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Big data has certainly left its stamp on every sector of business. Companies rely on real-time analytics insights to improve everything from equipment maintenance to supply chain management. However, the massive amounts of data they collect can make data integration difficult. They have data coming from so many different sources and systems that it creates data silos. Data silos make information more difficult to access and create a hurdle for analytics projects.

Data integration enables businesses to see a unified view of data from disparate sources and combine new and legacy systems. It even enables partners to share data and run separate and collaborative analytics projects. The key to maximizing your data is choosing the right integration strategy. In this brief piece, we’ll discuss different data integration solutions and tips for choosing the right one for your next integration project.

Learn the basics of data integration.

You need to understand the basics of data integration before you can understand why your company needs it. Data integration is the combining of information from different databases and data sources. It involves cleansing raw data to combine data from disparate sources in a unified view.

So, what is data integration? The simple answer is it’s merely a combining of data and analytics applications for business intelligence purposes. It’s one of the most important business processes as it enables them to initiate analytics projects, maximizing company and customer data.

Understand the different data integration processes.

The first step in the decision-making process is to gather all the necessary information. In this case, you need to know about the different integration strategies.

Extract, transform, load (ETL) is an integration tool for manual integration. It involves a data scientist choosing data from various sources, inputting the desired format, transforming the data to match that format, and loading it onto the new system. ETL is the optimal process for integrating data from legacy systems.

Data virtualization is one of the newer and more popular integration techniques. Its methodology relies heavily on automation, producing time and cost savings. Data virtualization involves using a data virtualization platform to pull data from various sources and make it ready and available for real-time analytics projects. One benefit of virtualization is it doesn’t require any data movement, meaning all data remains in its source systems.

Application integration is another of the more popular strategies. It’s the process of integrating different enterprise applications, enabling them to share data and certain functions.

Consider your data integration needs.

Now that you’re familiar with some of the more popular integration strategies, it’s time to think about your company’s needs. Indeed, your end goal should be one of your determining factors when choosing a data integration platform and strategy.

If you’re integrating data from legacy systems, it’s a good idea to choose the integration process that gives you the most autonomy. That would be manual integration for which you would use an ETL platform as a mediator. This method enables data scientists to manually transform data from legacy systems to fit your new data infrastructure.

Data virtualization is the better process if you’re looking for a big data project. Virtualization will enable your data scientists to create a virtual data federation from which you can see your data in a unified view and initiate various types of data projects.

Data integration is a lot like mowing the lawn. You don’t want to do it, but you feel much better about yourself and your yard afterward. The same is true of data integration processes. No company looks forward to them. However, once data integration is complete, they feel much better about their data quality and access.

 

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