Summarise Some of the reasons
the author offers as to why using Big Data is popular and also why it may not
be so useful in 150 - 200 words.
Google was able to track the spread of influenza across the US in just a day’s delay, which was much quicker than the Centre's for Disease Control and Prevention (CDC). Moreover, cost for tracking was extremely cheap as it was done by the collation of the digital exhaust of web searches updated in real time. Yet, results were uncannily accurate and fast. This, along with credit card payments and mobiles pinging the nearest phone mast, is a result of Big Data, making it very popular due to the success of the Google Flu Trends. By wising up to the potential of big data, costs can be reduced in factors such as health, through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes. However, there are a lot of small data problems that occur in big data, which was worsened when so much data comes so easily. Furthermore, it aims to merely find statistical patterns in the data, caring more for correlation rather than causation. It also heightens the chances of vast electronic surveillance and threatened personal security. Thus, Big data may not so useful after all.
READER RESPONSE QN: How will Big Data continue to be useful for us? How can we be better at using Big Data?Provide reasons for your answer in one paragraph, with reference to a specific argument in the article, within 200 – 250 words.
Although ‘Big Data’ can be rendered useless upon careless handling, it can still be useful if we choose to heed the familiar statistical lessons. Despite the setbacks faced leading to the failure of the Google Flu trends after the many success, it will bounce back, recalibrated with fresh data. Big Data opens up broader opportunities due to the ease with which we can gather and analyse vast data sets. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of influenza across the US. What’s more, they could do it more quickly than the Centers for Disease Control and Prevention (CDC). Google’s tracking had only a day’s delay, compared with the week or more it took for the CDC to assemble a picture based on reports from doctors’ surgeries. The speed, accuracy and reliability of the results derived from Big Data for the past many winters are still very useful to us. However, there are faults in the Data, and unless we learn the lessons of this failure, and further develop the potential of Big Data, we must not pretend that the traps have all been made safe. A theory-free analysis of mere correlations is inevitably fragile, and thus we must find both causation and correlation. This way, one would have an idea of what might cause that correlation to break down. From here, one can then find a solution for the problem, and receive more accurate data.
Google was able to track the spread of influenza across the US in just a day’s delay, which was much quicker than the Centre's for Disease Control and Prevention (CDC). Moreover, cost for tracking was extremely cheap as it was done by the collation of the digital exhaust of web searches updated in real time. Yet, results were uncannily accurate and fast. This, along with credit card payments and mobiles pinging the nearest phone mast, is a result of Big Data, making it very popular due to the success of the Google Flu Trends. By wising up to the potential of big data, costs can be reduced in factors such as health, through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes. However, there are a lot of small data problems that occur in big data, which was worsened when so much data comes so easily. Furthermore, it aims to merely find statistical patterns in the data, caring more for correlation rather than causation. It also heightens the chances of vast electronic surveillance and threatened personal security. Thus, Big data may not so useful after all.
READER RESPONSE QN: How will Big Data continue to be useful for us? How can we be better at using Big Data?Provide reasons for your answer in one paragraph, with reference to a specific argument in the article, within 200 – 250 words.
Although ‘Big Data’ can be rendered useless upon careless handling, it can still be useful if we choose to heed the familiar statistical lessons. Despite the setbacks faced leading to the failure of the Google Flu trends after the many success, it will bounce back, recalibrated with fresh data. Big Data opens up broader opportunities due to the ease with which we can gather and analyse vast data sets. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of influenza across the US. What’s more, they could do it more quickly than the Centers for Disease Control and Prevention (CDC). Google’s tracking had only a day’s delay, compared with the week or more it took for the CDC to assemble a picture based on reports from doctors’ surgeries. The speed, accuracy and reliability of the results derived from Big Data for the past many winters are still very useful to us. However, there are faults in the Data, and unless we learn the lessons of this failure, and further develop the potential of Big Data, we must not pretend that the traps have all been made safe. A theory-free analysis of mere correlations is inevitably fragile, and thus we must find both causation and correlation. This way, one would have an idea of what might cause that correlation to break down. From here, one can then find a solution for the problem, and receive more accurate data.
No comments:
Post a Comment