As your organization grows, you will likely merge data from different sources. By combining data from multiple sources, businesses can streamline operations and improve accuracy and efficiency. This can be daunting, but understanding the different merge stages can help you complete the process. This process allows you to combine data from two or more sources into a single table. The data in each source is usually formatted differently, so this process takes care of combining the data into a single table and then formatting the data to look the same in each source. Keep reading to learn more about the various stages of data merging.
Data Merging Stages
Data consolidation and data integration are terms often used interchangeably, but they have different meanings. Data consolidation refers to combining data from multiple sources into a single database or data warehouse. Data consolidation aims to improve performance by reducing the number of queries running against various databases. Data integration, on the other hand, combines data from multiple sources into a single logical view. Data integration aims to improve accuracy by ensuring that all the data in the system is consistent. There are a few different ways to combine data, but the most common way is to use an Excel spreadsheet as the data source. Excel is a popular program for creating and managing data, and it’s easy to use this process with Excel.
Pre-Processing Data: The first stage of data merging is data preparation. This can be done in various ways, but the most common is to load all of the data into a database or application. Once it’s all in one place, it can be easily sorted and filtered to find the specific information that is needed.
Matching Data: Once data is cleansed and standardized, the next step in data integration is to match the data. This involves identifying which records in one table correspond to which records in another table. The goal is to create a golden copy of the data, with each record represented only once.
Cleansing and Transforming the Data: This stage involves cleansing and transforming the data so that it’s in a consistent format and meets business requirements. Data cleansing is the most important stage, as it’s the foundation for accurate and reliable data. Data cleansing is the process of removing inconsistencies and inaccuracies from data.
Loading the Cleansed and Transformed Data Into a Final Destination: This stage involves loading the cleansed and transformed data into a final destination such as a data warehouse or operational database.
Types of Data Merges
There are three types of data merges:
Union: There are a few methods of union data merge. The most common is to use an SQL statement to combine the data sets. Another common method is to use a data integration tool to combine the data sets.
Append: This allows you to combine two or more data ranges into a single data range. The data in the merged range will be in the same order as in the first range.
Intersect: The intersect is perfect for combining data that has been cleaned and standardized. It can also be used to combine data from multiple sources. Using the intersect data merge, you can avoid duplicating data and create a single, consolidated list.
Once the data has been matched, it can be integrated into a single database. The final step in the data integration process is to clean up the data and remove any duplicates that may have been created during the matching process. Once the data is cleaned up, it’s essential to check the results to ensure no errors. This can be time-consuming but is necessary to provide accurate data.