The emergence of new data

It is fascinating to realize that a lot of new data comes from old data. Sometimes new data will replace old data because it is more current. However, when old data is categorized and processed, the trends that emerge can be recorded as more data which could possibly be analyzed further.

8-31 reading-response

This passage (found on page 9) in a few words captures the extent to which data has increased over the past several decades. Though, this should not be surprising. Throughout the excerpt of Big Data, the author keeps articulating different situations in which people have used large amounts of raw data to hypothesize trends. These hypotheses can be used to create larger trends, and the cycle can continue. The analysis of the raw data is what catapulted the surge of data the modern generation has now.

One example of this in Big Data was the navigator Matthew Fontaine Maury. He was a navigator that decided to find the best trade routes by going off of the popular approach. He asked everyone he encountered for their knowledge of the seas and the routes they have. He asked old fisherman their secrets for learning the seas so that he could find routes that didn’t fight nature, but rather routes that nature helped along. He collected his own data in order to create his hypotheses because the data he needed wasn’t readily at his fingertips, and in the end created trade routes far superior to the ones previous.

Modern day society has what Maury created for himself, a database just waiting to be examined. People have been able to predict when the price of airplane tickets will be cheapest or track packages all because they used the data in front of them. Big Data fosters the idea that society could have a lot of answers right in front of us that we just haven’t pieced together yet.

One thought on “The emergence of new data”

  1. I think that the answers being “right in front of us” may be an oversimplification of the big data idea. Data needs to be harvested and it needs to be harvested in the correct way for it to make sense. I wouldn’t say that databases are just waiting to be examined simply because many databases aren’t even created if the data was not meant to be analyzed. For example, the program used to collect data about the airline ticket prices did not exist yet to organize a new database in a usable fashion. in the reading it mentioned that many mathematical models are used to correlate certain things with other things; the best example of this was the algorithms that correlated the Google search terms with the H1N1 outbreak locations. It is important to note that the mathematical algorithms were developed based on past outbreaks and shows that these analytic systems take time to develop. If no algorithm had existed before the H1N1 outbreak, there would be no way to develop an effective algorithm overnight to predict the new outbreak. Yes the information is “free for the taking” but using the information in a way that can give us more data takes an incredible amount of time to develop.

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