Finding From Systematic Review Longitudinal -Myassignmenthelp.Com

Question: Discuss About The Finding From Systematic Review Longitudinal? Answer: Introduction Current developments in computational statistics and data collection are coupled with rising computing processing power. This also includes plunging costs of storage and makes technologies efficiently analyze a huge set of heterogeneous data found everywhere. Big data technologies have been raising serious ethical, privacy and security issues, left unaddressed and needed to survey. Key issues underpinning the above example: The above areas of surveillance bring about various issues. This includes finding a signal in noise, data silos, data inaccuracy and fast movement of technology (Kim, Trimi and Chung 2014). Evidence of the above problems: Big data refers to a huge volume of data which is structured and unstructured inundating business on a daily basis. However, it intensifies specific trends in surveillance related to networks and Information Technology. Having more data has not been any substitute to have high-quality data. For instance, an article in Nature reported that the elections pollsters of US have been struggling to gain representative samples of the population (Kitchin 2014). This is because they have been legally allowed to call landline phones, whereas people of America have been notably depending on cell phones. One can seek many political opinions on social media. It has been never reliably representative of voters. A significant share of Tweets and social media posts regarding politics are generated through computers. One current report from CapGemini stated that experience of digital customers has been all about knowing and understanding customers. This indicated harnessing every source. This meant assessing every contact and linking external sources like commercially available and social media data. It has been all about gathering, examining and interpreting data from digital supply chain from a myriad of connected devices (Jin et al. 2015). Finding signal in noise: It has been hard to get insights of a vast amount of data. For using Big Data, Maksim Tsvetovat, data scientist and writer of the book Social Network Analysis for Startups, a statement can be taken. He states that there needs to audible signal in the detected noise (Hilbert 2016). Many times, it is not just one. As intelligent surveillance is done on the data, people need to return and say they never measured the right or wrong variables. This is because there has been nothing one can detect here. In raw form, big data seems like a hairball and a clear progression to this data is needed. The article further states that it is approached and the person approaching behaves like a scientist. This means the hypothesis is failed. Some more hypotheses are needed to come up with. Perhaps, one of them could turn out to be correct. Data silos: They are primarily kryptonite of Big Data. They store every smart data that are captured in disparate and separate units. However, they have nothing to do with other groups. Thus they have no insights that can be collected from the data. This is because it is not merely integrated at the back end. These data silos are the reasons why one needs to number crunch for producing any monthly sales report. Moreover, this is the cause that any C-level decision is made at snails pace. It makes marketing and sales team just not get along (Chen and Zhang 2014). Further, it makes customers look elsewhere for moving the business ahead since they never feel needs are being met. To eliminate data silos data is to be integrated. Data inaccuracy: It is seen that data silos are ineffective at an operational level. Further, they are also a fertile, productive ground, regarding substantial data problems or data inaccuracy. As per recent report established from Experian Data Quality most of the businesses think that their customer-contact information is proper (Archenaa and Anita 2015). However, as one possess a database with full of improper customer data, one might have nothing at all. The most effective method to combat data inaccuracy is to eradicate data silos through integrating data. Fast progress of technology: Vast corporations of current data are prey to the data silos. The reason is that they prefer to keep databases on-premises. Moreover, the decision making regarding new technologies has been very slow in such case. To demonstrate this one instance of CapGemini report can be cited. It showed that substantial disruptions from new competitors have been coming up from various other sectors. This problem is mentioned by over 35% of respondents in every industry. This is comparable to the overall average of below 25% (Kitchin 2014). Essentially conventional players have been slower in moving on technological advances along with finding them being faced with various severe competition rising from smaller companies due to this. Key issues for government: The technology of big data is relatively a new concept. Though it has possessed many benefits, issues to using big data have been existing public sectors also. One of them is cost. Various agencies have been paying a high premium to both their internal resources and the external parties for controlling their information (Clarke and Margetts 2014). Further, data management has been at many times redundant as not set up properly. Here, for instance, any agency might translate documents and feeds of foreign social media. Through assimilating intelligent solutions coupling big data, like compatible translation tools, a government can turn out to be more efficient with spending. Other issues with big data have been rising with inherent regularity nature in the public sector. Very often regulations have never been taking into account the expanded new capabilities that have been offered by IT. Department of commercial enterprises has been presently working to set up IT governance for better managing their computing assets in lean resource environment. The government has already possessed all types of current laws and regulations in place. The rules have been strong many times. It has been difficult to push forward with the IT initiatives (Reed and Dongarra 2015). Though with enthusiasm and know-how, the process of approval can bottleneck pace at which new departments gets deployed. This has been at many times causing government agencies in running 50 to 10 years beyond the IT adoption of the enterprise. For example, PrimeCrime unit was closed due to severe outcomes of the wrong analysis. However, in the real world, the genie