Applications of Intelligent Video Analytics in the Field of Retail Management: A Study

Background

Few years ago, when big data was in its nascent state it had constricted utilization, even though there were technological products available which could capture data at the magnitude at which Big Data is really capable of working with. The major reason for this paucity was the limited tools available at the disposal of the organizations to understand and explore the information that lay underneath this heap of data. That is to say people knew they had a lot of information under this abundance of data, they knew the importance of Bigdata, they were ready to discover facts; however, they did not know how to extract these facts from the available mound (Adams 2007). This obviously was the most important task at hand for the organization. Even now with the advancement in Bigdata analytics, there is yet a lot of potential learning which is still hidden in plain sight. So, when did the companies steer towards understanding their own, easily available but for long forgotten or unused data? Well! That was right after the recent economic recession (Adams 2010). The week economy forced deep changes into almost every industry and across the globe, the burden of change was heavier on those businesses which were customer facing and especially those that were dependent on bulk purchasing (Spindler 2008). These later group of companies needed to know the expected trends in purchases of their customer segments, to make any changes or decisions on what to do and how to transform their businesses in future, if at all. Using advanced analysis techniques, they can now do exactly this. The study of big data helps them understand the industry trends, and current and future expected states of their businesses and customer expectations (by looking at customer behavior trends). If one wants to know what exactly is happening or going to happen to their business, their customer their industry, one needs large volumes of highly detailed data, and advanced analytics to extract meaning out of the data (Adams 2012).