There is a new generational gap emerging in the world of business, driven by a group of organisations we fondly refer to as “Generation D” — for data, which sits at the very heart of how these organisations think and act.
The bad news is, according to the statistics, chances are you’re not working for one of them. The good news is, unlike many generational trends this is not related to age, so there is hope yet.
We know that data and analytics has the potential to impact how we work each and every day, and is already being used to transform entire industries for the better. Generation D organisations are data-rich, analytically driven, and setting new benchmarks in business performance.
What is the future of visual analytics? Many would suggest you’d need an army of data scientists and extensive investment in hardware and software to find out. Not so! With the re-vamp of IBM Many Eyes to deliver a broader selection of visualization techniques, IBM is once again giving the ability to derive insight from data, to the masses.
When it comes to finding nuggets of insight hidden in sheets of data, many eyes are always better than one!
In 1997, Garry Kasparov, a human, famously lost a chess game to IBM Deep Blue, a machine. There-in started the age of “Man vs Machine” – a hot topic for many boardroom debates and box office movies. What many may not know, is the events that transpired in a freestyle chess tournament held in 2005. Neither Man nor Machine took home the title, it was in fact two men and a machine working in cooperation that reigned supreme and took home the title.
In Shyam Sankar‘s TED presentation “The rise of human-computer cooperation“, he so rightly points out that “brute computing force alone can’t solve the world’s problems“. Least of all, the world’s analytical problems. Which is why Man and Machine must team to drive greater insight.
This post is inspired by my beloved Essendon Bombers. Who, after showing significant promise at the start of the season beating the top contenders for the championship, have since suffered injury after injury after injury, with nothing short of humiliating defeats in the lead up to the finals.
Analytics in the world of sport is not a new concept, and yet many teams are still to realise its full potential. Much like the world of business, sporting teams around the world are looking to analytics to make the patterns and trends in human performance and game strategy that may be invisible to the human eye, visible.
Today I set myself a challenge: How much insight could I get from a data set that is foreign to me in under 10 minutes?
Would it be enough time to evaluate the physical data file, understand structures, formats, columns, names etc? Would it be enough time to rank, sort, group, slice? But most importantly, would I be able to not only understand the data, but draw conclusions about what it represents?
The skies are blue, the sun is shining, and if that isn’t enough to make your smile, it also happens to be a public holiday today. In celebration of The Queen’s birthday, I am….cleaning the house?!
In a flurry of washing, tidying, organising, cleaning, wiping, toilet training (my two year old, not me), I stumbled across something which filled me with fear – the dreaded lone jigsaw puzzle piece! This is not just a single piece of a jigsaw puzzle, it’s the determining factor as to whether a quiet rainy afternoon spent indoors will end in giggles or tears, and therefore it must find a way back to its rightful location.
Herein lies the problem, what jigsaw puzzle does it belong to?