of big data is out of a bottle and would not be going away sooner. Despite this, humanity never needs to live with this status quo. Misuse of data could amplify and perpetuate inequality (Al Nuaimi et al. 2015). Hence, significant data possess more potential as a tool for a positive social effect. It can develop educational outcomes and interconnect folks to resources such as loans, financing and mental health. Moreover, it helps in ending food deserts, decreasing traffics and improving environmental conditions under polluted neighborhoods. Regarding privacy, there have been various implications. They are discussed below. Implications Discussion Protecting transaction logs and data New challenges are being posed to storage of big data since auto-tiring process never keep track of the location of data storage. Validating and filtrating end-point inputs Different end-point devices have been the primary factors to maintain big data. Necessary, processing and storage tasks are done with the help of input data. This is supplied by end-points. Protecting and securing data in real time Because of an enormous quantity of data generation, most of the companies have been unable to control daily checks (Janssen and van den Hoven 2015). Protecting access control method encryption and communication A secured device of data storage is a smart step to protect data. Since devices of data storage are vulnerable, it is mandatory to encrypt access control methods. Issues of impacts on human rights The problem has been that one is merely unaware of every positive and negative effect brought by Big Data. This has made that difficult to undertake informed decisions. However, it is unknown that how computation, design thinking and science has been influencing conventional interventionist, protectionist, economic and legal frameworks (Wamba et al. 2015). Further, it is not clear whether similar technologies changing different aspects of social and commercial life possess the potential of addressing justice, empowerment and human suffering. Currently, various development and humanitarian communities have gone through approaches that are data-driven, interventions and innovations. Despite that, multiple issues have been emerging regarding distinct topics, policymakers, human security practitioners and human rights. As information and data have always been vital for all these areas, nature of networked and digital technologies and their ability to store collect and assess data has been developing very fast (Hashem et al. 2016). Apart from above, Big Data incentivizes more data collection and longer retention. Again, in economic terms, it has been accentuating information asymmetries of government and allows for people to get manipulated. Moreover, it has been emphasizing power differentials among people in society though amplifying present benefits and drawbacks. It is seen from the above study that big data as an essential contemporary surveillance issue for a government has made the system more save money, more efficient and identify fraud. It has been helping public bodies better serve the citizens. Data has been enabling a government to perform existing activities more cost-effectively. Recommendations: The following recommendations are helpful for understanding the likely effect of Big Data analytics on criminological research and policy. Maintain Security in Distributed Computing Framework: There are two methods to ensure trustworthiness of worker computers. The first one is truth establishment where workers are stringently authenticated and provided access to properties by masters only. The next one is Mandatory Access Control. Here every worker is constrained to a limited set of tasks. Security Practice implementation for Non-Relational Data Stores: Data integrity must be forced through application or middleware layer. Encryption must be used every time where data has been in transit and rest. Preserve the Privacy in Data Mining and Analytics: Privacies for legal causes should be preserved for monetary and system performance. Since a legal requirement varies from nation to nation, it is critical to comply with policies of countries where the activities are done. Encrypted Data-Centric Security: Identity and encryption based on an attribute are used to enforce access control over distinct objects by cryptography. It can encrypt plaintexts such that only one entity with particular identity could decrypt text. Attribute-based encryption is implemented similar control over attributes rather than personalities. An efficient homomorphic encryption scheme can keep data encrypted though it has been worked on. Besides, another tool useful to maintain privacy is Group Signatures. This allows individual entities to access data. This can be identified publicly only as a section of the group. Granular Access Control: There is complicacy to track and implement big data environments, where scale is so massive. Here it is recommended to decrease complexities arising from granular access controls at an application level. Secure Storage and Transaction Logging: Technologies to deal with some challenges have turned into more robust responding to demands of big data. The encryption is a vital part to maintain integrity and confidentiality of data. Further, digital signatures utilizing asymmetric encryption, regular audits and hash chaining has helped to secure data. Granular Audits: This can be started by enabling options for logging for every component assuring completeness of information. It has been including applications at every layer that includes operating systems. Data Provenance and Verification: It is also recommended that persistence of data-independency must be satisfied while upgrading provenance graphs. Endpoint Input Validation and Filtering: The solution has been taking two approaches. One is a prevention of tampering and other is filtering and detection of compromised data. Apart from this, it has been virtually building a complicated and extensive system that has been entirely resistant towards tampering. Real-Time Security Monitoring: It can be used to recognize threats. It has been including differentiation of actual threats from false positives. A framework of big data requires include analysis and controlling tools. As they could be available under a structure, these devices could be placed in front-end system. Their task is primarily to supply analytics needed to analyze feedback for identifying threats. References: Al Nuaimi, E., Al Neyadi, H., Mohamed, N. and Al-Jaroodi, J., 2015. 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