BIG DATA CAN BE MINED FOR INSIGHTS TO IDENTIFY TRENDS, PREDICT BEHAVIOR , AND EMPOWER DECISION MAKING
The Nepal earthquake in April claimed more than 9,000 lives and left crores of property damaged. While an earthquake is a natural disaster, many of us are wondering why earth- quakes cannot be predicted the same way as Tsunamis or cyclones ? Scientists say it is possible to identify the key regions where earthquakes can occur, but not predict the ex-act time when they would occur. This is because predicting an earthquake means analyzing past data and factors such as high temperatures, gas emissions , strange animal and abnormal cloud formations - appearing along the traditional fault lines. Bringing all these diverse parameters together and analyzing them is a difficult task as the volume and diversity of data makes it tough for analysis.
However, the advent of Big data, has given hope. One company called Terra Seismic says that earthquakes can be predicted 20 to 30 days before they occur . Using Big Data and satellite technology, the firm claims to process large volumes of satellite data taken each day from regions where the probability of an earthquake is huge. This data is combined with a huge number of earthquake precursors ; algorithms are built and analysed to judge the probability of an earthquake.
This means Big data is not just about the data ; the real value is in the analytic and the ability to use that intelligence to gain a desired outcome . Big data can be mined for insights to identify trends, predict behavior , and empower decision making. As we move into an era of the Internet of Everything (IOE) organisations that can find the intelligence in data resulting from numerous new connections can create new sources of competitive advantage. As big data grows, the challenge to achieve better business outcomes will become more complex. Smart organisations in both the public and private sectors must leverage the power of data and analytic to improve processes and profits or to reduce costs and risk.
Going back to the Nepal earthquake one interesting observations is that big data was used to help folks outsides of Nepal locate missing loved ones and to direct disaster aid and supplies to where and when needed. What may be even more interesting, is how ' the crowd ' utilized big data to become a factor in disaster response. Cloud based data companies such as Google and Facebook which generate huge data have also developed specific tools that helped to trace and contact people in disaster areas.
Talking of cloud based data and analytic, in today's era of increasing connectivity, businesses are using the web , social, mobile and other platforms to share their data (read information ) and gather data to understand their standing in the market . Social media platforms like Twitter and Facebook provide several real time analytic capabilities leveraging big data running within their cloud environment for users to gain real time insights.
Likewise several organisations today run their websites on cloud, weblogs are generated in the cloud and analytic can also be done via Had don on the cloud itself. More and more mobile applications now typically have their back end processing in the cloud. As a result , the data that is being collected from the mobile app is in the cloud where the mobile back end resides, and consequently analyzing that data can be easily done there as well.
Clearly several new sources of data to be analysed are already in the cloud. Organisations going forward might actually need to combine their data sitting in different clouds to generate new insights. Technologies concepts like Cisco's Inter cloud which helps connect various clouds will be an integral part of that next step.
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