Data Integration: What Is It? Meaning, Cases, and Frequently Asked Questions

In today’s world, companies possess an abundance of information about their products, customers, employees and operations, which is often stored in specialised software programs and databases. However, merging and scrutinising these datasets has the potential to offer priceless insights into a company’s workflows and procedures.

To tackle various obstacles, data integration has become an increasingly prevalent solution. It involves collecting data from different sources to store it together in a unified repository, which Business Analysts and Business Intelligence (BI) professionals can access and analyse.

This post will address the concept of data integration and various strategies to achieve it.

What Are the Benefits of Data Integration for Businesses, and Why Is It So Crucial?

Although the concentration of all crucial data and information is a significant benefit, it is the additional benefits that result from data integration that persuade business owners to contemplate it.

  • Data integration is a pivotal tool for businesses to ensure efficient data sharing across departments. In the absence of data integration, data is dispersed among various systems with differing interfaces, making it challenging to access and control. By streamlining this process, data integration enables organisations to maximise the value of the data they gather.
  • Centralising all data in a single repository simplifies access to the information necessary for each team to collaborate and share resources, which eventually results in improved products and inventive ideas.
  • Data integration platforms enable the consistent maintenance of data, recognition and elimination of duplicates, and resolution of any related concerns, which is considerably more challenging to attain through manual data examination across several systems.
  • Data integration enables faster and more effective decision-making by granting decision-makers access to reliable and current information.

Different Approaches to Integrate Data

To devise a data integration strategy, it is crucial to contemplate the primary need for data consolidation. Devoid of this understanding, it is unfeasible to determine the most suitable way to establish a data pipeline, thus emphasising the importance of recognising this requirement.

Now, let us examine few of the most common approaches to integrate data:

“Extract, Transform, Load” (ETL)

Information is assembled from its source, modified in a staging area, and eventually loaded into the target system via an ETL pipeline (for example, a database or data warehouse). This approach is particularly useful for smaller datasets that necessitate extensive processing.

Extract, Load, Transform (ELT)

In this method, data is first loaded into the targeted system and then transformed, comparable to the Extract, Transform, and Load (ETL) process. As loading data is frequently much faster than modifying it, this approach is usually utilised when dealing with vast datasets that need to be made accessible quickly.

Real-time Data Transfer

The method of transmitting data at all times from connected applications to a centralised system in real-time is referred to as data streaming. The data is analysed, processed, and made available for review after it has been received.

Virtual Data Integration

Data Virtualisation is akin to Streaming because it involves accessing data held in a centralised system. Nevertheless, unlike Streaming, no data is actually transmitted; instead, it is quickly retrieved from its source and processed as needed.

API-Driven Approach refers to the communication between applications through a standard

This setup facilitates the exchange of communication and data between applications using standardised interfaces. This method is most effective in systems with restricted users and software, as a rise in either can swiftly lead to the overload of the system.

Data Integration Examples

Given the various forms of data integration, what are some common examples? Let’s explore some typical use cases:

  • Collaboration between departments:

    Such connections can be advantageous for employees within an organisation. It has been demonstrated that a data integration system can not only ensure the accuracy and timeliness of data, but also encourage inter-departmental collaboration and foster innovation.
  • Improved Customer Satisfaction:

    With access to a centralised database containing all pertinent client information, both sales and support departments can perform their jobs more efficiently.
  • Data Synthesis:

    When it is necessary to examine multiple data points in order to make informed business decisions, a data integration system may be the solution to the demand for a centralised system that can collect, maintain, and update information in a timely manner.


What is the process of consolidating information from multiple sources into a unified entity?

Data Integration refers to the consolidation of various data and information sources into a unified database that can be centrally managed.

What is the total number of different system integration methods?

There are four primary approaches to system integration:

  1. API-based connectivity that uses links spanning data locations to transfer data.
  2. Webhooks, which are event-driven HTTP callbacks that push out updates.
  3. Integration Services Components (ISC) that use a central server to bind together all software integration tools.
  4. Automated orchestration, which synchronises data integration operations to ensure that all data stays current.

In your forecast, what will be the top 10 data integration tools by 2023?

Currently, there are various options available, but some of the most potent data integration platforms are:

  • Integration of TIBCO’s Cloud Platform
  • Matillion
  • Microsoft’s SQL Server Integration Services (SSIS)
  • GoldenGate from Oracle
  • Task Factory provided by SolarWinds
  • Central Office by Astera
  • Boomi
  • Data Intelligence offered by SAP
  • Replicate provided by Qlik

What is the meaning of SAP certified application associate?

To obtain SAP Application Associate certification, an individual needs to take and pass an exam that measures their foundational abilities needed to manage a SAP system. The evaluation concentrates on data integration utilizing SAP Data Services.

Which metrics will be used to assess the quality of integrated data and the level of standardization in business procedures?

Evaluating data integration and process standardization requires ensuring data quality. The following metrics can assist in achieving this goal:

  • Error Percentage
  • Count of Empty Values
  • The Issue with Unopened Emails

Considering the overall expense of data storage in the long run is crucial. Metrics can gauge the cost-effectiveness of preserving, modifying, or eliminating infrequently used and costly data. This assessment is valuable to ensure data quality and efficient resource utilization.


We trust that this summary of the principles, applications, and conventional practices of data integration has been helpful. Although it may be difficult to comprehend at first, this approach is crucial for amalgamating numerous businesses into a single entity.

At Works, we offer proficient staff as per your budget and company culture preferences. Our team of data engineers can help build and migrate to a data integration system that consolidates all your business data, as well as provide expertise in other developmental domains. Contact us to explore how we can be of assistance.

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