What is data integration?

Data integration is the process of combining data from multiple sources into a single target data store. Learn more now.

Enterprises that wish to remain relevant in their respective markets are embracing big data, including its benefits and challenges. Data integration plays a key role in big data as it allows organizations to better manage business and customer data while deriving and sharing key business insights. In this guide to data integration, we explain how data integration works and some of the tools that help companies to accomplish data integration goals.

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What is data integration?

Data integration refers to the practice of consolidating data that resides in disparate sources into a single and unified dataset. The objective of data integration is to make data accessible, accurate, complete and up-to-date so it can be used for data analysis, business intelligence and other applications.

SEE: Cloud data warehouse guide and checklist (TechRepublic Premium)

Data integration also allows an organization to have a 360-degree view of their business and increases the efficiency of collaboration between external and internal users. This is especially true for large organizations or companies that use big data.

How does data integration work?

There is no universal approach to data integration but rather various methods and strategies. In its simplest form, data integration works by connecting the source of data — such as a network of data sources — to the target — such as a master server — and routing data through this channel. This can include real-time integration of data streams or feeding copies of data from disparate data sets into a cohesive dataset.

How to choose data integration tools

Different types of data integration tools are used to move data from source to target. An organization should consider several factors when choosing the right type of data integration tools for its business requirements, including rreal-time data availability, scalability, security and compliance goals. Data integration tools help data teams to perform data mapping, data transformation and data cleansing tasks.

SEE: Cloud data warehouse guide and checklist (TechRepublic Premium)

There is an increasing number of cloud-based data integration tools that use a cloud data warehouse and iPaaS platforms. On-premises data integration tools use a local network for loading batches of data from several data sources. There are also open-source data integration tools that do not use any proprietary software and offer more control to an organization on how they want to integrate data.

Challenges of data integration

Data typically exists in sprawling and incompatible formats; if that data needs to be integrated, it may require modification to get it ready for a single and unified dataset. This often leads to extra work for developers. However, some digital transformation tools can help make this process more efficient by analyzing the base language of the data and making automatic changes to the data.

Another common challenge with data integration is the quality of data. Invalid or erroneous data can corrupt the entire dataset after data integration. A good way to eliminate or minimize this problem is to validate the data as soon as it is input into the system. This approach will also help to remove duplicate data before data integration.

Benefits of data integration for your business

An increasing number of organizations are embracing big data as they seek to get the maximum value from their data and gain a competitive advantage. The abundance of data, which can be in different formats, can make it challenging and inefficient for an organization to derive real-time information and perform data analytics.

This is why data integration is important, as it allows organizations to transform data into a format that makes it easy for them to use. In turn, companies can uncover real-time business insights, complete seamless knowledge transfers between users and maintain data integrity and quality across a variety of business use cases.