Overcoming Legacy Database Integration Challenges in the Era of Big Data

What are legacy systems? Legacy systems are simply incumbent computer systems that are both installed and working. In other words, the term is not pejorative, but the opposite. Bjarne Stroustrup, creator of the C++ language, addressed this issue succinctly, if not with a little sarcasm to drive his point home: "Legacy code" often differs from its suggested alternative by actually working and scaling.

What are legacy systems? Legacy systems are simply incumbent computer systems that are both installed and working. In other words, the term is not pejorative, but the opposite. Bjarne Stroustrup, creator of the C++ language, addressed this issue succinctly, if not with a little sarcasm to drive his point home: "Legacy code" often differs from its suggested alternative by actually working and scaling.

Many enterprise-grade software systems have reliably served the needs of their businesses, and the people who use them, for years and even decades. These legacy systems have survived multiple releases and multiple revisions of their help manuals and continue to keep the business humming.

A tremendous amount of business data and processes is tied up in legacy systems, ranging from mainframes to custom applications to other applications lacking accessible interfaces.

One benefit of these systems is their reliability and high performance. As a result, organizations are reluctant to abandon them for new technologies. In addition, the data residing in legacy systems is extremely valuable to the business, used in vital initiatives including business intelligence (BI) and analytics. These initiatives often support mission-critical operations, including marketing, HR, customer support, finance, logistics, and more, contributing to an organization’s competitive advantage.

Although legacy data is an indispensable resource, IT teams struggle to find efficient and cost-effective ways to access and leverage it for business purposes. Consider the following challenges:

  • The cost and complexity of migrating to newer platforms is prohibitive. IT analysts estimate that the cost to replace business logic is about five times that of reuse and that's not counting the risks involved in wholesale replacement. In an era where IT teams must do more with less, modernizing the IT infrastructure and migrating legacy platforms and databases to newer technologies may simply not be possible.
  • Accessing data in legacy systems is challenging. IT teams recognize that timely access to data in legacy systems is essential for internal customers, but accessing that information and integrating it with other database systems can be very difficult.
  • Data refreshes from legacy systems may be too slow for BI and analytics purposes. Long delays in legacy system data refreshes have a negative impact on the lines of business. Analysts must work with outdated information or wait for unacceptably long periods of time to get updated data.
  • Accessing data in legacy systems is often a burden for IT teams. In many organizations, business users send the IT department their information requests for BI and analytics tasks. Accessing legacy system data to meet these needs can take a long time due to limited human resources and a lack of expertise.

Want to learn more? Read part 2 of my blog tomorrow!

 

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