Definition and Evolution of Big Data
The chronology of the term big data dates back to 1977 used by NASA scientists Michael Cox and David Ellsworth. Later in 1998 it was used by John Masey, an SGI data scientist in a paper titled “Big data and the Next Wave of Infrastress”. John’s paper tries to elaborate the volume of data, storage and the necessity for analysis tools. He has contributed substantially in defining the term big data which is similar to the current notion of big data (Chan et al. 2018, 3).
According to Chan et al. (2018), in today’s debate around big data, there is a huge literature and scholars have defined it from different perspectives. The mutuality among them is referring to big data as a vast amount of data, generated and captured in a variety of formats from a number of disparate sources. This high volume of data exists in structured, semi- structured, and unstructured forms that has outpaced the current ability of standard data tools and methods of analysis (6).
Chan et al. (2018) states that there is no universally consensus on a rigorous definition of big data within the industry; however, the term is generally defined by some major characteristics;
such as volume (hugeness of data), velocity (speed of data processing), variety (disparity of the data sources) (6), veracity (disparity and noise in data) and smart analytics (combination of analytical tools with higher computational power). More importantly the way big data is used for different purposes (3).
Historically the advancement of big data during the last two decades has become unprecedented. Currently, it plays a revolutionary role in overall management of both public and private sector. Though, its emergence is a relatively new phenomenon but the tradition of storing big data dates back to the early 1950s. It has experienced relatively slower pace until mid-1990s, mainly due to the high cost of computer and data storage and data networks (Lee 2017).
Lee (2007) further stated that the big data has been accelerated vastly when in 1994 web mining techniques, web usage mining, web structure mining and web content mining were developing. It was absolutely revolutionized between the years 2005-2014 with the prevalence of social media content mining.
Big data entered in a new phase by the development of data streaming analytics, as the analysis of audio and video has become conceivable phenomenon. By its vast expansion, it has paved its way to several sectors such as agribusiness, financial regulations and medicine. (Lee 2017, 298).
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Approximately 90% of the current global data has been created during the past two years, with 2.5 quintillion bytes of data added each day. Interestingly almost 90% of the produced data is unstructured which is usable only by utilization of big data technology (Kim et al. 2014). For better governance, public agencies see the data outburst as a great opportunity. Thus, in recent years the developed nations have invested largely to harness the big data power for their own well- being purposes (Strang et al. 2017).
However, in this study, the term big data is defined as a huge amount of structured, semi- structured and unstructured data that mainly includes documents, natural language, web data, social media data, multi-media data (image, audio, video) and mobile data (sensor, geographical location and applications). It further tends to include the use of big data technology, tools and analytics.To be precise, when discussing big data utilization, this study relies only and merely upon the lawful and legitimately accessible data.
Big Data Application
Private sector has pioneered this area but governments are currently embarking to invest on utilization of big data for evidence-based decision making.Thus, all the leading countries in big data area, are finding it as a strategic tool in fulfilling their mission in making the public service easily and equally available, also to effectively involve citizens in public affairs (Kim et al. 2014).
On the application of big data in public sector, a study conducted in 2017, found that from an overall 17% of the entire publication in big data with management approach, 7% of them focuses on organization and public sector management. This study had analyzed the entire publications on big data from 2005 to the year 2017 (Sheng, Amankwah-Amoah and Wang 2017).
Though, it represents a relatively small percentage but by the advancement of technology, big data industry has flourished far beyond our imagination. It has proliferated and prevailed almost every sector including the areas of crime investigation, comprehension, and prediction.
Similarly, it is widely practiced in the area of law enforcement and intelligence investigations (Chan et al. 2018, 2).
Kim et al. (2014) studied big data in the area of security. Their study indicated that in some countries big data is prevailing the strategies and tools of fighting terrorism and violent crimes.
United States of America (USA) is one of the leading countries in the deployment of big data in its public and security sector and by investing largely in this arena; big data has become one of its national priorities. Apart from other institutions, only the USA’s National Science Foundation (NSF) has a dedicated program with $100 million budget in big data category (Strang et al. 2017).
However, despite the prevalence of big data utilization in today’s world, the use of big data in security sector is lacking the sufficient and systematic literature. Scholars have emphasized
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more on the issues of privacy and civil liberties. Other aspects are yet remaining unexplored. This deficit in the literature is simultaneous with severe implications for the policy makers and researchers, as the absence of a systematic study has posed huge limitations to researchers in understanding the implication of big data advancement in the area of security ((Puyvelde et al.
2017).
This applies particularly to USA as one of the pioneers with several years of tradition in utilizing big data in security sector. Although, a systematic literature in this area could help the policy makers in other countries in understanding the big data promises; which by turn could offer better understanding of the security situation and informing similar policy formulations.
Focus of this Study
This study tends to assess the effectiveness of big data in U.S security sector with emphasis in its potential in identifying, mitigating and predicting the crimes and possible threats. It also looks for evidences that proves big data effectiveness in informing decisions in security agencies.
To do so, it assesses the actual scenarios based on what the U.S security strategic plans and policies have envisioned as big data promises. For better understanding of this fact, it tends to posit big data utilization in a bigger environment; examining some major factors that have greatly contributed to advancement of big data utilization. Subsequently, on the basis of lessons from U.S security context, this study tends to conclude by providing a recommendation for countries where such interventions could be transferred as a constructive policy.
To do so, this research relies substantially on secondary data; existing in the forms of journal articles, books, official reports and online archive materials. This thesis encompasses six sections. It begins by providing some generic and conceptual information about big data. In the second chapter, it describes the methodological details. Subsequently in the third chapter, it provides a detailed account of similar literature and the research framework that this study tends to follow. It will be followed by results and analysis chapters, respectively. And finally, it finishes by listing the bibliographical details of all references used in this study.
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