Ever heard of a quintillion? No, neither had I until I came across this piece by IBM. It says that every day we create approximately 2.5 quintillion bytes of data. IBM believes it’s a lot. I looked up quintillion to get a feel for how much we’re talking about.
By the UK definition a quintillion is a million to the power five, or a number with 30 zeros after it. It’s also used in English literature to mean a number so large as to exceed normal description. I guess that was before IBM did its number crunching. For American readers, there is a difference in the definition when one crosses the Atlantic. The US regards a quintillion as a number followed by 18 zeros.
It’s why there is a Big in Big Data and looks like a reasonable judgment call regardless of which definition you chose.
IBM doesn’t clarify which definition they’ve used, which is a pity, but they do have a more practical concept to illustrate the accelerating speed of data production. Apparently, “90% of the data in the world today has been created in the last two years”.
This data comes from all kinds of places, particularly anything online and everywhere you could imagine you might put a sensor. That includes social media, purchasing transactions, digital pictures and videos, feeds from devices of all kinds, including mobile phones, and for everything, it includes their GPS signals.
The Big Data concept includes structured and unstructured data and implies such an astronomical size that it’s beyond the ability of commonly used data processing software tools to make sense of it. It’s high volume, high velocity, highly varied and can be complex to transform into meaningful insights.
One definition of Big Data adds that it needs “a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex and of a massive scale”. Thankfully that is changing, with a growing number of tools, such as Apache Hadoop, machine-learning Spark, NoSQL database tools and others, available to tame Big Data to allow big utility.
In healthcare this helps improve disease surveillance, outbreak modelling and predictions, predictive disease management, medication adherence management, prescription refill management, population health analytics, optimising medical insurance and planning emergency services.
For those of us in African countries it’s an opportunity to move into a position where we can exploit Big Data for our own health systems strengthening and transformation.
Now that we’ve reached a few quintillion, I’ve been wondering what’s next. Fortunately there’s no need to worry. We’ve lots more big, fun numbers to learn, such as a “Googol”, which is ten-to-the-power-of-one-hundred, and many more beyond.