Now that businesses are living in a digital age, there are many more uses for data, whether it’s customer data, business data, or data from third parties. However, how can companies sort through this massive amount of data? The answer is to have all of that data from various data sources held in one convenient, unified view. That’s where data integration comes into play. You may be wondering, “What is data integration?” We’re here to answer that question and provide some common examples of data integration in use. So, let’s jump right into it.
Data Integration Explained
Data integration is the process of combining data from disparate sources into a unified view. This can be done for a variety of reasons, such as to improve data quality, support reporting and analytics, or enable data sharing.
There are many different ways to integrate data. The most common approach is to use a data warehouse or data mart. A data warehouse is a centralized repository for data from multiple sources. A data mart is a smaller, more specialized repository that contains data from a single source.
Another common approach to data integration is to use a data bus. A data bus is a system that allows different applications to share data. This can be done either in real-time or as a batch process.
Data integration can also be done using a data lake. A data lake is a repository for large volumes of data, which can be processed in any way desired. This allows users to analyze data in its natural form, without having to pre-process it.
Finally, data integration can be done using a data federation server. A data federation server is a system that allows users to access data from multiple sources without having to create connections to each source individually. This can be useful for data that is spread out across multiple databases or data stores.
Data Integration in Use: Cleansing Data
Data cleansing is an important part of data integration, and it is used to clean up and standardize the data before it is integrated.
There are many different ways to cleanse data. One common approach is to use a data cleansing algorithm. This algorithm identifies and corrects errors in the data, such as incorrect values or missing data. Another approach is to use a data cleansing tool. This tool allows you to cleanse the data manually, by correcting errors and adding missing values.
Data integration is a critical process for organizations that want to make the most of their data. By cleansing and integrating the data, businesses can get a better understanding of their customers, their products, and their markets. This, in turn, can help them make more informed decisions and improve their bottom line.
Data Integration in Use: Importing Data
Importing data into a database is a common example of data integration in use. When a company wants to track its sales data, for example, it might import data from its point-of-sale system into a database. This allows the company to track sales data over time and see how it changes as the business grows.
Another common example of data integration in use is when a company wants to combine data from different sources. For example, a company might want to combine customer data from its CRM system with data about the customers’ purchases from its e-commerce system. This can help the company understand its customers better and create a more complete profile of them.
Data integration is also used to clean up data. For example, a company might want to combine data from two different sources that have different formats. This can be done by using data integration tools to map the data from one source to the data from the other source. This can help the company to get a more accurate picture of what’s going on in its business.
Making the Most of Business Data
With our explanation of data integration and the examples provided, you should have a clear view of what data integration can do for your business. This unified data can help you infuse the data in your systems into your more modern business applications.