IoT Data Analytics
Enhancing connectivity in daily life
When you are outside driving, you may have gone through tough times finding a place to park your car in the crowded city. To solve such inconvenience, in India, IT company A proposed a smart city system that notifies empty parking spots within the city to drivers. It would be a whole lot easier for drivers to find the place to park their cars and would help reduce lots of energy, time wasted. How can this be possible? The answer lies in the rapidly growing technology, Internet Of Things(IoT). Internet of Things, IoT, refers to the network of connected objects that are able to collect and exchange data using embedded sensors. Thermostats, cars, lights, refrigerators, and almost every object can be connected to the IoT network. By doing so, it can increase connectivity in people’s everyday life.
Even though connectivity is the key feature of IoT technology, only just connecting the objects with embedded sensors would not offer satisfying consumer experience on connectivity. To actually build valid connectivity in daily life, companies need to figure out which features consumers want the most in certain situations. However, since big data coming from a large number of IoT devices is beyond the level of usual data analysis or what human brains can do, finding out specific features and situations that consumers want is impossible without the help of big data analysis technique. However, unfortunately, There exist some challenges in utilizing the big data analysis technique.
One of the challenges companies face when analyzing big data from IoT devices is its technical difficulties. To analyze big data, a certain group of people who meet certain capabilities is required. They all should be knowledgeable about the data itself and data scientists must be included who are able to manipulate, analyze the data properly. However, such experts are so rare that, for IoT most device developing companies, it is difficult to get data analyzed utilizing their own resources.
Another challenge stems from in the similar sense as the first one. Since it is difficult to analyze data utilizing their own resources, companies either have a hard time analyzing the data due to lack of expertise or pay a lot of expenses to get the job done. Both of which can do critical harm to the growth of companies itself and, on a broader perspective, to the growth of IoT industry.
Managing Scattered Data at Once
In the past years, employees of K had to go through a lot of procedures and wait for a long time to generate data analysis. They needed to figure out which department was in charge of managing the data they needed. Then they had to request authority for accessing the data, first to the department and again to the bank’s overall database manager. This weeks-taking system of acquiring data was hindering the data from being used effectively. To solve this problem, K adopted Metatron solution. It made data analysis easier by allowing them to manage their entire data sources at once, with Hadoop-based system. This enables integrated management of bank K’s various data such as internal, external, processed and unstructured data. Now employees can perform all the necessary steps from being authorized to analyzing and sharing the result on a single platform with ease and speed.
Better Customer Experience
K now provides customized portfolios by using Metatron. Access ability of information both inside and outside the organization allows them to apply more data in their analysis, therefore making their suggestions more adequate to customers. Quick adaptation to changes is also possible by real-time management. They created 16 categories based on customer behavior pattern analysis. K recommends products liked by similar customers based on these categories. Also, employees can generate advanced analysis by using integration function with tools such as R and Python. As a result, K proceeded further from mere customer analysis to smart marketing.