Some Small Thoughts on Big Data

According to IBM

“Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data.”

And after spending the week running around investor conferences it seems that big data is big business, but it also seems that we are only scratching the surface.

While IBM’s vision of big data is unsurprisingly focused on the enterprise, using better analytics to predict customer behavior or make better decisions, it raises the question about whether better data driven analytics is the only use for this. Already we are seeing a rash of start ups working with the rather unimaginative application of using big data to allow people to sell things to us – OK there’s a business model there as brands are willing to pay for innovative marketing – but that’s hardy a revolutionary business model these days. Monitoring trillions of tweets to get real time brand awareness information is just an extension of what we have been doing for a decade on the web, and monitoring financial transactions for fraud or opportunity, and oh, yawn…..does the future always have to come in tiny incremental steps? History shows that innovation comes in big disruptive lumps.

So where are the opportunities for big data?

When we look at big data we see two issues where disruptive models can be developed, combining data and creating better data.

By combining data we can use pre existing data sets and combine them with real time information. Areas such as improved diagnosis of disease, where decisions are made by an individual on the basis of their expertise, but with no way of accessing the millions of similar accurate or false diagnoses that would allow them to make a better decision. A couple of applications that immediately spring to mind would be:

  • Combining genomics with biometric security to predict neurological disorders – the computer you sit in front of for most of the day will probably know more about your behavior than you do, and this can be an important predictor for a wide range of disorders;
  • Combining real time MRI/PET/CT scanning with millions of patient records to improve tumour detection – stage one was being able to process the data on the fly so that radiologists could visualize features of interest, but combining that with millions of patient records could massively improve the accuracy most scanning techniques.

But as anyone using the maps in iOS6 will know, the applications are only ever as good as the data they are based upon, and getting that data is still a challenge. As we move towards the ‘Internet of Things,’ and most of these things will be sensors of some kind, we will have more and better data about everything from the environment to personal behaviour.

As with the medical applications above, the winners, the game changers and the disrupters will come from companies that look beyond the software, and create integrated data acquisition and analysis businesses that control the source of the data as well as its end use.

Software is great tool, but you still need to think about the hardware and the context in which it used, so a triple play of ‘Internet of Things’ + Big Data + Cybersecurity could be a game changer, with many of the low power low cost  ‘things’ being enabled by nanotechnology and organic electronics



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