In recent years, cloud computing has captured much of policy makers’ attention. With its massive datacenters, the Internet cloud delivers virtually infinite resources, providing the storage capacity to orchestrate massive flow of information and, most importantly, providing an elasticity of computing resources that cuts costs for businesses and government alike.
The cloud’s economic importance to Europe is undisputed, in particular in a time of crisis and financial adversity. The European Cloud Strategy had thus kicked off the Cloud Partnership to foster economic opportunities and initiated efforts to tackle various policy challenges that have been identified.
While policy responses are ongoing, the capacity of the cloud is also driving an explosion in data. Techniques such as machine learning and advanced computer analytics are increasingly important to crunch and gain insight from this so called “big data” and cloud provides the processing power enabling us to tackle some of the world’s toughest problems in healthcare, energy and the environment, scientific discovery, and many other fields. It can also enable better government but, as with many transformational trends, creates policy challenges at the same. Big data is clearly the next big ICT policy debate.
What is Big Data?
The world is awash in data—on one estimate, almost three zettabytes (three billion terabytes) of information had been created by 2012, a digital deluge that is growing at around 50% a year. A unique combination of technological innovation, social media, ubiquitous connectivity and digital globalization, among other factors, is fueling this exponential growth in the volume, variety and availability of big data. The past decade saw a confluence of multiple technology enablers, including drastic improvements in processing power, the ready availability of that power to process data relatively cheaply in massive cloud datacenters, coupled with 1,000x decreases in storage cost, a plethora of smart multimedia capable devices that can be deployed pervasively to collect any data, and the ubiquitous network access that enables the transmission of those data.
By the end of 2012, the number of mobile connected devices will exceed the number of people on the planet. By 2020, there will be 50 billion devices connected wirelessly to the Internet. From 2012 to 2016, machine to machine traffic will grow 22 times to 5x1017 bytes per month (a CAGR of 86%). This means that the majority of big data will be collected passively and automatically, through machine-to-machine transactions, and that users will not be involved in most of the transactions. This has multiple policy implications that will be addressed further below.
How Can Big Data Enable Better Government?
There is no official definition of big data, although many refer to the volume of data being created¸ velocity at which it is generated and needs to be analyzed, and the variety of types of data involved as the defining characteristics. Increasingly powerful computing technologies can take massive amounts of these data, commingle them, and use advanced machine-learning and analytics to gain new insights and knowledge. And we are only at the start of this data revolution.
As the World Economic Forum observes in a new report, Unlocking the Value of Personal Data: From Collection to Usage, these collections of data, and the insights that can be gained from their analytics and aggregations, give rise to an emerging data-driven economy, where the flow of data hold extraordinary potential for new innovations, economic growth, and societal benefits. For example, predictive models developed from large-scale hospital data sets can be used to identify patients who are at the highest risk of being re-hospitalized within 30 days after they are discharged. A recent analysis using Microsoft technology applied machine learning to a large multi-year data set of patient hospitalizations in the Greater Washington, DC, metropolitan area. The resulting predictive model can reveal risk factors that were previously undetectable: for example, if a patient was admitted for congestive heart failure, they were more likely to be readmitted within 30 days if they were depressed or taking drugs for gastrointestinal disorders. These insights have implications that include improved clinical practices while reducing healthcare costs – a policy issue that is at the forefront of many countries where healthcare systems are inadequate to meet future demand.
Data analytics are also being investigated by governments around the world, in both developed and developing countries, to improve policy making – including city planning, epidemic tracking, disaster preparedness, and economic forecasting. Examples of these include:
• The UN Global Pulse, an initiative formed in 2009, is developing a Crisis Mapping Data Taxonomy to explore the use of digital data sources to better achieve development goals. The data sources include online search data, blogs and social media chatter, online news services, and mobile phone data, all collected and analysed in real-time to help understand the socioeconomic well-being of a community. Anticipated uses of these data include population movement patterns in the aftermath of a disaster or a disease outbreak, early warning upon detection of anomalies, real-time awareness of a population’s needs, and real-time feedback on where development programs are not delivering anticipated results.
• In Germany, the Car-to-X (C2X) project is a collaboration between the government, German carmakers, automotive suppliers, communications companies, and research institutes, to explore how vehicles can exchange information with each other and with the road-network infrastructure (e.g., traffic lights) to help improve safety, efficiency and driver convenience. The project began its largest ever field trial in 2012 with 120 network-linked vehicles operating on roads in Germany’s Rhine-Main region. C2X can alert drivers to potential traffic hazards, enabling them to slow down earlier or take a detour, while traffic-light systems can be re-configured in real-time to improve traffic flow.
• Scientists from the Harvard School of Public Health conducted a study in Kenya on the spread of malaria and discovered that it is driven primarily by movement of infected individuals rather than the movement of infected mosquitoes. They did this by combining patterns of mobile-phone usage with information about malaria infections provided by the Kenya Medical Research Institute and the Malaria Atlas Project. This enabled prevention measures that are targeted at specific geographical locations based on travel patterns. According to the World Health Organization, malaria death rates have fallen by 25% since 2000 due to such targeted prevention measures.
Hence, Big Data holds an incredible economic and societal potential that will inevitably transform our lives. Having said that, it is important to point out that this economy can only break through with appropriate policy framework that can unleash its full potential.
Stay tuned for our next blog on all the policy considerations that can enable Big Data.