Unito’s Ultimate Guide to Data Integration
Businesses today are drowning in data, and more driven than ever to make the most of it. When analyzed properly, that data has the power to boost efficiency and dramatically improve an organization’s operations, because it allows teams to base decisions on actual information about how their company works. But raw data isn’t much good to anyone — to be useful, it needs to be consistent, organized, and collected together into a single, unified system. That’s what data integration is all about.
Keep reading, and we’ll explain what data integration is, why companies do it, common struggles to watch out for, and a few of the most common ways to integrate data.
What is data integration?
Data integration is the process of pulling data from one or more systems and pushing it to another system. These systems can include software applications, servers, and databases. Through data integration, business users can consult any range of data from a single platform, rather than hopping from system to system to get a more complete picture of what they’re analyzing.
Data integration creates a single source of truth, which can eliminate duplicate data, enrich essential information, and reduce the number of systems an average employee has to use in their day-to-day work.
Why is data integration so important?
Companies use an average of 106 SaaS—software-as-a-service or subscription-based—apps. Add any software platforms that aren’t delivered through a SaaS model, like on-premise or legacy tools, and that number increases. Every app, tool, or system is another data source. Data that may conflict with, or be a duplicate of, data in another system. Anyone trying to collect a broad set of data for a specific task, such as analysis or reporting, has to either check multiple systems or knowingly work with a more limited dataset.
Data integration, by making data more portable and accessible, builds a strong data-driven foundation for a number of essential business tasks. And, for the average business user, it all happens in the background.
What are the essential steps of data integration?
Data integration can be performed in a number of ways, through a variety of platforms. But despite that variance, data integration always involves a source system and a destination system. The source is where the data is originally found, and the destination is where it needs to go. The process that gets data from source to destination almost always follows the same steps:
- Extract: The source data is copied from the system(s) where it’s originally found.
- Transform: The data is cleaned, updated, or modified to match the specifics of data found in the destination system. This process might involve checking individual bits of data for duplicates.
- Load: The data is loaded into the destination system.
While this process looks simple, it typically involves a significant amount of resources, whether that’s with custom-built solutions or data integration applications.
The benefits of data integration
Data integration can be an expensive, resource-intensive process. But organizations implement data integration because of the serious benefits it brings.
Easier access to data
The primary benefit of data integration is increased access to data for the average business user. Systems engineers and software developers might be able to navigate the many data storage systems the average organizations use, but the average business user can easily get completely lost. With data integration, you can pull from multiple systems and deliver essential data to these users.
High-quality data
When your data’s scattered across multiple systems, you’ll often deal with duplicates or variable quality. With data integration, duplicate data can be scrubbed out of your central system, while any bits of incomplete data can be enriched with information from other systems.
Scalable architecture
Disparate systems and scattered data aren’t scalable. The more your organization grows, the more data it produces, scattered across an increasing number of systems. By investing in a data integration infrastructure, you make your data systems more scalable. Even if you double the number of data sources you rely on, your data integration infrastructure will send that data where it needs to go.
Better productivity
Data integration makes every workflow more efficient. Marketers can run better campaigns because they have all the customer data they need without making a data request. Leaders make better decisions, finance teams spend less time hunting down statements, and developers can push updates without copying and pasting their work in multiple platforms.
The challenges of data integration
Despite its many advantages, data integration can be a very complex process that does come with inherent challenges. Most often, organizations have a general understanding of the goal of their integration process and the data that it will need to include. But setting up the necessary infrastructure can be difficult.
Resource requirements
Data integration requires a significant initial investment. Custom-built data integration solutions can take months to build and deploy, tying up your systems engineers. Even premade solutions require an investment in time and money to get working right.
Maintenance
The more complex your data integrations, the greater the need for ongoing maintenance. Updates in individual data systems, evolving processes, and even software issues with your data integration system can result in significant maintenance.
Multiple data formats
While the data in different systems might look the same to the end user, it rarely is. Data integration platforms process data from the source system to match the data in the destination system. The extent of that processing varies, and it can affect the compatibility of some systems with others.
4 types of data integration
Data integration connects disparate systems to keep data flowing between them. The way that’s done can vary widely depending on the infrastructure you use.
ETL (Extract, transform, load)
ETL data integrations extract data from a source system, transform it, and then load it into the destination system. This allows data to be standardized before it’s added to the destination system, improving compatibility of that system with the rest of your infrastructure. That said, it can increase the load on your integration infrastructure, since it needs to process all the data it’s pushing.
ELT (Extract, load, transform)
Similar to ETL systems, the main difference here is that ELT integrations transform your data in the destination system instead of transforming it on the way. This can reduce the load on your integration, but it does require a more robust destination system.
Data streaming
ETL and ELT tools typically move data according to a scheduled interval, such as once a day or once a week. Data streaming, in contrast, continually sends data from source systems to your destination system, giving you fully up-to-date data in real-time.
Data virtualization
Data virtualization systems only move data when an end user requests it, instead of syncing it in batches or in real-time. This gives users near-instant access to data while reducing the load on your overall data integration infrastructure.
Integrate and dominate
Getting the most out of data is essential for companies to compete in today’s business world. That means more people need to become familiar with complicated processes like data integration.
With more and more of your interactions happening online, nearly every stage of a business’s operations generates data — and all of it contains clues as to how those processes can be optimized, streamlined, and made more efficient. For companies, data integration is just one step in the process of using that data to reach their goals.
How can Unito help with data integration?
Here's how the data team at Unito syncs data points from Airtable to Google Sheets to create a hollistic data integration system.
FAQ: Data integration
What are the essential features of data integration platforms?
Data integration platforms need the following features:
- A library of pre-built data connectors for popular applications.
- API support and developer platform for creating custom connectors.
- Visual data mapping.
- Business logic application.
- Data enrichment.
Why is data integration so essential?
As organizations use more and more software tools, data becomes increasingly scattered throughout these platforms. Data integration allows business users to get access to more data, no matter where it’s housed, without hopping from platform to platform.
Do I need a developer for data integration?
Typically, you’ll need a developer or engineer for at least a portion of the data integration process. If you use a pre-built data integration platform, you may not need to rely on developers as much, but you likely will need them for initial deployment and ongoing maintenance.
Recent updates
September 29th 2025: We significantly reworked the structure of this article to add information on types of data integration, the data integration process, and an FAQ