TOK Post: The Flaws of Big Data (Shared Knowledge)

Big Data. It's a concept that my dad talks about a lot, and something that I don't fully understand. According to some sources, big data is "a popular term used to describe the exponential growth and availability of data, both structured and unstructured." Big data is essential to discover trends in business, science, and opinions of people in society. The presence of Internet users and the growing volume and variety of data today makes it important to organize the data into something analysts can understand. 

Big Data is great. In 2012, Target was able to detect one teenage girl's pregnancy before even her father did so. Despite its appeal for predictions, however, big data isn't as universal as it seems. The flaws of big data were shown with the outbreak of Ebola last year in an article, “Why big data missed the early warning signs of Ebola.” The Ebola outbreak that started in March of 2014 was one of the most terrible, devastating cases of the year. The first deaths of an unidentified, severe disease were reported not through the detections of HealthMap, which is a website that collects disparate data sources of diseases, but through a local French-language news report in Guinea. Long before the news report, however, local medical workers and citizens around the Guinea region had reported cases through social networking. So what had caused the sophisticated data detecting HealthMap to miss these early signs?

The problem was this: the early reports were not written in English.

It's a surprise. Although we tend to believe that the world is more globalized and connected than it has ever been before, most of today's data monitoring systems emphasize material written in the English language. The article mentions that the GDELT Project translates global media into English. However, this translation process can cause a delay, and it's not always the case that all media coverage becomes translated. Thus, the early French-written reports of Ebola symptoms in Guinea weren't detected until later by HealthMap. And instead of the revered data of social networking that is emphasized today, the traditional local news was the first to report the start of the Ebola epidemic.

HealthMap's detection of global diseases. HealthMap also has a timeline of Ebola outbreaks.

I think this article is a great reality check on where humans are today in social and technological advancement. It seems that we have tendencies to come to quick to conclusions, rather than meticulously investigating flaws.

As the article mentions, "Instead of trying to beat the international news through massive investments in computer models, we should instead be focusing on listening better." Big Data looks at the broader spectrum of data around us. However, some important pieces of data are found in local communities of small numbers, rather than in large numbers in social networking. In relation to TOK, I examined the relationship with shared knowledge. This example has shown that there are many levels of shared knowledge, and that not all shared knowledge yields results. The shared knowledge of the early signs of Ebola between the local medical workers in Guinea were not yet shared with the rest of the world. Through the news report and subsequent reports, however, the knowledge of Ebola has become a large global issue. I hope that as our society delves deeper into advancements in technology and computer systems, we don't forget to consider everyone on the globe.