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Putting big data analytics to work is not an event, it is a process. And it is a process that is constantly changing. It’s important to be aware of the ways in which that change could leave your organization behind if, like a dinosaur, it does not evolve to keep up.
There are very few organizations that are not facing a huge growth in data volume. A large factor in this growth is the increasing numbers and types of touchpoints where data is generated. Take cell phones, for example: in a world of 7 billion people, there are some 6 billion cell phones. And this is far from all. Smart electricity meters, new types of medical monitors, vehicle telematics: The list of data sources is literally endless, growing and unlikely to stop any time soon. One projection estimates that there will be nearly 19 billion network connections by 2016. Welcome to the Internet of Things, with all of its implications for your big data solutions.
The velocity at which the data is captured and needs to be analyzed is also growing. For example, the New York Stock Exchange captures one terabyte of data every day. The speed with which computer systems can decide to make a trade leaves human traders in the dust. An algorithmic trading system that is just one millisecond faster than the competition’s could bring in an extra $100 million per year, according to one source. And with increased power and speed of computers, networks and data stores, you can be sure this acceleration will continue.
So, how well-evolved are your big data analytics solutions to cope with all of this? Perhaps you are just starting out on your big data journey. Or maybe you have implemented analytics using Hadoop which, at least until recently, was the latest and greatest framework for big data analytics. But now, ready or not, here comes Spark. Not only is data evolving, but the very frameworks created to manage and analyze the data are evolving too. How do you ensure that you are not left behind by this evolution?
It’s important to have an infrastructure that can handle these challenges. For starters, a solution such as IBM Platform Symphony can help coordinate applications across a shared infrastructure, eliminating resource silos and yielding faster time to results while helping reduce capital expenditure on infrastructure. Are you looking at Docker? The latest version of Platform Symphony supports Docker integration. And if you are following – or even participating in – the exponential growth of interest in Spark, IBM Platform Application Service Controller can help you integrate your Spark applications into your existing infrastructure. Our solutions have been proven over many years, and we continually update them to support our customers’ evolving needs.
When the world around you is changing, getting left behind leads to extinction. Dinosaurs come in all shapes and sizes. Take a good look in the mirror for any signs that you are a big data dinosaur, and make sure you are ready to evolve along with the big data world in which you live.