Chen Cuello
AUG 8, 2023
5 min read
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Data integration is a special data management process designed to combine data from disparate sources within an organization. The main goal is to provide users with a unified view of data.

The main aspect of data integration involves making data more accessible and available to users while also being easier to consume and handle by operational systems.

As a robust process, data integration provides accurate, complete, and up-to-date datasets for data analysis, BI, and other business processes and applications. Data integration includes a subcategory of other data processes like replication, ingestion, and transformation. The intent is to deliver standardized data formats to be stored in a targeted data repository (a data warehouse, data lake, or lakehouse).

When done right, data integration can reduce IT costs, free resources, and improve data quality. Additionally, the process makes room for innovation without disturbing the existing applications or data structures.

There are several types of data integration processes conducted by developers and managers using special data integration software and tools. The overall process can involve manual data integration, or it can be completely automated with the use of specialized software to expedite different integration operations.

How Data Integration Works

Data integration is not a one-size-fits-all method of collecting and processing data. However, there are a number of core components characteristic of every data integration process, including various sources of data and a master server. While relatively simple to understand, data integration can be a challenging process to implement. Integration begins with the so-called data ingestion, which, as the name indicates, involves collecting or acquiring data from a data system.

When a customer or client requests a specified type of data, the main server receives the request and sources information from different data systems. That server then takes out the relevant data from all sources and routes the information back to the target in a unified system.

After this information is integrated, it is sent to a transaction processing system (TPS). Then, depending on the data type – structured or unstructured – the integrated information is stored in data warehouses or data lakes. Structured data is usually stored in a data warehouse, while the latter type is in a data lake.

Two key components of data integration are the source and the target. In between them is the process of acquiring and copying or transforming information from the data source from its base format to a format readily available for use to the end user, i.e., the target. The source can be cloud data, core transaction systems, or something else, while the target can be a replica of the source data, a data lake, or a data warehouse.

IT technicians and software developers usually create and use a special data integration tool or software to automate integration and decide for what purpose and format information from data sources is presented to the target.

To sum up, the data integration process involves ingesting (obtaining) the data, processing, copying, cleansing, and/or transforming the information, and routing the relevant data unit to the target.

Importance of Data Integration

Data is terribly important to all modern-day digital (or land-based) enterprises. Even the smallest establishments, like a simple online vendor, require data to conduct the most basic of services. And when large-scale enterprises are in question, data becomes an indispensable asset

As the business grows, so do its databases, to a point where conventional ways of managing data become obsolete and ineffective. This is where a master data management system comes into play. The system must inevitably involve a data integration solution to aid the enterprise in getting a better handle on its information caches and repositories.

Data integration is practiced in any workplace that employs a data management system. The process is particularly useful for business enterprises, NGOs, and educational and scientific institutions handling vast data volumes.

Organizations need fast access to relevant data, and for that, they need to source, structure, and transfer data from different data repositories and sources into a single data batch. This integrated data is then readily available and accessible to use for further processing or to serve a particular purpose.

Any medium- and large-scale organization needs data integration to streamline operations. Enterprises, institutions, consortiums, or any other complex work environment with exhaustive databases that integrate data can improve operational efficacy, provide fast access to relevant data to different departments and users, and enhance data-related operations all around.

Data integration solutions can prove a cost-effective alternative to wide-scale changes to an organization’s distinct data sources. By integrating data across different sectors, enterprises are able to ensure data quality and availability, which will consequently boost the establishment’s handling of complex databases.

Data Integration Challenges

The first step to reaching a solution is understanding that data integration is not without its challenges. For instance, lack of planning is one of the major issues users face when employing a data integration process.

Data operators probably know that their data is as good as the operations it is being used for. Before commencing data integration, it’s a good idea to ask (and answer) the following questions:

  • What data am I integrating?
  • What data formats do I need to merge?
  • How is this data relevant to my business goal?

These (and many more) questions will help businesses understand the sole importance of data interaction, as well as the different types of data integration tools at their disposal. Another challenge businesses face when considering data integration is using manual data integration. Entering data in Excel or Google Sheets requires tedious manual work, which is not the best practice.

Data integration is supposed to ease or minimize the need for manual work. Instead of using manual data integration tools, try an automated data integration tool that gathers (and processes) data in real-time so you have it ready for use when you need it.

