Companies that want to stay relevant in their respective markets are embracing big data, including its benefits and challenges. Data integration plays a critical role in big data, enabling organizations to better manage business and customer data while capturing and sharing key business insights.
Data integration refers to the practice of consolidating data that resides in disparate sources into a single, unified dataset. The goal of data integration is to make data accessible, accurate, complete, and up-to-date so that it can be used for data analytics, business intelligence, and other applications.
Data integration also allows an organization to have a 360-degree view of its 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.
There is no universal approach to data integration, but rather various methods and strategies. In its simplest form, data integration works by connecting the data source – such as a network of data sources – to the target – such as a master server – and routing the 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 data set.
Different types of data integration tools are used to move data from source to destination. An organization must consider several factors when choosing the right type of data integration tools for its business requirements, including real-time data availability, scalability, security, and compliance goals. Data integration tools help data teams perform data mapping, transformation, and cleansing tasks.
There are more and more cloud data integration tools that use a cloud data warehouse and iPaaS platforms. Local data integration tools use a local network to load batches of data from multiple data sources. There are also open source data integration tools that do not use any proprietary software and give an organization more control over how it wants to integrate data.
Data Integration Challenges
Data often exists in scattered and incompatible formats; if that data needs to be integrated, it may need to be modified to be ready for a single, unified dataset. This usually means 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 it.
Another common challenge in data integration is data quality. Invalid or erroneous data can corrupt the entire data set after data integration. A good way to eliminate or minimize this problem is to validate the data as soon as it is entered into the system. This approach will also help eliminate duplicate data prior to data integration.
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