How to Build a Modern Data Architecture with Legacy Data
Business competitiveness depends on an organization’s ability to leverage data. 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.
Meanwhile, competitive pressures are building. 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.
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.
Common challenges of building modern data architectures
Different organizations are at different stages of data-driven maturity, but they all tend to face the following common challenges:
- Data access
- Environmental complexity
- Data quality
- Multiple data types and formats
- Data delays
- Data strategy
- Inadequate resources
- Competitive pressures
Furthermore, incorporating legacy data from the mainframe brings its own unique challenges, such as:
- Data structure
- Data mapping
- Different storage formats
This eBook will walk you through the four steps of building a modern data architecture that’s cost-effective, secure, and future proof. Learn more.