What 127 Hours Tells us About Social Networks

In an interview this week for Mark Kermode’s Film Review show, Danny Boyle made it clear that he used the success of Slumdog to make a film that might otherwise not have been made. To leverage the success of 2010 most acclaimed film is an indication that 127 hours is more than this years ‘would you?’ movie.

For those few that have not yet heard about it, it is the true story of Aron Ralston who gets trapped under a boulder whilst canyoneering alone in Utah. The desperate measure that he takes to free himself is well documented so it is not giving anything away to say that he was trapped by his arm, he has a multi-tool (a really cheap one) and a little under 127 hours to debate if he should … or should not.

Before, I go on you might be wondering what’s the connection between BI and Social Networks let alone the connection between Danny Boyle’s latest movie and Social Networks. Those that follow my posts and tweets will know that Social platforms interest me because I think they are changing the way we share and use information in business and will profoundly change the Business Analytics space over the coming years. A social platform has already made it into IBM Cognos 10 because these guys, again, are ahead of the game. Many don’t see it yet because the original use of social platforms have trivialised their significance but it’s there nonetheless.

 

Back to the connection. Aron Ralston, played by James Franco, is an all-American hero. He’s young, fit, strong, intrepid and independent. He is good at what he does, he has spent a lot of time in his chosen wilderness and is able to navigate it with speed and ease. In fact, at one point in his story, he briefly but convincingly takes the role of park guide. The hopelessness of his literal and figurative fall takes a long time to sink in for our hero. Indeed, when it does dawn on him that he could have shared his hiking plan with his friends or family it wouldn’t be exaggerating to call it an epiphany. It’s clearly a powerful realisation for Aron that he’s not a hero, he’s an arse.

 

There is a moment in the movie where Aron says ‘thank you’. It’s a strange moment. I don’t want to give away why it is strange but once you have seen the movie, you will know why. For me, it was significant because he knew that if he made it home alive (which was still, by no means certain) then he would be changed forever. He would live the rest of his life in the knowledge that however strong, smart and experienced he was that those tiny connections we all make each day matter. Sometimes in small ways because it’s just about about sharing. Sometimes in significant and surprising ways.

For me, I am continually and pleasantly surprised by what I learn on the subjects of analytics, organisational leadership, productivity, start-ups and social media in my twitter stream. It’s full of links to content that cover important ideas from solid thinkers. Admittedly none of them are life-saving but, at a stretch, a rare few might be described as life-changing. Each of them make a tiny but positive difference and sometimes someone in my network helps me (or me them) in a surprising way.

 

More and more choices for BI Solution Architects

We analytics practitioners have always had the luxury of alternatives to the RDBM as part of our data architectural choices. OLAP of one form or another has been providing what one of my colleagues calls ‘query at the speed of thought’ for well over a decade. However, the range of options available to a solutions architect today is bordering on overwhelming.

First off, the good old RDBMS offers hashing, materialised views, bitmap indexes and other physical implementation options that don’t really require us to think too differently about the raw SQL. The columnar database and implementations of it in products like Sybase IQ are another option. The benefits are not necessarily obvious. We data geeks always used to think the performance issues where about joining but then the smart people at InfoBright, Kickfire et al told us that shorter rows are the answer to really fast queries on large data volumes. There is some sense in this given that disk i/o is an absolute bottleneck so less columns means less redundant data reading. The Oracle and Microsoft hats are in the columnar ring (if you will excuse the garbled geometry and mixed metaphor) with Exadata 2 and Gemini/Vertipaq so they are becoming mainstream options.

Data Warehouse appliances are yet another option. The combined hardware, operating systems and software solution usually using massively parallel (MPP) deliver high performance on really large volumes. And by large we probably mean Peta not Tera. Sorry NCR, Tera just doesn’t impress anyone anymore. And whilst we are on the subject of Teradata, it was probably one of the first appliances but then NCR strategically decided to go open shortly before the data warehouse appliance market really opened up. The recent IBM acquisition of Netezza and the presence of Oracle and NCR is reshaping what was once considered niche and special into the mainstream.

We have established that the absolute bottleneck is disk i/o so in memory options should be a serious consideration. There are  in-memory BI products but the action is really where the data is.Databases include TimesTen (now Oracle’s) and IBM’s solidDB. Of course, TM1 fans will point out that they had in-memory OLAP when they were listening to Duran Duran CD’s and they would be right.

The cloud has to get a mention here because it is changing everything. We can’t ignore those databases that have grown out of the need for massive data volumes like Google’s BigTable, Amazon’s RDS and Hadoop. They might not have been built with analytics in mind but they are offering ways of dealing with unstructured and semi-structured data and this is becoming increasingly important as organisations include data from on-line editorial and social media sources in their analytics. All of that being said, large volumes and limited pipes are keeping many on-premises for now.

So, what’s the solution? Well that is the job of the Solutions Architect. I am not sidestepping the question (well actually, I am a little) However, it’s time to examine the options and identify what information management technologies should form part of your data architecture. It it is no longer enough to simply chose an RDBMS.