Is Your Mainframe Data Ready for Cloud Analytics & BI
We recently looked at the topic of “Why Mainframe Data Management is Crucial for BI and Analytics” in an Analytics Insight article written by our CEO, Gil Peleg. Our conclusions, in brief, are that enterprises are missing opportunities when they allow mainframe data to stay siloed. And, while that might have been acceptable in the past, today data and analytics are very critical to achieving business advantage.
How did we get here? Mainframes are the rock on which many businesses built their IT infrastructure. However, while the rest of IT has galloped toward shared industry standards and even open architectures, mainframe has stood aloof and unmoved. It operates largely within a framework of proprietary hardware and software that does not readily share data. But with the revolutionary pace of change, especially in the cloud, old notions of scale and cost have been cast aside. As big and as powerful as mainframe systems are, there are things the cloud can now do better, and analytics is one of those things.
In the cloud no problem is too big. Effectively unlimited scale is available if needed and a whole host of analytic tools like Kibana, Splunk and Snowflake, have emerged to better examine not only structured data but also unstructured data, which abounds in mainframes.
Cloud tools have proven their worth on “new” data, yielding extremely important insights. But those insights could be enhanced, often dramatically, if mainframe data, historic and current, were made available in the same way or, better yet combined – for instance in modern cloud-based data lakes.
It turns out that most organizations have had a good excuse for not liberating their data: It has been a difficult and expensive task. For example, mainframe data movement, typically described as “extract, transform, and load” (ETL), requires intensive use of mainframe computing power. This can interfere with other mission-critical activities such as transaction processing, backup, and other regularly scheduled batch jobs. Moreover, mainframe software vendors typically charge in “MSUs” which roughly correlate with CPU processing loads.
This is not a matter of “pie in the sky” thinking. Technology is available now to address and reform this process. Now, mainframe data can be exported, loaded, and transformed to any standard format in a cloud target. There, it can be analyzed using any of a number of tools. And this can be done as often as needed. What is different about this ELT process is the fact that it is no longer so dependent on the mainframe. It sharply reduces MSU charges by accomplishing most of the work on built-in zIIP engines, which are a key mainframe component and have considerable processing power.
What does all this mean? It means data silos can be largely a thing of the past. It means an organization can finally get at all its data and can monetize that data. It means opening the door to new business insights, new business ideas, and new business applications.
An incidental impact is that there can be big cost savings in keeping data in the cloud in storage resources that are inherently flexible (data can move from deep archive to highly accessible quickly) rather than on-premises. And, of course, no capital costs – all operational expenses. Above all, though, this provides freedom. No more long contracts, mandatory upgrades, services, staff, etc. In short, it’s a much more modern way of looking at mainframe storage.