Is Big Data Vs Thick Data or Big Data Complements Thick Data?
Hey Guys,
In this article, we discuss about big data and thick data. These terms look some time confusing. Is big data vs thick data or they complement each other? So, lets explore in detail about these buzz words.
Big data has become a buzz word in the data driven and IT revolutionized word. In today’s world everyone is talking about big data and businesses are running around big data. For example, entertainment giants like Netflix, Spotify, Amazon Prime etc. People feel that the greater number of data means better decisions. But it is partial true. It is well said that garbage in garbage out. Thus, the quality of data is equally or rather more important than the size of data set. The quality of data is called thick data.
Nokia is one of the best examples which did not feel the power thick data and heavily relied on big data. In 2005 after launch of iPhone, Nokia employ suggested to the company to feel the nerve of the customers sentiments (that time only small chunk of smart phone users was there). At the point of time, Nokia was on a high. In the year 2006 Nokia had 60% share in mobile market and it was the undisputed leader. Nokia was relying on their previous sales and customer base (big data) but in few years of span Nokia became history in the mobile industry.
So, the most important and signification question arises. Are big data enough? or big and thick Data both needed.
Big data is essential to address a prediction in any business, but alone big data is not enough. Big data rely on quantitative and macro dimension of information to answer what’s happening?
But, to deeply understand human psychology and intentions, the macro and quantitative dimension (big data) has to be combined with a micro and qualitative (thick data). Thus, the combination of both can efficiently address why is something happening? Both big data and thick data are important to from a complete picture they present different types of insights at varying scale and depth.
Many times, a small data that we see and produce an incredible depth of meanings and stories. It is called thick data. It is contrary of big data. Big Data, which big in size, and adhere 3V (volume, velocity, and variety) and leverage new technologies for capturing, storing, and analyzing.
Thus, the integration of big data and thick data may provide organizations a more complete context of any given circumstances. To paint complete picture of the business they need to leverage both big and thick data because each of them produce distinct sorts of insights at varying scales and depths. Big Data relies on humongous, large number of data points to discover hidden patterns from the large data whereas thick data relies on a small number of data points to see human-centered patterns in depth. Thick data depends on human learning, while big data relies on machine learning approach.
Big data uncovers insights from large amount of dataset while thick data reveals the social context of connections between data points in small dataset (qualitative). Some time organization traps in the myths that more data produce more insights, but it is not always true, big data is heavily dependent on quantitative results and overlook the importance of qualitative data.
Conclusion
Using Big Data in isolation can be problematic. Thick data is the best way to know what is unknown. When businesses want to know what they do not know, they need to explore thick data because it delivers somewhat that big data cannot do. Thick data often discovers the unexpected. So, it is good approach to use in the things in complementary manner.
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