Data integration brings together data from different sources to produce useful business information or initiate new business processes. It helps to transfer and sync different data types and formats between systems and applications. Data integration is not a one-and-done event, but a continuous process that keeps evolving as business requirements, technologies and frameworks change.

Data has become increasingly distributed, and the sources are not limited to mainframe and applications, but extend beyond the enterprise IT landscape. You might need to connect with business partner data or curate data from social media sites or third-party data services through APIs. As organizations generate more and more data, it provides an opportunity for better business insights. Your data integration strategy determines how much value you can get out of your data.

The benefits of data integration

Companies are constantly challenged by the four Vs of data: velocity, volume, variety and veracity. A data integration platform helps to standardize, automate and scale data connectivity as these variables grow or change. Team productivity improves when you automate data workflows and reuse frameworks and templates to accommodate new data type and use cases.

A common data integration use case is consolidating data from multiple sources to a data warehouse or data lake. The data is standardized and its quality is ensured before it’s made available to the consuming applications, systems and services.

Data integration enables a holistic view of business. The opportunity to combine the right sets of data irrespective of the sources empowers stakeholders to make fast business decisions while keeping multiple perspectives in mind. As organizations become more data-driven, analytics take precedence. Success of data science and advanced analytics projects depends on how much employees can trust and access the data they need. Timely access to valuable business insights can give a company the competitive advantage they need to stay ahead of the curve.

With the right data integration platform, it’s easier to govern the data flow. Data integration solutions ensure a secure and easy data flow between two different systems. The visibility of end-to-end data lineage helps ensure data governance and maintain compliance according to the corporate policies.

Data integration is core to any company’s modernization journey. The success of digital transformation initiatives depends on how well connected the IT landscape is and how accessible the data is. And, now with architectural patterns changing from monolith to service-oriented architecture to microservices, data integration among these various cross-functional components is crucial.

With a heterogeneous IT environment, the data resides in siloed and fragmented locations –be it in legacy system on-prem, SaaS solution or IoT (internet of things). A data integration platform serves as a backbone in this fluid, ever-changing and ever-growing environment.

Three steps to data integration: Extract, transform and load (ETL or ELT)

There are three basic operational steps of data integration: Extract, transform and load. But, it starts with gathering business requirements and locating data sources. Data catalogs and data publication subscription models can help you figure out the right data. Once the sources are identified, you need to ingest or extract the data. Typically, data is then enriched to match the requirements of the target location. Transformation can be done after loading the data in the target application as well.

Data integration patterns: ETL and ELT

Two common architectural patterns of data integration are ETL and ELT.

ETL (extract, transform and load) is the most common pattern and has been practiced for a while now. However, ELT (extract, load and transform) is relatively new and aligns with single cloud ecosystem architecture.

In a heterogenous IT landscape that spans multiple clouds and on-premises environments with several data sources and targets, it might make sense to process data locally with ETL and then send the transformed data to downstream applications and datastores.

ELT, on the other hand, is efficient if you have your data source and target in the same ecosystem. For example, for transformations within a single cloud data warehouse, ELT can be effective from both a cost and performance standpoint.

Another way of categorizing data integration is based on frequency and latency of dataflow.

Batch integration is an efficient way to process data, allowing you to scheduling data integration at regular intervals. Batch integration helps optimize resource allocation and improve performance for high-volume data transformation and transfer.

Real-time integration is triggered every time new data is available, bringing the latency to almost zero. As businesses switch to an always-on mode, real-time integration helps serve customers better and faster.

Cloud data integration is part of data integration

With cloud coming into the picture, data integration solutions can be deployed in several different ways where the IT team can offload the IT infrastructure maintenance part in steps.

Cloud data integration gives you the benefit of the cloud:

  • Scalability in connecting data across multiple cloud environments and on-premises
  • Agility and time to market improves as companies cut down on time to provision and deprovision IT infrastructure
  • Flexibility of consumption-based pricing

Serverless cloud integration takes cloud data integration a step further. IT teams do not have to manage any servers, virtual machines or containers. They don’t have to pay anything when the application sits idle. Serverless integration also enables auto-tuning and auto-scaling for effortless data pipeline processing.

Data integration and application Integration

The IT integration landscape involves application and API integration along with data integration. Application integration is preferred when business processes are automated and operational data is shared between applications in real time. Data integration is mostly used for consolidating data for analytical purposes. Generally, data integration is taken into consideration when normalization, transformation and reusability of data sets is required.

