Scope for exploiting Big Data and Big Data Analytics in the local Essay

Scope for exploiting Big Data and Big Data Analytics in the local transport industry - Essay Example With great developments in information and communication technology, most of the data produced every day is generated by people all over the world through social networks; however other types of important data are collected using cameras, GPS equipment, satellites and other devices for many uses. Over the last decade, business strategy has become increasingly dependent on information about potential customers and their characteristics. This data is obtained from the huge collection of data referred to as Big Data through processes like data mining and analyzed to help in business strategy. Analytics is the other method of collecting vital consumer information and it involves real time tracking of consumer characteristics. This paper examines how Big data analytics can be used in the transportation industry to improve quality of service, add value to services and develop applications that will enhance service provision in the industry and reduce loss of time and money. The study has d efined Big data and some of the theories that enable its application as well as examined the benefits and challenges provided by big data analytics in the transportation industry locally and globally. 1.0 Introduction 1.1 Background More data is currently being generated worldwide than at any other point historically. Over the last five years, the volume of data generated globally is estimated to have increased by a factor of six to over 1000 exabytes (Dumbill, 2012). The ‘digital’ universe is expected to reach 8 zettabytes by the year 2015. In general the data explosion is projected to increase with time especially with new data types being developed and increased access to networked devices all over the world including smart phones and geo-positioning devices (Woo et al., 2011). The data being accumulated comes from a wide range of sources. However, the data growth is driven by two main sources working together with decreasing storage costs. The first source for data is the â€Å"internet of things†. A number of sensors collate information on our activities and environment on a daily basis. These connected devices contribute substantially to the amount of information accumulated daily and they are projected to rise from about 4.5 billion devices in 2010 to over 50 billion in 2020 (Dumbill, 2012). The second greatest source of data is the social web of networks where information about human activities is shared on a daily basis. This includes data about human preferences, interests, and locations. On addition to the two major sources of data highlighted above, there are a number of other private sources including hospital records, phone communications, financial transactions, information captured on CCTV and many others. The McKinsey Global Institute has termed big data as the next frontier for competition, innovation, and global productivity (Manyika, 2011). The analysis of masses of unstructured and semi-structured data which some time a go would have been considered prohibitive in terms of time and money is now considered the next step towards business advantage. One of the reasons why this data has turned out to be very important is that great insight can be gained from the data by monitoring the patterns of human interaction. One of the areas in which big data displays great potential is the transportation industry. This is an industry which increasingly showing great requirement for an industrial big data platform. With increasing urbanization and expansion of many cities across the world, traffic management and related challenges are getting bigger by the day. In some of the largest and more congested cities in the