What is Data Integration?

Businesses generate a lot of data. And it’s become increasingly distributed. Data sources are no longer limited to mainframe and applications but extend beyond the enterprise IT landscape. Data integration brings together data of any type, from one or more sources, into a single destination; often used to produce useful business information or to enable new business processes.

How does data integration work?

As a process, it’s comprised of the many different architectural techniques, practices, and solutions companies use to achieve their data goals. Since every business has different data goals, there’s no one size fits all method. Still, data integration tools and solutions are usually comprised of a few standard building blocks: users who need data, a primary server, and a disparate or interconnected system of external and internal data sources plugged into the primary server. Users request data from the main server. The main server then intakes, aggregates, enhances, and combines the requested data from the various data sources into a unified set of data that’s delivered to the user.

Why is data integration important?

Overall, data integration helps to transfer and sync data from different systems, types and formats between systems and applications. It’s not a one-and-done event, but a continuous process that keeps evolving as business requirements, technologies, and frameworks change. 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, and which data integration type will work best for your use cases and initiatives.

Data Integration Types

One way of categorizing data integration is based on frequency and latency of dataflow. There are two main ways to do this: batch data integration and real-time data integration. Your data integration strategy should help guide you to selecting the best way to integrate your data. Regardless of which data integration system you use, cost-effectiveness and speed will play into your decision.

Batch data integration

Just as the name implies, it involves processing data in batches. A group of data are collected and stored until a specified amount is gathered, then that data is processed all at once as a batch. Batch data integration is the traditional way to integrate data and for years was the standard approach; older technologies had an easier time processing data in batches as opposed to in real-time. Batches helped save bandwidth by cutting the number of input & output events. Batch data integration is still widely used today when companies don’t have a need to gather and analyze data in real-time.

Batching is an efficient way to process data. It lets you schedule data integration at regular intervals, optimize resource allocation, and improve performance for high-volume data transformation and transfer.

Real-time data integration

A newer way to integrate data, real-time integration is triggered every time new data is available, bringing the latency to almost zero.

Companies are relying on real-time data integration and data processing more than ever to capitalize on up-to-date insights, analytics, and to help serve customers better and faster. For example, say you’re a global car rental company like Avis Budget Group. You want to cut costs, drive cost efficiencies, and increase revenue by connecting your fleet of over 650,000 vehicles worldwide with a complete global view of your business. You’d need to be able to ingest and integrate data from thousands of vehicles in real-time and quickly onboard new Internet of Things (IoT) devices, formats, and data fields as the technology shifts and changes. Many companies like Avis rely on streaming analytics platforms to achieve real-time data integration and data processing.

See “Streaming Analytics: What It Is and How it Benefits Your Business” for an in-depth overview.

Data Integration Patterns

Two common architectural patterns of integration are ETL and ELT.

ETL (extract, transform, load)

ETL is the most common pattern and has been practiced for a while now. However, relatively new patterns are gaining momentum as pushdown techniques get smarter.

In a heterogenous IT landscape spanning 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 (extract, load, transform)

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.

What are the basic characteristics of a data integration tool?

While data integration tools have evolved over time, the basic feature set remains the same. All quality data integration tools should be able to:

• Write data to target systems:

This feature copies data from the source application, whether located in the cloud or on-premises, then saves the transformed version of the data into the target applications, systems, and services. The transformation step is key as without it, data integration isn’t happening.

• Access data from a mix of sources:

Data integration is designed to enable both the transfer of data as well as the ability to combine or collate data from different sources and present it in a standardized format to any target applications or users.

• Interact with sources and targets:

Data integration is also a method of communication between source and target systems. There are many ways to set up communication methods: 1-to-1 communication, one source to one target, or 1-to-many and many-to-1 channels among various data sources and targets. One such way to connect data is an integration hub where sources publish data to the hub and the targets, or users, subscribe to that data as and when they need it.

• Transform data:

The main component of data integration is the ability to transform data for consumption by a target application. That’s what differentiates data integration from data transfer or data ingestion.

• Design the dataflow:

A basic data integration tool feature is the ability to create a data pipeline using a combination of targets, sources, and transformations. One can set up the process regardless of whether it’s ETL or ELT and automate it.

• Support efficient operations:

Data integration tools are designed to make interconnective systems efficient by removing the manual effort of coding. With automation and monitoring, the management of data pipelines becomes easy, timely and less error prone.

