Good business decisions require accurate, timely data. But when that data exists across separate systems, mistakes happen, from wrong orders and late shipments to inaccurate inventory.
That’s why many companies use Electronic Data Interchange (EDI). It allows them to share important documents — like orders, invoices, and shipping notices — quickly and accurately, reducing errors and improving efficiency.
But EDI only works well when your internal systems are connected, whether inventory, accounting, or orders. Data integration makes this happen, keeping vital info accurate and ready to use when you need it.
In this guide, we’ll explain what data integration is, why it matters, and some of the top integration solutions to consider.
What’s Data Integration?
Data integration is the process of combining information from different sources — such as databases, applications, or spreadsheets — into a single, unified view for a specific use, such as reporting, analysis, or automation. It plays a key role in enterprise data integration, a broader strategy that connects systems across an organization and gives teams consistent access to information. This makes it easier to analyze data, support business intelligence efforts, and act on insights (more on enterprise data integration below).
Across industries, teams rely on data integration to optimize operations. In marketing, it helps companies understand customer behavior by combining data from channels like social media and advertising platforms. In healthcare, it supports better patient care and larger initiatives. Some organizations use data virtualization to access records from multiple systems in real time — combining health records, insurance claims, and feedback into a complete patient view without physically moving the data.
7 Key Concepts to Understand
Enterprise data integration isn’t a single tool or process. Think of it as an umbrella concept that includes ideas like connecting data sources, automating workflows, integrating data across platforms, and ensuring systems can communicate with each other. It also involves data management, which includes maintaining accuracy, accessibility, and consistency across systems.
To fully grasp what enterprise data integration is, here’s a look at some of the key methods and concepts it involves:
ETL
ETL (Extract, Transform, Load) is a classic data integration method. It pulls data from various sources, converts it into a usable format, and loads it into a target system like a data warehouse. The goal is to create a centralized, unified view of data. ETL often works alongside other techniques, such as data replication or data virtualization, which allows systems to access and analyze data without physically relocating it.
ELT
ELT (Extract, Load, Transform) is similar to ETL, but it loads raw data into the target system first and transforms it afterward. Since the target system often has more processing power, ELT is often more efficient than ETL, especially for large-scale datasets.
Data Streaming
Data streaming moves information in real time. It continuously extracts and processes data from different sources as it arrives, allowing businesses to react immediately to new trends or issues.
Application Integration
Application integration connects different software systems — such as apps, platforms, and internal tools — so they can share data automatically and work together more efficiently. This reduces the need for manual data entry, duplicate records, or other time-consuming workarounds.
Data Virtualization
With data virtualization, businesses can access and analyze information from multiple sources without physically moving or duplicating the data. This creates a real-time view across systems, ideal for fast reporting and decision-making.
Data Governance
Data governance refers to the policies, processes, and standards that define how an organization handles data management, including accuracy, security, access, and compliance. For companies handling large volumes of data, integrating data with a strong governance strategy supports better decision-making and reduces risk.
Why Data Integration Matters: 4 Benefits for Enterprises
Integrating data across systems offers several advantages, especially for growing businesses. Enterprise data integration helps streamline operations, improve decision-making, and reduce costly inefficiencies. Here’s an overview of four primary benefits that data integration provides:
Ensures Better Decision-Making
Data integration gives businesses a complete view by combining information from multiple sources — whether different departments, apps, or systems. This centralized approach supports stronger business intelligence, making it easier to spot trends, find growth opportunities, and make informed decisions.
Improves Efficiency
When businesses integrate their data, they break down data silos that slow teams down and cause inconsistencies. Integration eliminates duplicate records, improves accuracy, and streamlines access to data — and by automating data transformation, it reduces the need for manual corrections and reformatting.
Reduces Costs
Data integration makes it easier to find patterns, spot errors, and understand where things aren’t working well. This helps businesses cut costs and discover opportunities to create new products or services.
Enhanced Accuracy and Trust
Data integration ensures consistent, accurate information that helps businesses build trust across departments, with stakeholders, and within teams. Clear, accurate data strengthens an organization’s credibility with customers and supports better relationships with trading partners.
6 Use Cases of Data Integration
Integrating data plays a major role across industries, from banking to healthcare. Here are some common use cases:
Data ingestion collects and imports data into a target location, such as a data warehouse, for future analysis. Banks use this type of data collection for real-time transaction processing.
Data replication is used in e-commerce and telecommunications to maintain consistent inventory data and customer records. It copies data across several locations so that it’s accessible and consistent for all users.
Data warehouse automation streamlines data management and operations within data warehouses, helping teams speed up reporting and improve accuracy.
Big data integration combines large datasets for analysis. Governments often use this method for policy decision-making and in cybersecurity to monitor threats.
Data lake development stores structured and unstructured data for later use in scientific research or analysis.
Customer 360°Viewbuilds unified profiles using data from sources like social media, purchase history, reviews, and feedback. It helps businesses tailor marketing and sales efforts based on personalized insights.
7 of the Best Enterprise Data Integration Software Tools in 2025
With so many platforms on the market, choosing an integration system comes down to how well it fits your business goals, tech stack, and growth plans. Here are some of the top integration solutions available today:
Orderful is a cloud-based EDI platform that simplifies B2B data exchange. Its API-driven design allows businesses to connect with trading partners quickly without needing custom integrations.
Informatica is a data integration platform that simplifies routine tasks using low-code and no-code tools, making it accessible for technical and non-technical users alike.
Talend Data Integration allows businesses to connect, manage, and transform data from various sources by developing and using data pipelines.
CData helps organizations consolidate data by connecting enterprise systems and applications, making it easier to access and analyze information across platforms.
Airbyte is an open-source data integration platform that supports ELT and ETL workflows and includes API and Terraform integrations.
Qlik collects and transforms real-time data from various sources and delivers analytics-ready data for faster insights.
Mulesoft lets businesses integrate data across on-premises and cloud-based systems. It also provides tools for API management and system orchestration.
Unlock Seamless Data Integration with Cloud-Based EDI
Whether you're just starting with data integration or looking to modernize your integration system, cloud-based EDI can make a big difference. From automating document exchange to improving data accuracy, cloud EDI helps your business move faster and operate with greater confidence.
Ready to take the next step? Talk to an expert today and discover what the right EDI partner and data integration solution can do for you.
Data Integration FAQs
Can a Data Warehouse Help with Business Intelligence?
Yes — unlike a regular database used for daily operations, a data warehouse works with BI (business intelligence) tools to help companies analyze their data more easily. It stores processed and structured data, which makes analysis faster and more straightforward.
What’s the Difference between a Data Lake and a Data Warehouse?
Both data lakes and data warehouses store data, but they serve different purposes. A data lake collects raw, unprocessed data from multiple sources, while a data warehouse holds structured, ready-to-analyze data. Many businesses use data lakes to handle large volumes and data warehouses to support fast, efficient analytics.
How Does Data Visualization Simplify Integration?
Data virtualization lets businesses analyze data across systems in real time without moving it between platforms. This eliminates the need for manual data transfers, which can be complex and time-consuming.
How Can Data Integration Remove Data Silos?
Data silos form when different teams or departments store information in separate systems that don’t communicate with each other. This setup makes it harder to access information, increases the risk of duplication or errors, and slows down business processes. Data integration solves this problem by linking systems and combining information into a single, shared view. When teams use integrated data, they collaborate more easily, make faster decisions, and compete more effectively in the market.