What is Data Integration? Definition, Benefits, and How It Works

What is data integration?

Data integration is the process of bringing data together from multiple sources and creating one consistent view. It’s not just about moving data from one place to another — it’s about making sure that information is standardized, accurate, and ready to use. Done right, integration eliminates silos, connects business systems, and gives teams reliable data for analysis, reporting, and decision-making.

Key elements of data integration include:

• Data sources: Databases, cloud applications, spreadsheets, and other systems where raw data lives.
• Data pipelines: Automated processes that move and sync information between systems.
• Data transformation: Cleaning and standardizing data so it’s usable across the business.
• Data storage: Central repositories like warehouses or lakes that keep integrated data in one place.
• Unified view: A single, consistent dataset that supports everything from dashboards to machine learning.

Why is data integration important?

The average organization has data all over the place. Data may be stored in an on-premise solution or an outdated proprietary system that was created many years ago. There’s also data in spreadsheets. There’s data in cloud systems. There’s data in the platforms and applications that employees use each day to do their jobs. The challenge of data integration is getting that data into one place, making it work together, and using it to gain new insights, solve problems, and deliver better experiences.

When done well, integration helps you:

• Eliminate silos so every department can work from the same data.
• Improve accuracy by reducing errors and inconsistencies.
• Save time by cutting down manual reporting and reconciliation.
• Speed up insights so teams can react quickly to market changes.
• Power better analytics and business intelligence.
• Lay the groundwork for innovation with a complete, trusted data foundation.

Access data more efficiently

‍Let’s say you are a product manager. You contact your analytics team and ask for information about how many customers are licensed for a particular product and logged in over the last 10 days. Unless your organization has invested in data integration, chances are your analytics team will respond like this: “We can get that information, but it will take some time. We need to pull numbers from several databases and our CRM (customer relationship management) system to create a report. We’ll get it to you in a couple of weeks.” But you need that information now, not several weeks from now.

Data integration makes it possible to deliver valuable data insights and analysis more quickly and easily than ever before. It saves time by eliminating the need to manually gather data and assemble reports. Employees across organizations can access information without navigating through other departments, and IT and analytics teams can deliver data securely across all lines of business. This improves collaboration and builds unity between teams.

Reduce the risk of human error

‍When individuals manually gather data, errors are inevitable. Analytics teams must know every possible location where data may be stored for a given project or data set and have all the systems and software in place to gather that data successfully. If they miss even one source, the data set will be incomplete. And, as new data is gathered and sources are added, manually created reports have to be redone periodically to remain current.

Data integration synchronizes information in real time so reports are always accurate and up to date.

Improve data quality

‍Over time, data integration efforts improve the value of an organization’s data. By pulling data into a centralized system, it is easier to identify quality issues or gaps in data sets. As businesses implement changes based on these insights, new data is more accurate and builds a solid foundation for analysis.

How Does Data Integration Work?

Data integration follows a repeatable process to pull information together and make it useful.

1. Identify sources: Databases, spreadsheets, cloud apps, and APIs that hold the data.
2. Build pipelines: Automated workflows that extract and move the data.
3. Transform: Clean and standardize the data to eliminate duplicates, errors, or mismatches.
4. Store: Load it into a central place such as a warehouse or lake.
5. Share: Provide a unified view so teams can access and analyze information.

In practice, organizations often add steps like mapping, validation, and governance to make sure the data is accurate, compliant, and secure.

Common Data Integration Models

Different models handle the process in different ways:

• ETL (Extract, Transform, Load): Data is cleaned and transformed before it enters the target system. Good for structured, batch jobs.
• ELT (Extract, Load, Transform): Data is loaded first, then transformed inside the warehouse or lake. Useful for cloud-scale environments.
• Real-time streaming: Moves data as it’s created, enabling immediate insights for fraud detection, monitoring, and other fast-moving needs.
• Change Data Capture (CDC): Only moves changes (like updates or deletions) instead of entire datasets.
• Data virtualization: Creates a virtual view across multiple systems without physically moving data.
• API integration: Connects applications directly so they can share and sync information.

Key Benefits of Data Integration

Effective data integration delivers a wide range of benefits that significantly enhance business performance, efficiency, and agility. Here’s how:

Improved decision-making

By creating a unified, comprehensive view of your data, integration enables faster, more informed, and strategic decision-making. Leaders can rely on accurate, consolidated information rather than piecing together fragmented data from multiple sources.

Enhanced business intelligence (BI)

Integrated data serves as the backbone for BI tools, powering dashboards, reports, and advanced analytics. This leads to clearer, more actionable insights that guide key business strategies and help identify growth opportunities.

Increased efficiency

Automating the process of data consolidation eliminates time-consuming manual tasks and minimizes the risk of errors. Teams can focus on higher-value work, improving overall productivity.

Faster time to insight

With integrated data readily accessible, businesses can analyze and act on information in real time. This accelerates response times to market changes, customer demands, and operational challenges.

Support for innovation

Access to high-quality, comprehensive data empowers teams to develop new products, services, and processes. Integration provides a foundation for creativity and experimentation, fostering innovation across the organization.

