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Is the Value of Real-Time Mainframe Data Oversold?

Gil Peleg


May 4, 2021

Vendors are scrambling to deliver modern analytics to act on streams of real-time mainframe data.  There are good reasons for attempting this activity, but they may actually be missing the point or at least missing a more tantalizing opportunity.  

Real-time data in mainframes comes mostly from transaction processing. No doubt, spotting a sudden national spike in cash withdrawals from a bank’s ATM systems or an uptick in toilet paper sales in the retail world may have significance beyond the immediate “signal” to reinforce cash availability and reorder from a paper goods supplier. These are the kinds of things real-time apostles rave about when they tout the potential for running mainframe data streams through Hadoop engines and similar big data systems.

What’s missed, however, is the fact that mainframe systems have been quietly accumulating data points just like this for decades. And where mainframe data can be most valuable is in supporting analytics across the time axis. Looking at when similar demand spikes have happened over time and their duration and repetition offers the potential to predict them in the future and can hint at the optimal ways to respond and their broader meaning.

Furthermore, for most enterprises, a vast amount of real-time data exists outside the direct purview of mainframe: think about the oceans of IoT information coming from machinery and equipment, real-time sensor data in retail, and consumer data floating around in the device universe. Little of this usually gets to the mainframe. But it is this data, combined with mainframe data that is not real-time (but sometimes near-real-time), that may have the greatest potential as a font of analytic insight, according to a recent report.

To give mainframes the power to participate in this analytics bonanza requires some of the same nostrums being promoted by the “real-time” enthusiasts but above all requires greatly improving access to older mainframe data, typically resident on tape or VTL.

The optimal pattern here should be rescuing archival and non-real-time operational data from mainframe storage and sharing it with on-prem or cloud-based big data analytics in a data lake.  This allows the mainframe to continue doing what it does best while providing a tabula rasa for analyzing the widest range and largest volume of data.

Technology today can leverage the too-often unused power of zIIP engines to facilitate data movement inside the mainframe and help it get to new platforms for analytics (ensuring necessary transformation to standard formats along the way).

It’s a way to make the best use of data and the best use of mainframe in its traditional role while ensuring the very best in state-of-the-art analytics.  This is a far more profound opportunity than simply dipping into the flow or real-time data in the mainframe. It is based on a fuller appreciation of what data matters and how data can be used. And it is the path that mainframe modernizers will ultimately choose to follow.

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About the author

Gil Peleg | CEO
Gil has over two decades of hands-on experience in mainframe system programming and data management, as well as a deep understanding of methods of operation, components, and diagnostic tools. Gil previously worked at IBM in the US and in Israel in mainframe storage development and data management practices as well as at Infinidat and XIV. He is the co-author of eight IBM Redbooks on z/OS implementation.

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