Data integration as a formal practice gained prominence in the late 20th century with the rise of relational databases and the need for a more efficient way to manage and analyze data. It has come a long way from manual processes to highly automated and scalable solutions that meet the ever-increasing data challenges of the modern business landscape.

To gain a greater understanding, Franklin Okeke, writing for TechRepublic Premium, looks at how it functions, its importance, the advantages, the drawbacks (and how to mitigate them) and the methods of data integration.

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CHALLENGES OF DATA INTEGRATION

Data security and privacy concerns

These are imperatives in data integration, especially with confidential and sensitive data. As the data integration process involves collecting, sharing and exchanging data from disparate sources, the risk of exposing data is considerably high.

If an organization is lax in implementing data security measures like proper encryption methods to protect sensitive data from security breaches, access, control and unauthorized use, they stand the risk of exposing vital information to the wrong hands, which can result in cyberattacks and phishing, loss and destruction of data, lawsuits, fines and compliance issues.

For example, you might need to handle data that includes people’s personal information. It’s crucial to ensure that this sensitive data is kept safe by using techniques like data masking, such as tokenization or encryption, to avoid revealing private details.

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TIME SAVED: Crafting this content required 20 hours of dedicated writing, editing and research.