Top 5 Data Integration Methods For Great Businesses

The world of information technology is evolving to newer heights. The global IT spending will reach $3.92 trillion towards the end of the year. More businesses use CRMs, ERP’s, and applications to manage their operations.

Yet, one thing that binds all the isolated systems is data integration. It is the process of gathering, cleaning, and presenting data to derive meaningful information. Firms collect their data in many data-lakes, archives, and file systems. This increasing amount of data even includes not valuable “bad” insights.

So, you must extract analysis-worthy information from this massive deluge of data. Choose the right data integration tools to make smart-informed decisions. It will help you retrieve business intelligence scattered across databases. The following are the top five data integration methods to help you grow your business.

What Are The Different Data Integration Methods?

Middleware Data Integration

Middleware acts as a bridge between your different applications. It enables data transfer back and forth. It serves as a system interpreter when you integrate legacy services with newer ones. You can deploy middlewares for the following types of interactions.

  • System to system/database,
  • Database to a database,
  • Human to system,
  • Web and mobile-based.

Middleware data integration tools can connect both internal and external systems. Most of these tools use APIs to ease data flow. These APIs can combine two different applications by acting as a shared layer. Because of their versatility, businesses are using them across the IT landscape.

But they do have their limitations. For example, they need regular maintenance and updates by a technical developer. To cope up with the issue and simplify the process, you can hire a MuleSoft development company. Their experts can manage complex data types and lessen the difficulties.

Application-based Data Integration

When you remove the Middleware layer, you have two applications talking to each other. And that’s another way to integrate data into a unified repository. The primary advantage of this approach is its simple setup. There is no complex component, and a single application does all the work.

It makes the process faster. One application serves as a source and another as a destination. You can deploy this data integration system for many applications with real-time data.

But, this approach shows data inconsistency, which will reflect in the results. When there is no middleware layer, the information doesn’t stay consistent. Moreover, setting up the system can be a difficult task for some businesses. It may demand the involvement of cross-department developers and managers.

Data Federation

You cannot run an enterprise-level data integration workflow without data federation. This method takes a different approach and works by moving data physically. It creates an abstraction layer that offers a unified view of the other systems.

You can consider the abstraction layer as a virtual database. This database is reliable and efficient. So, data analysts can work with both the systems from a single platform. In this method, there’s no need to move data across the networks.

The data federation method works best for heterogeneous data retrieval and analytics. You can semantically connect and access them on relational databases or cloud platforms.

Data Consolidation

Businesses using high-quality data for analytics generate better revenues. It is the process of combining data from various sources into a single location. Once processed, the data from different sources will count as a unified data store. Businesses use this method for analysis/reporting, where the original data source is not crucial.

Another critical factor that separates data consolidation from other methods is latency. Latency is the time taken to retrieve data from a source and transfer it to a centralized archive. The lower the latency, the better the analysis will be in real-time.


The last data integration methods you’ll find are ETL and ELT. ETL means Extract-Transform-Load, while ELT implies Extract-Load-Transform.

Let’s talk about ETL first. First, the system extracts data from a source and applies a transformation logic. After that, it loads the result into a database or data lake. It is an excellent option for businesses processing large data volumes. In the case of ELT, the system first loads the data (after extracting) and then applies the logic. The advantage is low data latency and power consumption.

How Does Data Integration Help?

  • It builds confidence in your data. You can deliver consistent information for your big data applications and projects.
  • It helps you govern data in real-time. You can use, manage and improve insights to drive results.
  • Its parallel processing capacity reduces the cost and consolidation risk.
  • It automates manual processes to enhance business process execution. It also improves the customer experience.

Final Thoughts

Data integration enables businesses to make data-driven decisions. There are two vital components for its successful execution.

  • Establish a strategic plan.
  • Choose a suitable integration solution.

Most businesses work with two or more data integration methods. They leverage high-performance automated solutions for their existing and incoming data. If you’re a small business, first assess your requirements. You can consult a service provider to help you deploy the appropriate method.