What is data integration?

Data integration is the process of combining data from multiple sources into a single, unified view. It involves extracting data from different systems, transforming it into a consistent format, and loading it into a central repository for analysis. This approach helps organizations improve business intelligence, streamline workflows, and make more informed decisions by eliminating data silos.

Data integration plays an important role across all industries. Every organization benefits when siloed data sources are brought together.

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 Data may be stored in an on-premise solution or an outdated proprietary system that was created many years ago.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 successfully, data integration helps organizations make and achieve key performance indicators (KPIs) and streamline processes across departments. In short, data integration helps businesses succeed.

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.

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.

How does data integration work?

Most data integration takes advantage of extract, transform, load (or ETL) methodology.

The first step is to extract data from all the various sources and systems. An ETL tool uses pre-built connectors to sync with data sources or queries the source application programming interface (API).

Then, data is cleaned to correct errors or deficiencies and transformed into a standardized format. This could include standardizing values like time zones, units of measurement, and currencies; validating data to eliminate missing values and duplicates; and applying any specific organization rules or compliance requirements.

Finally, the data is loaded into either a single location, like a data warehouse, or a destination system like Domo.

Data integration can also follow an ELT — or extract, load, transform — approach. This is particularly useful when working with a cloud platform. The data is extracted and loaded into the cloud and then transformed.

No matter the approach, this data ingestion process is repeated frequently so that the central data source is updated with the latest information. Domo is designed to give users access to real-time data, constantly refreshing data sets and capturing streaming data.