Just in case you missed the incessant drumbeat coming out of IBM, the company committed to cognitive computing. But that works for z data centers since IBM’s cognitive system is available on-premises only for the z. Another z first: IBM just introduced Machine Learning (key for cognitive) for the private cloud starting with the z.
There are three ways to get IBM cognitive computing solutions: the IBM Cloud, Watson, or the z System, notes Donna Dillenberger, IBM Fellow, IBM Enterprise Solutions. The z, however, is the only platform that IBM supports for cognitive computing on premises (sorry, no Power). As such, the z represents the apex of programmatic computing, at least as IBM sees it. It also is the only IBM platform that supports cognitive natively; mainly in the form of Hadoop and Spark, both of which are programmatic tools.
What if your z told you that a given strategy had a 92% of success. It couldn’t do that until now with IBM’s recently released cognitive system for z.
Your z system today represents the peak of programmatic computing. That’s what everyone working in computers grew up with, going all the way back to Assembler, COBOL, and FORTRAN. Newer languages and operating systems have arrived since; today your mainframe can respond to Java or Linux and now Python and Anaconda. Still, all are based on the programmatic computing model.
IBM believes the future lies in cognitive computing. Cognitive has become the company’s latest strategic imperative, apparently trumping its previous strategic imperatives: cloud, analytics, big data, and mobile. Maybe only security, which quietly slipped in as a strategic imperative sometime 2016, can rival cognitive, at least for now.
Similarly, IBM describes itself as a cognitive solutions and cloud platform company. IBM’s infatuation with cognitive starts with data. Only cognitive computing will enable organizations to understand the flood of myriad data pouring in—consisting of structured, local data but going beyond to unlock the world of global unstructured data; and then to decision tree-driven, deterministic applications, and eventually, probabilistic systems that co-evolve with their users by learning along with them.
You need cognitive computing. It is the only way, as IBM puts it: to move beyond the constraints of programmatic computing. In the process, cognitive can take you past keyword-based search that provides a list of locations where an answer might be located to an intuitive, conversational means to discover a set of confidence-ranked possibilities.
Dillenberger suggests it won’t be difficult to get to the IBM cognitive system on z . You don’t even program a cognitive system. At most, you train it, and even then the cognitive system will do the heavy lifting by finding the most appropriate training models. If you don’t have preexisting training models, “just use what the cognitive system thinks is best,” she adds. Then the cognitive system will see what happens and learn from it, tweaking the models as necessary based on the results and new data it encounters. This also is where machine learning comes in.
IBM has yet to document payback and ROI data. Dillenberger, however, has spoken with early adopters. The big promised payback, of course, will come from the new insights uncovered and the payback will be as astronomical or meager as you are in executing on those insights.
But there also is the promise of a quick technical payback for z data centers managers. When the data resides on z—a huge advantage for the z—you just run analytics where the data is. In such cases you can realize up to 3x the performance, Dillenberger noted. Even if you have to pull data from some other location too you still run faster, maybe 2x faster. Other z advantages include large amounts of memory, multiple levels of cache, and multiple I/O processors get at data without impacting CPU performance.
When the data and IBM’s cognitive system resides on the z you can save significant money. “ETL consumed huge amounts of MIPS. But when the client did it all on the z, it completely avoided the costly ETL process,” Dillenberger noted. As a result, that client reported savings of $7-8 million dollars a year by completely bypassing the x-86 layer and ETL and running Spark natively on the z.
As Dillenberger describes it, cognitive computing on the z is here now, able to deliver a payback fast, and an even bigger payback going forward as you execute on the insights it reveals. And you already have a z, the only on-premises way to IBM’s Cognitive System.
DancingDinosaur is Alan Radding, a veteran information technology analyst, writer, and ghost-writer. Please follow DancingDinosaur on Twitter, @mainframeblog. See more of his IT writing at technologywriter.com and here.
Tags: Anaconda, analytics, Assemble, Big Data, Cloud, COBOL, cognitive computing, confidence-ranked results, ETL, FORTRAN, hadoop, IBM, Java, Linux, machine learning, mainframe, mobile, private cloud, programmatic tools, Python, security, Spark, System z, technology, z/OS