This article was written by Kate Jackson-Maynes, Cheng Lim, Michael Swinson, Annabel Griffin, Smriti Arora and Lauren Murphy.
10 points on data and how it will transform the fabric of every industry
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1. Data is an abundant resource...
In the new information economy, we are creating more data than ever before through our connected behaviour, our sharing of information, video and music on social networks and our spending patterns. And we will create even more data when the 20 billion connected devices that we use or interact with come online through the Internet of Things (IoT). “Big Data” refers to these very large and complex data sets (characterised by the 3 “Vs” — Volume, Variety and Velocity) that are difficult, if not impossible, to analyse or process using traditional data processing techniques.
2. ...and, like any resource, data must be refined...
The Big Data “boom” has occurred because of the new, innovative and sophisticated technologies and statistical methods that can store, process, analyse and visualise vast amounts of data to provide valuable insights and correlations that were not previously possible.
3. ...as well as utilised and valued...
Big Data is fundamentally changing the way organisations operate their business, engage with their customers and outperform their competitors. Organisations that are investing in and deriving value from their data have a distinct competitive advantage. Big Data has the potential to bring about dramatic cost reductions, efficiency gains and new product and service offerings. For example, GE estimates that 1% fuel reduction in the use of big data from aircraft engines would result in $30 billion saving for the commercial airline industry over 15 years. By inputting real-time information and making decisions based on data rather than “gut feel”, organisations are able make large changes with less risk. McKinsey estimates that a retailer using Big Data to its full potential has the ability to increase operating margins by more than 60 per cent.
4. ...and is best harnessed when you identify which problems you want to solve.
The problem is not the amount of data, it is determining what you are trying to understand or achieve. Organisations must develop a Big Data strategy that identifies the business outcomes they are trying to attain and the problems they are trying to solve. An effective Big Data strategy is informed by the Big Data sets that are available, and how these can be connected (both technically and legally) to draw inferences and visualisations between data and the problem.
5. Big Data is not new and is here to stay.
Big Data first appeared on the Gartner Hype Curve in 2011 and was at the “Peak of Inflated Expectations” in 2013. But tellingly, Big Data was removed from the Gartner Hype Curve in 2015 because it had moved beyond hype, and in effect was something very real and important.
6. Big Data is no longer confined to the IT back office...
Big Data analytics is not something which belongs in the IT back office but should be embedded throughout all parts of organisations, to explore new ideas, develop new strategies and drive industry with the benefit of data-driven insights and correlations.
7. ...and has huge potential in all sectors of our economy.
Imagine being able to accurately and quickly identify or predict outbreaks of cholera or flu, or being able to design healthcare systems to deliver services cost effectively to those who are most in need. Imagine a world run on smart grids that allows power companies to predict our energy needs and better plan for future infrastructure. These are among some of the most vivid and promising applications of Big Data.
8. Its potential impact is enormous...
Increasingly standardised datasets, efficient dataset linkages and increased data sharing between and amongst government agencies and organisations are all rapidly increasing the potential impact of Big Data. As the Murray Inquiry noted, the challenge is to maintain commercial incentives for developing datasets, while facilitating the release of data where this improves efficiency.
9. ...for so long as people allow.
Managing and protecting the privacy of individuals and their perception of their privacy will be critical to winning their trust. Complex questions around data “ownership” and adequate customer consent also need to be resolved. Big Data techniques can provide insight into highly personal matters, as shown by the example of Target accurately predicting a customer’s teen daughter was pregnant by analysing her buying patterns. Looking further into the future, consumer-driven data decisions may become the norm, and lead to consumers receiving payment for sharing data.
10. However, cyber-security will be a major challenge.
One of the biggest challenges for Big Data will be to ensure the cyber-security of the datasets, the information in them, and the insights drawn from the analysis of them. This risk is particularly high with the IoT, with an HP study in 2014 showing that 70% of common IoT devices had a significant security vulnerability, with Symantec’s 2016 Internet Security Threat Report renamed the IoT the “Insecurity of Things”.