The past decades have seen the rapid development of Information technology, particularly in world’s data growth. Research by scientist in Barkley – University of California in 2003 revealed that from the beginning of human history until 2002, the world had produced 5 exabytes (5 billion gigabytes) of new information. Surprisingly, we were able to create the same amount of data in 2011 within only 2 days. Finally, we managed to create 5 exabytes within only 10 minutes in 2013. Clearly, the data is growing exponentially (Lyman and Varian, 2000).
In addition, the social network is playing a very significant role in data streaming which contributes to the increase of the amount of data available in the Internet. This huge amount of data, by some people, refers to Big Data. According to Katal, Wazid, and Goudar (2013), Big Data can be defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. With the huge amount of data hanging around in the Internet, this represents the first characteristic of Big Data which is Volume. The other characteristics of Big Data are Velocity, Variety, and Variability. Velocity represents the speed of data coming from various sources. Variety represents various resources and forms of data. Variability deals with the inconsistency of data flow.
As mentioned above, the abundance of data can be meaningful if we can extract the information out of it with the right method. For instance, marketing departments can use Twitter feeds to conduct sentiment analysis to determine what users are saying about the company and its products or services. In this case, customer sentiment will be integrated with customer profile data to derive meaningful results. Another example in politics happened when Big data analysis played a large role in Barack Obama’s successful 2012 re-election campaign. Despite the use of Big Data, it now becomes more and more difficult to perform effective analysis using the existing traditional techniques.
Internet of Things paradigm was first coined by Auto-ID Center from Massachusetts Institute of Technology in 1999 (Patrick and Sweeney, 2015). The idea was to create Internet of Things by using Radio Frequency Identification (RFID) and Wireless Sensor Network to facilitate person-to-object and object-to-object communications. Although many authors have their own definition of Internet of Things, we adopt the definition provided by Gubbi, Buyya, Marusic, & Palaniswami (2013). They defined Internet of Things as an Interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications.
In nowadays technology, this concept can be seen as your day-to-day “things” will be connected to the Internet and share their experience. This can benefit human because when things communicating each other, data can be traveled between them and can facilitate and anticipate people’s need. Imagine this scenario. Someone use a bracelet that can detect his steps, activities, and know how well he has slept. In addition, it can communicate via network. Using this bracelet, our other “things” or objects can start to work automatically when this bracelet share information about its user. For instance, the coffee machine will turn on automatically and create user’s favorite coffee when the bracelet senses that the user has awakened and sends the status to the coffee machine. In addition, the content is on demand and tailored to the user’s preferences.
By looking at both Big Data and Internet of Things, we can see that both concepts are complementing each other. In both concepts, data plays the most important part in the whole ecosystems. When an object or device is doing their activity, the sensors attached on it will produce amount of data that will be recorded and stored in the Internet. In the end, the data are accumulated and contribute to the Big Data. On the other words, Internet of Things will exponentially increase the volume, variety, and velocity of data. Here, the role of IT people is to work closely together with business units to understand their Internet of Things use cases and further provide the right technology that match business’ needs. IT is needed to identify, assess, and evaluate the best analytics platforms and tools in order to deliver the data that business users need, analyze it, and act as quickly as possible.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.
Katal, A., Wazid, M., & Goudar, R. H. (2013, August). Big data: Issues, challenges, tools and Good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.
Lyman, P., & Varian, H. R. How Much Information (2000). Retrieved from http://www.sims.berkeley.edu/how-much-info on 2015, May 30.
Patrick, J., & Sweeney, I. I. (2005). RFID for Dummies.