How to Build a Modern Data Architecture with Legacy Data
Unlike newer companies, which were born in the cloud, well-established companies haven’t had the benefit of using all their data from day one. To compete effectively, they must integrate data from many disparate sources, although mainframe data is often missing because it’s too difficult to access, to build a modern data architecture.
Yet a business’s ability to stay competitive depends on its ability to leverage data. Today’s most successful companies operate beyond function-specific analytics and even interdepartmental analytics, enabling enterprise-wide analytics that incorporate data from a combination of internal and external sources. They’re also using machine learning to answer questions they haven’t been able to answer previously.
Despite technological advances, enterprises still have trouble even accessing their data, especially legacy mainframe data. As a result, most companies have a fragmented data architecture that doesn’t support their strategic goals. Incorporating legacy data from mainframe brings its own unique challenges but modernizing your data architecture doesn’t need to be a daunting project.
This eBook will walk you through the four steps of building a modern data architecture that’s cost-effective, secure, and future proof. And address the most common challenges of integrating mainframe data:
- Data structure
- Data mapping
- Different storage formats