Bridging the Gap Between Mainframe and Modern Data Streams: A Critical Business Need

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Click to learn more about author Ibrahim Surani.

Information overload is real. Every business today is swimming in a sea of enterprise applications, platforms, and data streams, and the water level rises every time a business expands its portfolio, invests in a new technology, or builds a strategic partnership.

Integrating external resources such as survey data, social media streams, consumer demographics, etc. with internal data sets is imperative to achieve the coveted ‘single version of the truth.’ However, this remains an unattainable feat for many businesses despite investment in modern data integration tools. A major reason behind this is the inability to integrate traditional data with modern data streams.

Mainframe Remains Relevant in the Cloud Era

It is well-documented that a majority of large enterprises run on ancient IT infrastructure. According to American Banker, 71% of Fortune 500 companies still use mainframe computers. More interestingly, 80% of the world’s corporate data is still managed by the first mainframe computer introduced by IBM in 1964. As a result, businesses generate a tremendous amount of COBOL-based data on a daily basis.

Leaving critical data isolated in legacy databases or mainframes leads to missed opportunities. Capturing mobile or social media data is important, but it needs to be enriched with customer reference data, which is stored on legacy systems for better insights. These insights facilitate the decision-making process and help unleash the true potential of Big Data, Machine Learning, and other modern technologies.

Breaking data silos and achieving a unified view of modern and traditional data streams offers unparalleled benefits. However, this can be a challenging endeavor due to a multitude of reasons mentioned below.

Why Legacy Data Integration is a Challenge

There is no denying the utility of modern languages like Java, Linux, C++, and UNIX over older ones, and yet COBOL, considered obsolete, is still omnipresent in the business world. The shortage of mainframe programmers is increasingly dire as COBOL programmers are retiring in droves. The fact that 73% of universities no longer have COBOL programming as part of their curriculum further intensifies the skills shortage.

As COBOL-based systems endure in IT, businesses need to look for alternatives to cope with the challenge of mainframe talent shortage and integrate their legacy data.

Here are a few options that can help businesses unlock mission-critical data from their mainframes and drive better insights for all their enterprise data:

  • Modernize the Mainframe: For better flexibility, scalability, and compatibility, businesses may consider migrating their mainframe applications to new technologies. While this approach may be viable for some organizations, it is expensive, time-consuming, and risky, particularly when a business has outsourced its mainframe application support.
  • Close the Talent Gap: There are various strategies that businesses can use to source or develop talent and bridge the skill gap. While internal talent development is the right approach in the long run, it requires resources and an ongoing commitment. To close the skill gap momentarily, businesses can set up a COBOL training program to develop talent from within the organization.
  • Invest in an Integration Solution: An end-to-end data integration solution that can offload data from mainframes onto the enterprise data hub is the most viable approach to integrate and leverage legacy data. When looking for a data integration tool, it is important to select one that offers the capability to integrate, transform, and write mainframe data onto a variety of destinations.

Legacy systems contain the time-tested, mission-critical business knowledge that, when integrated with modern data streams, can help drive better insights. However, selecting the right approach to legacy data integration remains a complex decision that requires a thorough analysis of the unique data integration needs and objectives of a business.

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