Data Integration Best Practices for Businesses

As a robust process, data integration is vital to the end-goal success of businesses. Generally, businesses that need to combine data from different sources to make better-informed decisions, boost operational capacities, and gain a competitive edge will gain the most from data integration.

Some of the most common data integration best practices for businesses include the following:

  • Data governance: Establish clear data governance policies to ensure data is accurate, secure, and compliant with GDPR and/or HIPPA regulations and guidelines.
  • Define clear objectives and requirements: Clarify the goals and objectives of your data integration process as clearly as possible. Pinpoint distinctive data sources, formats, and requirements regarding data quality.
  • Data quality assurance: Implement regular data quality checks and data cleaning processes. This ensures the integrated data stays consistent and accurate.
  • Scalability and performance: Tweak your data integration solution to work entirely in your favor. In other words, the data integration solution you choose should be able to scale as your business grows. You can optimize its performance by working with the right software, hardware, and database technologies.
  • Monitoring and logging: Ensure you employ reliable monitoring and alerting systems that will detect and tend to data integration issues in real time. Keep data integration logs as detailed as possible – it will help with auditing and troubleshooting.

Types of Data Integration

There are several ways of integrating data into cohesive units.


ETL stands for Extract, Transform, and Load, and the process involves precisely that – extracting the data from data systems, transforming it, and finally loading it, usually to a data warehouse. ETL data integration is one of the first integration systems used for about 50 years and is a principal integration process used in data warehousing.


ELT is an almost identical integration system; only the order of data integration is shuffled. ELT stands for Extract, Load, and Transform. So, rather than first extracting, then transforming, and cleaning the data, the ELT process entails extracting and loading unprocessed data to the target. Only after the data is loaded does the transformation process begin.

Batch and Real-Time Integration

In addition to these two, there is also batch and real-time data integration. Batch data integration is collecting data over time and, processing and piling it in a batch, then routing data from the batch in increments.

Real-time integration transforms and transfers data immediately after it is extracted, allowing enterprises to obtain, process, and move data in a split second. Real-time integration involves a process called change data capture (CDC). This is when updates or other changes that occur in the data source are almost instantaneously made to the data warehouse or another target data system.

Data Replication

Data replication is another form of data integration that can be applied to batch or real-time data integration processes. It involves the replication of data and changes to data from a source to the target database. Data replication is typically used as a method for data synchronization and recovery.

Data Virtualization

Data virtualization, as an integration method, is a process of presenting data for viewing purposes. That is, it provides real-time integrated information from multiple distinct sources in a single data set without actually replicating, transforming, or loading the data from its source.

Data Integration Tools and Techniques

In a way, ETL, ELT, data virtualization, and CDC are all techniques for integrating data in different ways. The technique or method used to incorporate data can depend on the type and size of the enterprise.

Manual integration is one common technique and involves a developer or IT specialist manually processing and loading information from separate sources. In this instance, the integration process is not automated but rather manually handled by an expert.

SQL (Structured Query Language) had long been the preferred method of integration before being replaced by more advanced data integration tools that automate the entire process. ETL has also been an enduring technique of integrating data, and many tech companies have created their proprietary tools that can be used for ETL, ELT, CDC, and other data integration processes.

Middleware data integration and application-based integration are also widely practiced strategies for integrating data. Middleware integration is the use of intermediary software used to establish communication between applications or software systems.

On the other hand, application integration (A2A, or application-to-application integration) is a technique of integrating data from multiple applications, making information from multiple sources compatible so the data can be transferred between the sources.

Data Integration Examples and Benefits

Data management and integration are invaluable assets for businesses, particularly when handling big data sets. There are countless practical examples of how a certain data integration process can benefit a particular enterprise or institution

For instance, when you book a room or a flight or purchase a ticket to an event online, the provider of that service uses real-time integration to make updates to relevant databases in real time.

On the other hand, energy and electricity suppliers are one example of how batch data integration is used. The supplier does not calculate the data consumption from day to day but rather collects the relevant data for each day in a batch throughout the month and then analyzes the accumulated data to estimate the consumption at the end of the month.

Today’s businesses, particularly those with a heavy presence online, can benefit most from integrating unorganized data to streamline operations and improve customer service but enterprises have a consistent inflow of data. Without a proper integration process, it can be challenging, if not impossible, to process every piece of information entering the company’s databases and provide meaningful, valuable, and relevant information.

