Climate change endangers the lives of millions of people. Its negative impacts, such as flooding in coastal areas, poor harvests in agriculture and decreasing habitat areas for animal life like polar bears, are becoming more and more visible today. But climate change, which is basically an increase of the measured worldwide mean temperature, brings some new opportunities to humankind as well. For example, the shipping passage on the northern coast of Canada and Russia opens for several months, helping to avoid transportation costs from Asia-Pacific to Europe. The meltdown of the ice shield of Greenland brings the possibility of drilling for rare earth metals like Scandium, Yttrium and Neodymium in some regions previously hardly reachable—and such resources are heavily needed in the computing and smart devices industry.
One effect, then, of the decreasing ice panzer (a very thick ice shield) of the northern polar region is the possibility of drilling for oil and gas: an extended period of two to three weeks of summer brings the opportunity to exploit oil for a longer time. No doubt, the oil industry in America and in countries like Russia or Norway is seeking for ways to increase its national income from those natural resources. The exploitation of oil in the northern polar region nevertheless remains a challenging task. Those firms running oil platforms have to take hard weather conditions like storms, heavy seas and large icebergs into account. In a worst case scenario, an iceberg could hit and damage an oil platform, making a huge impact on the life of humans and the environment.
This is the point where big data and analytics can help. Weather measurement stations at the ocean’s surface and observation by satellites in the earth’s orbit in various wavelengths can create a big enough local grid for precise weather forecasting as well as for determining the sizes and kinetics of icebergs. If the calculated trajectory of a huge iceberg shows it colliding with the oil platform location—and if a warning is given in an appropriate time—we can take precautions like moving the oil platform out of the way. There are numerous variables to track, including the measurement points defining the grid and the number of physical parameters collected for calculating the near-term local weather forecast and trajectories of icebergs constituting a potential threat for a given oil platform. Thus one may have petabytes of real-time data that needs to be transmitted, analyzed and brought into a form a human can read, understand and use to make informed decisions.
I use the example above, of an iceberg smashing an oil platform, to illustrate the creation and usage of big data. This idea can be extended to many diverse areas like stock exchange trading, exploration of outerspace, cancer treatment and fighting terrorism. While we can imagine the security issues related to big data in the case of icebergs and oil platforms, such concerns become more visible when we consider the manipulation possibilities in trading stocks or fighting terrorism. Just imagine a trader for metals getting the false information that large reserves in new copper fields were recently detected, which will probably decrease the worldwide copper price, affecting many industries processing copper. Or take the example of unmanned drones capturing images and other communication information in a hostile country and transmitting them to a central place. If a large number of images and other information is transmitted unencrypted it may be easily visible to the enemy as well, or even be open for manipulation.
Today, data is being collected from open sources like social media, by sensors like those in weather stations and airport scanners, or in other detectors like web cameras. Quite often a huge percentage of this data is unstructured. In some respect this means that additional effort is required to make sense of it and therefore it has a kind of built-in encryption. On the other hand there has already been a huge investment to create and collect the data, such as sending satellites into the earth’s orbit, sending out unmanned drones, setting up detectors and measurement stations and so on. If petabytes of data are being collected and need to be analyzed in near real time, any security consideration comes as an additional burden since encrypting and decrypting data consumes additional computing power and therefore increases the necessary investment into computing equipment while slowing down the real-time analysis capability. We could say the same for raw data storage, which would again increase the costs significantly for creating a very large and responsive storage capability.
Big data security is an emerging field for investigation and, in my eyes, a potential large future market. As we gain more access to big data flow and computing power any interested party can extract and manipulate data or take the appropriate actions. An expensive drone may just bomb mountains or empty houses without killing any terrorists; a stock exchange trader may sell all his copper options fearing a price decrease; or an iceberg may in fact smash the oil platform if security considerations are not made while collecting and processing big data. In each case, we need to seek a sound balance between the speed of data collection, processing in near real-time and data security.
Dr. Turgut Aslan is IBM Cloud Managed Services (CMS) Security Development Workstream Leader in R&D Germany. He joined IBM in 1999 and has more than 14 years of in-depth experience in the IT security and compliance areas.
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