Sometimes, just sometimes, what happens in Las Vegas shouldn’t stay in Las Vegas. That was clearly the case this week when TIBCO CTO Matt Quinn took the stage to talk about the myths and realities of Big Data. In Why Big Data Won’t Make You Smart, Rich or Pretty, Quinn provided his perspective on Big Data based on years of experience in some of the biggest data environments around, like FedEx, Nielsen, and others.
Forget the ‘big’ part for a moment, think about variety
Quinn pointed out that most customers are not struggling with ‘big’ data but are instead still struggling with data. In Quinn’s view, it is the complex interactions between customer data sets that cause the majority of the issues. Success depends more on piecing together different data sets across wildly different applications and systems, with variety of data being the key.
In Quinn’s opinion, solving this data ‘jigsaw puzzle’ is often overlooked and tools like Hadoop, while clearly in focus, is just one tool in the toolbelt and can be a clumsy tool when dealing with real-life complexity.
If your company purports to want to innovate and become a market leader, why do they seem satisfied with an average data solution? What’s even worse, businesses unwillingly aspire to be just average while spouting off to employees and customers they are anything but. Companies with average IT solutions will always produce average results and never go from average or good to great.
By now, we all understand the enormous scale of big data and that enterprises have indeed begun to store, collect, and analyze historical data for intelligent action. This basic minimum is not enough, nor will it ever be, as big data continues to wildly increase. The few companies who actually use big data to continuously improve, spot opportunities, and mitigate risks, all in real time, are soaring past those who let data collect dust in databases, under the false impression that collecting the data is enough.
With all the talk about how big data should be used, what for, and why, rarely do we hear about who uses “it.” All the recent buzz around big data is not because data has all of a sudden become more valuable, it’s that people are now realizing and discussing how to use new technologies and architectures to derive value from these large data sets.
Springtime is nature’s way of allowing your neighbors to judge you based on whether your house is in order. The sun’s light shines on that faded deck, the overgrown lawn, and those cracks in the paint that have grown deeper and more extensive in the past few months. While deep down we know all of these chores are necessary, it isn’t until we allocate our resources to check-off some of these duties, and are motivated by shame or pride (take your pick). A CIO’s motivation is not that much different from the driving emotions that provoke you to manage your household. Data piles up in the corners and is swept under the rug. Springtime proves an excellent excuse from some honest accounting and action items.
Pinterest, the social image-sharing board, has been incredibly successful at creating a loyal community of users who love to organize and share ideas with other users. I’m a pretty active user, and if you haven’t started using it, I encourage you to try it out (though I warn you, it’s incredibly addictive). Pinterest does an excellent job of communicating with users in a way that is both engaging and informative. Of course, they always have a little nugget about themselves (new features, a new partner, etc.), but it always includes something for me and something about me.
“What exactly does a new video game release have to do with Big Data?” I hear you ask. The answer is everything.
Now, well into 2013, the concept of Big Data is already becoming an outdated non sequitur. As data increases rapidly, storing huge amounts of data in uncorrelated, separated silos (in database or data warehouse storage) that need to be constantly queried can’t drive any new, intelligent change in a business. In fact, this approach creates even greater challenges. Big Data by itself can’t drive change because it is just a more efficient, more technological way of doing business as usual. Databases that store transaction history are a practice as old as a shop keeper maintaining a ledger of purchases and sales. How is simply scaling that same idea into the millions of entries going to drive any real change in business? That old approach is Big Data 1.0 and it can’t compete with correlated, referential Big Data. Integrating varied information in an individual context, in the moment of customer’s engagement is fundamental to move business forward in any way and has to be the foundation of any conception of Big Data 2.0.
