As more organizations recognize the importance of using all of the data at their disposal, it’s important that their data scientists have an understanding of the capabilities of these technological advancements. Data integration is crucial to creating a standard by which companies deal with the litany of information on hand. This can assist in their workflow while helping to make better decisions regarding business processes. Let’s take a deeper look at data integration and a couple of techniques that are commonly used today.

Understanding Data Integration

Before we time into data integration applications, we must first understand what goes into this data virtualization capability. Data integration is the process of bringing data from disparate sources together to provide a more unified view. The premise of integration is to make this information more freely available and easier to consume for systems and users alike. Data integration is designed to reduce IT costs, free up resources, and improve data quality without sweeping changes to existing applications.

Companies with better data integration capabilities have significant advantages over their competition, increasing their operational efficiency by reducing the need to manually transform and combine data sets. This provides more valuable insight development through a holistic view that’s easier for analysts to maintain and understand. A digital business is built around algorithms that process and extract information through proper data management. Data integration enables a full view of all of this information flowing through an organization’s different systems.

Extract-Transform-Load

While there are a handful of data integration techniques, one of the most common is ETL: extract, transform, and load. The ETL process revolves around extracting the desired data from a source system, transforming it into a consistent format, and loading it into a target system. ETL is largely automated, with modern tools offering workflow creation where you can specify the data sources and sets of variables that are of the utmost importance to your query. Once completed, the workflow could include multiple integrations from different sources for a digital transformation for companies of any size.

This consolidation of data through ETL starts with deciding on an extraction method. This can be based on notifications or changes within certain data lakes. It can be done in an incremental fashion, or through full extraction of data depending on the line of business. Transforming the data can be accomplished through the standardization of formats, but there is also the cleansing of all of this information. Cleansing is important to scrub through missing values or any repetitive data, preventing any redundancies that could skew analytics and lead to flawed decision-making based on incorrect data.

Point-to-Point Integrations

While ETL has been a common approach for decades, point-to-point integrations are still popular in a number of enterprise applications. If you want all of your applications to be able to maintain proper convergence, you’ll need to create proper contacts for the interpretation of the results. With each new application release, you’d need to do regression testing and fix issues continuously. While this can be a lot to undertake for real-time data integration, it gives true insight into the integration process with each step.

Point-to-point integration is now relying on variants such as Enterprise Application Integration, or EAI, to create models with a hub-and-spoke approach to data analytics. With data integration software, there’s a pre-built environment that allows for rapid development, while better addressing error handling. The result is an integrated layer of services, with business intelligence applications invoking services throughout the entire organization for useful insights. Regardless of technique, investing in data integration early on can help a business build on a strong basis for combing through vast databases as growth continues.