What is data integration?


Data integration is described as a process of bringing data from multiple sources to a single destination. And it is from this single location that meaningful observations are managed to gather. In addition, it will integrate the collected data in a format that the information is detailed, precise, accurate, and up to date.

Data integration tool

Because there are so many options on the market today, choosing the best data integration platform may be difficult. If you don't know what you're looking for, you might make the wrong choice. So, before deciding on a provider, it would be highly beneficial unless you were prepared with the necessary ones and knowledge about data integration tools. 

Type of data integration tool

1. On-premise data integration

requires that an enterprise buys a software license for using it. Instead of hosting it from a third party, you can install and manage it at your company's physical office in the house. Therefore, the on-premise software is run under the direct control of your company.

2. Open-Source Data Integration

If you ever want to avoid using proprietary and potentially costly business solutions, open-source data integration platforms are the best option. It is also the best option if you want complete control over your data in-house.

3. Proprietary Data Integration

In terms of pricing, proprietary data integration tools mainly vary from open-source tools. They are often also constructed to cater very well to particular cases of business use. 

4. Cloud-Based Data Integration

As services that help incorporate data from multiple sources often into a cloud-based data warehouse, cloud-based data integration tools are iPaaS or integration platforms. 

Why Do You Need It? Data Integration

Data integration is a process that systematically consolidates and converts information from disparate source systems into usable and meaningful information.

To get a complete picture of your customers, you'll need to integrate data from your CRM system, marketing automation tools, customer-facing applications, and so on. It is hard to analyze this data without first retrieving it from its source.

Do you think it would be an option that is time-efficient? No way!

It enables you to access consolidated data and produce detailed reports in many places without manually signing into different accounts. Moreover, before analyzing, you can efficiently download information from multiple sources, reformat it, clean it, and automate this whole cycle for faster processing.

What are the different data integration methods?

1. Do it manually

It is a time-consuming and resource-intensive method in which integrations are completed point-to-point, and it must continuously track and manage them. 

2. Use middleware

Middleware data integration serves as a mediator between information that needs to be normalized and the pool of master data. 

3. Create uniform access

This method of data integration provides a front end that looks consistent with data from various sources. It returns the data to its original location. 

4. Use common storage

A copy of the data from the source is saved and used for general viewing in the integrated method. Thus, the fundamental concept in data warehousing is simple storage.


Data integration benefits everything from real-time information delivery to data enrichment, customer data analytics, and business intelligence. The original use case of data integration can also be said to be consumer and company data management.


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