Data integration and API Integration

Application programming interface (API) acts as a window to enable interaction and sharing of data among applications, systems or services. With growing cloud and web-based products and applications, API integration has gained momentum. There is greater control over APIs when it comes to security, monitoring and limiting access. You can custom-build internal or public-facing APIs, open up the data for innovation and monetize from it. Companies now opt for data integration using API technology.

Data integration in various industries

Certain initiatives are common across industries like digital transformation analytics and business intelligence projects. But, there are other aspects that are unique to an industry or a segment. Data integration plays a big role how an industry innovates and addresses its data-driven use cases. There are data integration frameworks specifically tailored for an industry. Let’s take a quick look at data integration use cases across various industries.

Data integration in healthcare

As the healthcare system embarks on the digital transformation journey, there is an increased focus on data privacy and protection. The right data integration strategy will ensure that today’s medical system provides valuable insights about each patient while keeping data safe and confidential.

With patient data at healthcare staff fingertips, it is possible for the medical systems to take a predictive approach to healthcare rather than rely on reactive methods. Data integration plays a critical role in combining real-time patient data from IoT or mobile apps with historical medical data to provide personalized care and mitigate risk if any.

Data integration in finance

Fighting fraud, ensuring compliance, and running complex analytics are some of the priorities that financial services institutions need to address when considering data integration solutions. Data governance, industry regulations and privacy issues have to be handled before the data is made available for consumption. Financial services companies are slowly and cautiously shifting to cloud with tried-and-tested cloud data integration solutions.

Data integration in the public sector

Government organizations are modernizing their data infrastructure to achieve mission-critical outcomes while complying with regulatory mandates. Integrating trusted data would help agencies gain real-time insights and improve decision-making. A modern data integration platform paves the way for public sector to transform and stay more connected to its people.

Data integration in manufacturing

Manufacturing companies are going through a series of automation fueled by the intelligence derived from the data they generate. Sensor data integration helps with real- time monitoring of the equipment in the plants, boosting performance and ensuring data quality.

Automation and integration with the whole supplier ecosystem enable transparent transactions. Inventory and warehouse management gets easier with data integration. The orchestration of orders and deliveries help to optimize resource allocation and remove inefficiencies.

Data integration in retail

Customer experience is a big brand differentiator in the retail industry. Not only are traditional retailers setting up online stores, they are also going above and beyond to provide a seamless digital experience.

With the right data integration framework, retailers can get a 360 view of their customers using data from their online behavior, social media interactions, purchase or preference history and many other sources.

Data integration customer success stories

Japan for UNHCR

Japan for UNHCR is a nonprofit organization determined to raise awareness, help the world’s refugees, and ease the plight of displaced people. By moving from manually intensive data flows to the Informatica data integration platform, they Increased developer productivity, making new fundraising tools available in weeks instead of months.


Anaplan connects people, data, and plans to enable real-time planning and decision-making in rapidly changing business environments. Intelligent Data Management Cloud extracts data from more than 25 data sources and imports it into a Google Cloud data warehouse, delivering the right data, to the right people, at the right time, enabling better, faster decisions. In just four months, the teams used Informatica Cloud Data Integration to build 90 complex ETL jobs spanning 17 source systems.

The Department of Culture and Tourism - Abu Dhabi

The Department of Culture and Tourism - Abu Dhabi regulates, develops and promotes the emirate of Abu Dhabi as an extraordinary global destination. Their goal was to build a cloud data warehouse with data ingestion from 100s of integration points. Within the first two months they achieved 760 integration processes using a unified integration platform to automate application and data integration coupled with business partner process automation.

Get started with data integration tools

Informatica’s cloud data integration solution provides critical capabilities key to centralizing data in your data cloud data warehouse and cloud data lake:

  • Rapid data ingestion and integration with an intuitive visual development environment with Informatica Cloud Mass Ingestion
  • Pre-built cloud-native connectivity to virtually any type of enterprise data, whether multi-cloud or on-premises with Informatica Connectors
  • Critical optimization capabilities such as pushdown optimization for efficient data processing
  • Serverless-based Spark processing for scalability and capacity on demand with Informatica Cloud Data Integration-Elastic
  • Intelligent data discovery, automated parsing of complex files, and AI-powered transformation recommendations

Find out more about cloud data integration as part of our industry-leading, metadata-driven cloud lakehouse data management solution, which includes metadata management and data quality in a cloud-native cloud data management platform.

You can also download the Gartner report, Critical Capabilities for Data Integration Tools, to discover key criteria for comparing various data integration vendors on functionality and performance.

If you are new to data integration or simply want to refresh the foundational concepts then join our "Back to Basics – Data Integration webinar series".