Real-time integration is especially advantageous to medium and large enterprises for purposes like business intelligence, data analytics, and key performance indicator (KPI) assessment.

What Is the Role of Data Integration in Today’s World?

The fast-evolving economies in different industry sectors are heavily reliant on various data structures to consolidate the information that enters their ecosystem and provide a better, more personalized experience for their clientele.

The vast quantities of data businesses deal with daily can be overwhelming, especially without a proper strategy to integrate and present that data in its most valuable form. Data is like the proverbial gold mine, and integration processes are the tools for extracting and harnessing that gold.

Different types of data get piled from day to day in separate data systems. Major enterprises need to manage various types of customer data, data from customer relationship management (CRM) systems, operational data, performance and financial data, and other datasets. Without an integration strategy, this can turn into a frighteningly convoluted process.

But with information integration systems, the enterprise is able to collect and source that information and consolidate it with datasets from different sources.

Data integration can have far-reaching benefits. Some can include reducing the risk of errors and miscommunication to a minimum, streamlining work processes, saving time and money, and even leveraging bulk data to solve previously unsolvable problems

What is a Data Integration Platform?

A data integration platform can simply refer to the software or integration tool used by IT professionals to locate, collect, clean, and transmit data from different datasets. Using an integration platform, developers can create organized and accurate datasets that can serve for analytics or related purposes.

Integration platforms provide an all-encompassing architecture model for extracting, processing, storing, indexing, and, of course, integrating information from disparate datasets. Such platforms are taking over the industry and replacing the more traditional database management systems (DBMS) and usually eliminate the need for coding or manual data handling.

Integration platforms are also more cost- and time-efficient, easier to use, and allow businesses to scale their operation and be on top of the huge quantities of data that are being perpetually stored in various data systems.

What Is the Difference Between Process Integration and Data Integration?

The key difference between process-based integration and data-based integration is the time it takes for the data integration to take effect.

Process-based application integration allows for two or more applications to connect and synchronize operations and unify data in real time.

With data integration, the information is integrated only after the integration processes are done and typically does not involve real-time integration. Data integration is conducted in batches using information from stationary data systems.

Meanwhile, process or application integration occurs in real time and synchronizes and processes the information from at least two applications.

Another difference between application and data integration is the volume of data that is in use. Process integration utilizes small sets of information, allowing it to make fast-track data changes as they happen, compared to the bulk of data from separate systems handled in batches by data integration.

What Is Enterprise Data Integration?

Enterprise data integration is the integration of data across different sources in one or more businesses. This type of data integration is characteristic of large organizations, conglomerates, business associations, and consortiums that deal with large sets of data stored from data silos or other master data systems.

One common instance of enterprise integration is when two companies are partnered or when one company acquires or merges with the other. In these cases, both enterprises need to consolidate and synchronize databases across various business departments.

Enterprise data integration is a fundamental element of managing data in any large-scale organization that deals with an overwhelming amount of information. This particularly goes for entities with voluminous patches of siloed, unstructured, scattered, and unused data in different formats.

The process gives the enterprise a bird’s-eye view across all databases and helps it integrate data in a centralized interface. As a result, the company can bolster productivity and establish a data integration architecture for all existing and incoming data.

Data Integration Use Cases

When it comes to data integration, every organization’s needs are different and depend on different factors. These factors include the industry they’re part of, the products/services they offer, the type of customers they cater to, their data workflows, and more. Below are a couple of the most popular data integration use cases across industries.

Migrating Data into a Data Warehouse

Businesses create repositories for big data with the intent of combining and processing it to gain data-based business insights. Before businesses can run reports, employ data analysis, or develop insights, they need to collect all relevant data from different data sources. Additionally, once data is collected, it needs to become properly formatted for analysis. This is where data integration comes in.

Syncing Records to Multiple Systems

Businesses that operate using different data systems need to have a unified and cohesive view of data to be able to use the insights properly. Presumably, if two retailers were to merge, both parties would have their own independent data systems that store more or less the same data. The businesses would have to merge and synchronize data across their system to maximize the existing data from the independent data systems. This will help remove duplicates and filter irrelevant or outdated data, etc.

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