What Is Data Integration And Why Do You Need It?

On average, a business uses 928 apps* to store enterprise data along with numerous other on-premise systems. To manage data coming in from different vendors, stored in disparate sources and different formats, enterprises need a robust data management solution. 

This is where data integration comes into the picture. It enables enterprises to make sense of their data assets, dispersed throughout different business units, by consolidating it in a centralized location. 

This begs the question, what is data integration, and how can it help your business grow?

What is Data Integration? 

Data integration is a process that consolidates data from heterogeneous sources to create a 360-degree view. It becomes more significant in the event of mergers and acquisitions, vendor or customer onboarding, and data migration where information assets from disparate systems have to be brought together to generate insights and improve business processes.

Source: ScienceDirect

Benefits of Data Integration

Here’s how data integration helps businesses generate value from their data:

1. Enhanced Data Reliability

For data-driven businesses today, trustworthy and reliable data is crucial for decision-making, reporting, and business intelligence. Data integration ensures that error-free, consistent, and unique data in fed into storage systems so business users can make accurate decisions backed up by data.

2. Reduced Data Silos

Every department uses a different app or system to store data. Generating insights from these versatile systems is a lot of hassle, as every field or attribute cannot be found in one system. At the same time, the presence of duplicates can skew results. Using a data integration tool, data can be consolidated and stored in a unified location that can break down data silos, reduce manual processes, and improve business process execution.

3. Improved Collaboration

Improved collaboration within internal and external resources is essential for any business. But that’s only possible if you can find a solution that connects data residing within and beyond the enterprise. Data integration not only connects internal and external data assets but also ensures all team members can use the data they need to fuel business processes.

4. Faster Data Access

Using a data integration tool, you can effortlessly access data whenever required without depending on complex querying or manually seeking data across numerous systems. This guarantees faster time-to- insights.

5. Superior Analytics 

As data integration makes your enterprise information accessible at a single place, it allows you to analyze the available data easily. You can enjoy access to integrated data in real and use it to get valuable insights, find new opportunities, and recognize potential bottlenecks before they even occur. 

Common Data Integration Approaches 

Now that we’ve read the benefits of data integration, let’s see how it can be achieved. There are two most common approaches to integration:


ETL is an acronym for extraction, transformation, and loading. It allows you to fetch data from one repository and place it into the destination system after it is transformed to fit the end-system’s requirements.

ETL involves three steps:

  • Extraction – It pulls out data from a source. In this step, the data is read and collected, usually from several and different kinds of sources, like on-premise and cloud databases, business applications, file systems, etc.
  • Transformation – The data extracted is then transformed into a format that is compatible with another repository. In this step, data is manipulated using rules or lookup tables or by concatenating one dataset with another.
  • Loading – The last step involves writing or loading the transformed data into the target repository.

ETL is a key component of the data integration toolkit that consolidates data at one site to obtain valuable business insights. 

Image source: mssqltips


ELT (extract, load, and transform) is a process that moves raw data from a source to a destination and then transforms the data for downstream applications. Unlike ETL, where data transformation happens on an intermediary server before being loaded into the destination, ELT loads raw data directly into the destination and transforms it over there. This makes ELT more helpful in managing high-volume data, making it more suitable for business intelligence and big data analytics.

In comparison with the ETL process, ELT significantly decreases the load time. This makes it a more resource-efficient procedure as it takes advantage of the processing capability developed into a database system, reducing the time consumed in data transmission.

Image source: Jet Software

Wrap Up

With the growing volume of data gathered through a wide range of channels, companies need a solution that can help them extract value from it to fuel business procedures. Using data efficiently can empower you to make smart business decisions, drive growth, and increase productivity. If your business wishes the right type of data to reinforce cutting-edge analytics procedures or to generate multi-dimensional views of consumers, you must pursue data integration as a strategic function that is in line with your business goals.