I recently did a college visit. You know, those visits where you sign in at admissions, the information session is at 2:00 p.m. and then the campus tour follows. You learn all about why this school is infinitely better than any other school you will see. And the tour guides either walk forwards or backwards, depending on the rules at said school. If you’ve tried walking backwards through a campus over an extended period of time you realize this rule is actually not such a bad idea.
As I wove through the volumes in the stacks in the libraries, the classrooms, the dining halls and dorms, and an amazing rec center with indoor track, three swimming pools, and a smoothie bar, one thing struck me (Besides that everyone looks so young). Something that is even more pervasive on campus than mice in frat houses. That one thing was Data.
Wherever you turned, there it was. Millions of books, 50,000 students, 300 degree programs, 6000 employees, thousands of course offerings, 1000 acres, unlimited statistics on sports from football to fencing, $100 million in endowment. Data on everything from how many college logo hoodies in size XL are in the bookstore to videos of a semester of Bio 101 lectures to whether female students with brown eyes from Cleveland like smoothies. Strawberry or coconut.
A few days after my college visit I read that universities are now even using Big Data concepts in many traditional disciplines — such as analyzing literature by statistical parsing and aggregation of thousands of novels. Just so you know, of all authors ever, Jane Austen — one of my favorites — had the greatest effect on all other authors, with her works clustering together in terms of style and theme. I just knew it! In fact, algorithmic methods are truly part of every academic discipline now. It won’t be long until college course names see a major overhaul. History becomes Cliometrics. English Lit is Stylometry. Social Sciences and Humanities transforms into Culturomics.
When we talk about Big Data we are usually talking about the famous 3 V’s. Or the 4 V’s. Or even the 5 or 6 V’s — it depends on who you talk to but almost all versions include some of the following: Volume, Velocity, Variety, Variability, Veracity, Value. The ability to collect, measure, and analyze this massive flood of data for meaningful insights requires important non-functional IT requirements like reliability, availability, security, and of course performance. If my student ID card doesn’t give me my coconut smoothie really fast, I am just not happy.
The IBM announcement today brings together all of this – new products and offerings that leverage cloud to improve efficiency, focus on data to deliver more actionable insight, and secure this critical data to protect and reduce risk. It’s Cloud Ready, Data Ready, Security Ready. And, of course, Performance Ready with:
As a student, I really don’t care where the data resides. I may care that the data is secure, not wanting everyone on campus to know, for instance, just how many coconut smoothies I had this week. But as a young person with a need for speed, I surely know one thing and that is that I want it all really really fast.
Elisabeth Stahl is Chief Technical Strategist and Executive IT Specialist for IBM Systems and Technology Group, and has been working in systems performance for over 25 years. You can reach her on Twitter: @ibmperformance.
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