In the spirit of Halloween, I call to mind the words of the horror movie icon Freddy Krueger, who asks of his nemesis in Freddy vs. Jason, "Why won't you die?" That's the same question data old-timers ask about the mainframe -- a haunting IT specter, indeed.
Freddy Krueger
We've been hearing about the mainframe's impending demise since the advent of microcomputers in the 1990s. Over the last 20 years, many organizations have downsized their legacy applications and production systems to prepare for the day when these artifacts from an era of lesser technical intelligence disappeared from the computing landscape.
But, just like Jason, the mainframe lives on and on. In fact, fresh models keep rolling off the assembly line, as the New York Times reported in August about IBM's new zEnterprise EC12. Microcomputers may have facilitated the personalization of information processing, but the mainframe remains a viable option (though a pricey one) for organizations processing huge volumes of information. Mainframes have been a staple in banking and telecommunications, and they're selling well in emerging markets like Asia and Africa, the Times said.
Now big-data has some organizations revisiting mainframe costs and benefits. If big-data benefits are large enough, they may be able to justify the capital investment in a mainframe. And, given this potential, analysts ought to be aware of the mainframe's big-data processing potential.
For those 40 and younger who have not worked in a mainframe environment, that means becoming familiar with this technology. Your big-data analytics may very well run on a mainframe one day. You should learn the operating system syntax and utilities, which are different from those found in Microsoft Windows or Unix systems. You should become familiar with the storage structure and file naming convention. Furthermore, you should become comfortable with workspace allocation and disk-space partitioning. If your organization adopts a mainframe solution to address big-data resources, you'll benefit by understanding the seasoned methods and tools you'll need to do your work.
If you are older than 40, then you may need to brush off your REXX, CICS, and COBOL skills, so you can create new applications using old tools. Or you'll have to learn how to tie analytics into cloud processing and to tailor legacy code to work on mainframes that may also use blade servers. In effect, the old big iron machines have been reengineered to do some new stuff.
Whether you are young or young at heart, the mainframe is a platform that will become or remain a part of your career. It's been said, "A bend in the road is not the end of the road, unless you fail to make the turn." The folks at IBM have made the turns that prevented the mainframe from reaching the end of the road. Similarly, analysts will have to extend their platform awareness.
Like Jason, the mainframe just won't die. Do you think that's an IT trick or a treat?
Hi Bryan, we hear a lot these days about high-performance analytics, using speedy processing techniques like in-database computing, in-memory computing, or grid computing (HPA, of course, is favorite of SAS, this site's sponsor). Do you see organizations making a mainframe or HPA decision?
When i began learning about computers something like 12 years ago, we already learnt about mainframes in the history part of computers and didn't have a place we could go to see an actual mainframe. Its interesting how they've been creeping back to relevance in the wake of big-data since about 2 years ago, something like the return of the dinosaurs and the IBM empire...
HPA - High Performance Analytics - using speedy processing techniques like in-database computing, in-memory computing, or grid computing. Basically, you make the analytics faster by moving the processing to another location or splitting up the processing across processing nodes.
In-memory computing can indeed speed up data analysis and help companies gain competitive advantages. In-memory computing benefit from the constantly falling price of memory which most organizations can easily afford nowadays. The move to in-memory computing might even become inevitable for certain analytics solutions where entier set of data need to be investigated rather than a representative.
kichecko -- I remember years ago (more than 12 but I won't say how many!) as a young reporter covering the rapidly changing telecom industry, I had the opportunity to visit an MCI (now long defunct) data center to see its mainframe operations. I knew nothing about computing, but the size was impres sive. Now the smaller the size the more impressive, it seems! I suppose mainframes are more common in US, for example, with more mature markets?
As to HPA -- that's high-performance analytics (analytics run on high-performance computing platforms). SAS, this site's sponsor, has lots of info on HPA to share. If you're interested, you can find out more on its HPA microsite.
Beth, - Thanks for the HPA link. Talking of years back, its actually 14 and not 12 now that i think of it. When i had just joined high school...lol. Whats funny is that upto today i still haven't seen an actual mainframe computer though i've visited a handful of relatively big server rooms locally. I should make it a mission to find out who has a mainframe nearby so that i go and look.
Actually, I see organizations making decisions to move away from mainframes for cost reasons and stay away from/move cautiously toward HPA for cost reasons. There are exceptions like the Census Bureau, which processes huge volumes of data and has licensed most of SAS' products. But outside of the Census Bureau, shops with legacy applications are trying to migrate productions systems to UNIX servers to save money. Often these same shops have invested in contract labor augmentation vis-à-vis investing in new analytic software such as HPA. Seasoned contractors with database and analytic skills can mimic the HPA environment without the sponsors having to purchase additional software. Location is also important. SAS human capital is abundant between Virginia (Northern) to New Jersey; NESUG is one of the largest conferences. In those areas where talent acquisition is a challenge, organizations lean toward purchasing solutions like HPA. In short, companies invest in either people or a product line. Acquiring affordable off-shore talent is also a popular option in the effort to migrate from big boxes and high-end solutions. Traditionally, SAS' did most of its high end solutions in Europe, the Middle East and Asia because the talent pool was sparse. The US had more analytics talent (as demonstrated by the plethora of users groups). If shops can get their staffs to design efficient solutions, they will do so (the cost of training is less than expanding the range of licensed products). But if the younger talent pool has more heterogeneous interests (products beyond analytics), the HPA is seen as the better choice.