Eliminating silos

Data integration breaks down barriers between departments and systems, creating a seamless flow of information. This encourages collaboration, improves transparency, and ensures that all teams work from the same data set.

Common data integration challenges and solutions.

Growing data volumes

‍Modern systems are producing more data than ever before, and many organizations are struggling to scale with the volume. When you collect data, you have to have a place to put it. Continually adding physical infrastructure isn't practical or cost-effective. Cloud-based data warehouses are one solution that offers affordability and scalability.

Integrating new types of data

‍New data types are being created in every sector. Just take a look at the growth in the last decade of IoT devices. With this in mind, organizations can design a data integration strategy that is flexible enough to integrate emerging data types with ease.

Gathering data is time consuming

‍Without the right technology, integrating data from disparate sources into a central format and location is time consuming and can drain an organization's resources. With a data integration solution that is built with native connections to popular data sources and the ability to support cloud data warehouses, it is quick and simple to create data pipelines. No more developers writing custom code and diverting resources.

Data isn't optimized for analytics

‍Most of the data organizations collect isn't optimized initially for quick analytics. In order to use the data to get any real insights, it has to be transformed — cleaned and standardized — into formats that are easier to use and interpret when teams create analytics reports. The right data integration tool simplifies this process and makes sure that all data is useful data.

Modern data integration approaches and definitions.

Data fabric

‍A data fabric is an environment made up of a unified architecture and all the services and technologies that run on that architecture. It helps organizations manage their data more effectively by maximizing the value of their data and speeding up the process of digital transformation by integrating data management across cloud and on premises systems.

Data virtualization

‍Data virtualization connects and combines data to give organizations a holistic view of information. Users can access data through reports, portals, dashboards, and mobile and web applications. It makes it simple for stakeholders to access data sets with high speeds at lower costs than many traditional data integration processes.

Federated connections vs. native integration

‍Federated connections and native integrations are two approaches to retrieving and using data. Federated connections means that a tool like Domo connects to cloud data warehouses via federated queries, which means organizations can leave their data in their current warehouse and the BI tool can retrieve it to use in visualizations. Native integration means that a BI tool can do just that, integrate natively, within a cloud data warehouse to optimize BI.

iPaaS

‍The number of organizations adopting an integration platform as a service (iPaaS) is growing. An iPaaS is a platform that standardizes how an organization integrates applications. In doing so, an iPaaS makes it easier for businesses to automate their processes and share data across applications.

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How different industries use data integration.

Business

‍Modern businesses have data coming from many sources and in many different formats:

• Social media and internet ads
• User analytics from websites and mobile apps
• Customer service databases
• Accounting and financial applications
• Sales databases
• Marketing leads
• Content management systems

Data integration helps businesses manage this data and get a complete view of everything from customers to manufacturing and supply chain operations to regulatory compliance efforts. This data informs KPIs and can even help decision makers analyze financial risks. It also improves collaboration with organizations outside of the corporation like suppliers and governmental oversight agencies.

Healthcare

‍Data is absolutely critical in the healthcare industry. With correct data, healthcare professionals can better care for patients, diagnose medical conditions, and advance medical research. Organizing patient data from different medical records and systems into a unified view ensures no information is lost and that the information a doctor is seeing is up to date. Effective data integration also improves claims processing for medical insurers by updating patient records and contact information in real time.

Government

‍From the smallest municipal government to the federal level, departments and agencies handle massive volumes of critical data. Citizen information, budgets, research and statistics, infrastructure, and public health all come into play. But the government sector is notoriously siloed, with individual departments or agencies using their own software and data systems. Plus, the average government employee isn't a data analyst, and without the proper tools wouldn't even know where to start to find the data sets they need. This creates a perfect storm for unused, valuable data.

Data integration between areas of government breaks down silos for more effective interdepartmental communication and productivity. It reduces costs by accelerating data processes, and with the right system, even the most non-technical government employee can easily access and share data.

Data Integration Tools

Today’s tools make integration simpler and more reliable:

• ETL/ELT platforms for structured pipelines.
• Streaming and CDC tools for real-time sync.
• Middleware and service buses for app-to-app connectivity.
• Virtualization and iPaaS for fast, flexible access.
• Data quality and governance solutions to keep data compliant and accurate.
• Master data management (MDM) for consistent customer and product records.
• API management platforms for secure app integration.

How will data integration evolve in the future?

As more people work remotely, the need to consolidate data and easily access it will increase. More organizations will invest in cloud data warehouses, such as Snowflake, Redshift, and BigQuery, as part of their digital transformation strategy. With that investment, the need for seamless and user-friendly data integration will increase as well.

Data integration systems will adapt to interact with these cloud data warehouses in more effortless ways. Domo's multi-cloud data fabric is the perfect example. This innovation allows Domo to integrate more tightly with data warehouses and augment what they do by connecting more sources and outputting data from across multiple cloud platforms into a single interface. It will make it easier for customers to achieve modern business intelligence for all and accelerate the speed of business transformation.

The future of data integration is already here. Explore how Domo’s multi-cloud data fabric connects all your data into a single, unified view — watch a demo and see the difference for yourself.