Data integration and application integration

On the surface, data integration and app integration may look the same. They’re both cloud-based and give you all the benefits of cloud computing. They both take different data sources, translate, and convert them into a new data set. But the big difference is when and how they’re used.

Why use application integration?

Application integration is preferred when business processes are automated and operational data is shared between applications in real time.

Why use data integration?

Data integration is mostly used for consolidating data for analytical purposes. Generally, data integration is taken into consideration when the normalization, transformation, and reusability of data sets is required.

Check out “Application Integration: An Intelligent Way to Modernize your Data & Applications” for a deeper look.

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 private or public APIs; open your data for innovation and monetization opportunities. Companies now opt for data integration using API technology.

The Benefits of Data Integration

Companies are constantly challenged by the four Vs of big data: velocity, volume, variety, and veracity. A data integration platform helps to standardize, automate, and scale data connectivity as these variables grow or change.

Improve productivity

Team productivity improves when you automate data workflows and reuse frameworks and templates to accommodate new data types and use cases.

Improve data quality

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

Gain a holistic business view

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 in decision making. The 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.

Better data flows

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 systems. The visibility of end-to-end data lineage helps ensure data governance and maintain compliance according to the corporate policies.

More cross-functional data

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, data resides in siloed and fragmented locations. These could be a legacy on-premises system, SaaS solution, or IoT device. A data integration platform serves as a backbone in this fluid, ever-changing, and ever-growing environment.

Cloud data integration

Serverless cloud integration takes 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 an application sits idle. With cloud in the picture, solutions can be deployed in several different ways where the IT team can offload the IT infrastructure maintenance part in steps. Serverless integration also enables auto-tuning and auto-scaling for effortless data pipeline processing.

Cloud data integration gives you:

• 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

Uncover key criteria for comparing various data integration vendors: Download your complimentary copy of the Gartner^® Critical Capabilities for Data Integration Tools Gartner report.

Must have advanced data integration features

Finding the ideal data integration tool for your business goals depends on identifying which product features are must-have, should-have, and nice-to-have. Here are some advanced data integration tool features you should look for:

Large-scale data integration

The data available to companies is growing larger and more complex every day. From data volume to data formats and sources, data load parallelization, third-party app invocations, and more, your tools must be able to ingest and process this data quickly and accurately. Look for solutions that give you easy scalability and high-performance processing.

Pushdown optimization

A must-have feature to look for in a data integration tool is pushdown optimization for the ELT operations. This feature can help you save costs by using data warehouse and ecosystem resources more effectively.

Job scheduling and automation

These two features are a lifesaver! Automating data integration tasks gives you a faster and easier way to extract data for insights and analytics. Job scheduling lets you easily schedule any of your data integration tasks.

Data pipeline error handling

Sometimes data pipelines break. This can be for a variety of reasons ranging from conditional logic errors to corrupt data. Find a tool that ensures data availability, consistency, and accuracy by easily and efficiently managing errors.

Cost optimization

No matter what data integration solution you buy, cost is going to be a critical factor. Look for tools that can leverage artificial intelligence (AI) and machine learning (ML) to recommend the most cost-effective option for your workload.

Industry Examples of Data Integration

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

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 systems provide valuable insights about each patient while keeping data safe and confidential.

With patient data at healthcare staff’s 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.

Finance

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

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 the people they serve.

Manufacturing

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

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

Retail

Customer experience is a big brand differentiator in the retail industry. Not only are traditional retailers setting up online stores, but they’re 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, preferences, purchase history, or other data sources.

Real-Life Data Integration Use Cases

Boosts productivity and saves time

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 time-intensive manual data flows to the Informatica data integration platform, they Increased developer productivity, making new fundraising tools available in weeks instead of months.

Aligns systems and improves collaboration

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, their teams used Informatica Cloud Data Integration to build 90 complex ETL jobs spanning 17 source systems.

Automates processes and unifies different platforms

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 hundreds of integration points. Within the first two months they achieved 760 integration processes using a unified integration platform to automate application & data integration coupled with business partner process automation.

Data Integration Resources

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

Get Started with Data Integration Tools

Cloud data integration solutions by Informatica provide the critical capabilities key to centralizing data in your 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 Spark-based 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 data management platform.