For those of us who are old school but young at heart, we will concede that the mainframe won't die. It just keeps getting reinvented. At the IACIS conference in Myrtle Beach earlier this month, network appliances were demonstrated that enable computer labs or workstation clusters to be set up using virtualization. Effectively, what was being shown was a new-fangled, Windows-based version of mainframe/terminal architecture. It was presented as if it were something new, and yet the underlying concepts were exactly the same as whay my dad was doing at Sperry/UNIVAC 40 years ago! At that same conference, cloud concepts were demonstrated, but you could have pulled the word "cloud" out of the paper and replaced it with "timesharing" and the paper could have just as easily have been written about MIT's 1960's project CTSS.
In my view, there are certain foundational concepts or architectures, if you will, that transcend the passage of time. The hardware will get faster, more sophisticated, more capable, but the underlying implementation concepts remain largely unchanged. Certainly there were game-changing technologies that invent their own framework or foundation, but those are few and far between.
That said, there's no shame in building a better mainframe, even if it's not sexy to call it that.
I really admire the managers who oversee the IBM mainframe division. Those folks have not only technical skills but also exceptional political skills. They did not get comfortable with their success but saw the advent of the PC and figured out how to protect its product line. They have set a marvelous example for all of the young turks who think that product longevity rolls in on the wheels of inevitability. Lotus 123, Visi-Calc, Word Perfect - a lot of products had their 15 minutes of fame. But the mainframers dug in and made survival a part of the product line. Yes, I was one of those who waited for the demise of the mainframe. But those folks fought for their job security and kept themselves relevant and are still looking for ways to stay relevant. That requires technical and political know-how. They serve as a model for the young product makers of today - find ways of keeping your product relevant - know your business environment - do the R&D - understand what people need - look for ways to integrate your product with other products - stay hungry. This reminds me of an old saying 'In the jungle, whether you are an antelope or a panther, if you stop running, then you will not survive'. The IBM mainframe crew have never stopped running - mega kudos to them!
Some things never die. Everyone still has a calculator lying around the house, heck I even have a TI 10-key adding machine I could never part with, particularly helpful during tax season. I'm not surprised that the rust has been scraped off the Big Iron with the rise of Big-Data and the demand for number crunching is heightened. Now, if the power grid can keep pace.
So, what's next? Time to dust off my abacus skills?
My wife is Japanese and she grew up using a soroban and she was speed lightening. You can still find many older mom and pop shops and restaurants using them to calculate their customer's bill. Regarding the profile for a skilled abacus user, they should have nimble fingers, a quick brain, and an underlying comprehension of math and numbers. I found this simple virtual soroban that should help anyone's Sudoku skills.
@callmebob, the art of the abacus... i can see this is no kindergarten learning tool! I'm sure it's amazing to watch your wife or other abacus experts in action. I'd be lost, no doubt.
LEADERS FROM THE BUSINESS AND IT COMMUNITIES DUEL OVER CRITICAL TECHNOLOGY ISSUES
The Current Discussion
Visual Analytics: Who Carries the Onus? The Issue: Data visualization is an up-and-coming technology for businesses that want to deliver analytical results in a visual way, enabling analysts the ability to spot patterns more easily and business users to absorb the insight at a glance and better understand what questions to ask of the data. But does it make more sense to train everybody to handle the visualization mandate or bring on visualization expertise? Our experts are divided on the question. The Speakers: Hyoun Park, Principal Analyst, Nucleus Research; Jonathan Schwabish, US Economist & Data Visualizer
To save this item to your list of favorite AllAnalytics content so you can find it later in your Profile page, click the "Save It" button next to the item.
If you found this interesting or useful, please use the links to the services below to share it with other readers. You will need a free account with each service to share an item via that service.
John Cassara, a former US Treasury special agent and now an industry adviser to SAS, explains why text and social media analytics are the next big thing.
Elizabeth Barth-Thacker, a BI and informatics technology manager at Humana, tells us how her team is creating data transparency and building engagement with the business with the help of an internal collaboration portal called Humanalytics.
Speaking at SAS Global Forum Executive Conference, Rajeev Kaul, SVP of pricing at OfficeMax, uses a Chinese proverb to explain one of the reasons he's deploying SAS Visual Analytics.
In an All Analytics interview, Mike Cavaretta, technical leader, predictive analytics at Ford Research & Advanced Engineering, shares how big-data is fueling vehicle decisions.
Analytics professionals and SAS executives share how organizations can get on with their work so much faster when working in a high-performance and visual analytics environment.
With today's advanced visual analytics tools, you can stream data into memory for real-time processing, provide users the ability to explore and manipulate the data, and bring your data to life for the business.
Dynamic data visualizations let analysts and business users interact with the data, changing variables or drilling down into data points, and see results in a flash. Advance your use of data visualization with tools that support features like auto-charting, explanatory pop-ups, and mobile sharing.
No doubt your enterprise is amassing loads of data for fact-based decision-making. Hand in hand with all that data comes big computational requirements. Can traditional IT infrastructure handle the increasing number and complexity of your analytical work? Probably not, which is why you need a backend rethink. Big data calls for a high-performance analytics infrastructure, as Fern Halper, a partner at the IT consulting and research firm, Hurwitz & Associates, discusses here.
Redbox's bright-red DVD kiosks are all but ubiquitous these days, located in more than 28,000 spots across the country. Jayson Tipp, Redbox VP of Analytics and CRM, provides an insider's look at how the company has accomplished its phenomenal nine-year growth.
InterContinental Hotels Group (IHG), a seven-brand global hotelier, has woven analytics into the fabric of its operations. David Schmitt, director of performance strategy and planning, shares IHG's analytics story and his lessons learned.
Tweet This
15 
17 
