiling: Before merging, it is crucial to profile the data, analyzing the different parts of data sources. This step helps an organization understand the outcomes of merging and prevent any potential errors that may occur. Data profiling consists of two important steps Analyzing the list of attributes that each data source possesses. This step helps an organization understand how the merged data will scale, what attributes are intended to be merged, and what may need to be supplemented Analysis of the data values in each part of a source to assess the completeness, uniqueness, and distribution of attributes. In short, a data profile validates the attributes of a predefined pattern and helps to identify invalid values


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Pre-merging Process Data Profiling: Before merging, it is crucial to profile the data, analyzing the different parts of data sources. This step helps an organization understand the outcomes of merging and prevent any potential errors that may occur. Data profiling consists of two important steps Analyzing the list of attributes that each data source possesses. This step helps an organization understand how the merged data will scale, what attributes are intended to be merged, and what may need to be supplemented Analysis of the data values in each part of a source to assess the completeness, uniqueness, and distribution of attributes. In short, a data profile validates the attributes of a predefined pattern and helps to identify invalid values

The process of merging can either be an integration or an aggregation. Once all the previous steps have been completed, the data is ready for merging There are a number of ways this process can be achieved According to specific use cases, appending rows, columns or both can be done. This can be quite simple if the datasets do not contain many null values and are reasonably complete. But there could be problems if there are vacant spaces in the datasets that need to be looked up and filled. Often, data merging techniques are used to bring the data together. It is also possible to perform a conditional merge initially, and then finish the merge by appending columns and rows. There is also the challenge of data duplication. There are different ways in which data duplication can happen in the dataset. To start with, there may be multiple records of the same entity. Further, there may be many attributes storing exactly the same information about an individual entity. These duplicate attributes or records can be found in the same dataset or across multiple datasets. The solution to this problem is using data matching algorithms and conditional rule.




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Data Merging Benefits

  • Merging data saves time and resources
  • It improves the accuracy and completeness of your dataset
  • By combining data from multiple sources
  • Merging data streamlines business processes
  • Improves decision-making







FAQ

What is Meant by Data Merging?

Data merging is a method for merging similar datasets from two or more tables to create a single data set (table) for easy reporting & analysis.

What is the goal when merging?

Mergers are most commonly done to gain market share, reduce costs of operations, expand to new territories, unite common products, grow revenues, and increase profits

why we need to choose CampaignBurst?

We Understand your target audience and customer, Our business teams can use their deep understanding to make your customer-centric decisions.


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