Big Data Analysis and Nosocomial Infections

Question: Discuss about the Big Data Analysis and Nosocomial Infections. Answer: Introduction: Data is the most valuable asset of organisations in the present-day world (Kroes, 2011). Data is quantity, and almost everything can be quantified, thanks to the decreasing costs and increasing ubiquity of sensors like Internet of Things (IoT), and wearable devices. Also, the volume and velocity of data being created and recorded are increasing every passing minute. This paper looks into big data analytics. To begin our discussions in earnest, let us first explore what big data is. Data is any quantifiable thing. The past generations also worked with data, but they were limited in the creation of data (Mohanty, et al., 2015) especially due to the absence of sensors and devices. Also, the technologies at their disposal for storing and mining the data were limited. In other words, data per se is not new, but the ability to refine it is what distinguishes the present times (Lin, 2015). The human way of living is now generating data from multiple sources (both machines and men), and instead of being discarded, most of it is being archived, either for private use or being released to public domain. Also, advanced technologies like machine learning, pattern matching allow automating most of the labour involved in analysing big data. Thus, big data is characterised by V's of Volume, Velocity, and Variety (Rouse, 2014). Later on the uncertainty of data (in the form of Veracity), and the business value of that data (in the form of Value) is also being included (Mishra, et al., 2016). Analytics means to analyse something to discover trends and patterns, to answer questions about the data, and to gain new synergetic insights where the sum is greater than the parts. When the analysis is done on big data, patterns and insights can be gained which were earlier impossible. The analytic processes and tools are applied to structured, semi-structured, and unstructured data (Beal, n.d.). Throughout this report, we will be touching on real-life examples where big data analysis has provided insights and actionable information to governments, companies, organisations, and individuals. As an aside, the first big data analysis was done way a scientist, using basic tools, much before the advent of digital computers - Copernicus (Bayless, 2016). He meticulously noted details about solar activities (like sunrise, sunset) for decades and crunched that data to come to the conclusion that it is Earth which moves around the Sun and not otherwise. Technically, big data is that point where the 3 V's of volume, velocity, and variety render the traditional relational approaches to database ineffective for analysis (Mishra et al., 2016). Big data was originally a technical problem. Analytics on big data have become feasible in the recent times due to the dropping costs and increasing storage and processing power (Russom, 2011). One of the characteristics of big data is variety. Variety means that the original sources of data are varied, and the data they present may be structured, semi-structured, or unstructured. Structured data is well defined in its properties and usually, follows certain business rules e.g. information filled in forms. Semi-structured data is one which partly possesses the rigidity of formal specifications e.g. email messages usually have a salutation in the first paragraph and a signature (name of sender) in the last paragraph. Unstructured data is one which cannot be relied upon for any order e.g. posts on micro-blogging site Twitter (also called as a tweet) are not guaranteed to follow any syntax or order in their content. Now, analysing big data requires fusing all of the data sources being considered together. This analysis may entail an effort to parse meaningful data from the unstructured and semi-structured data sources. For example, tools like Tweedr and TweetTracker track tweets from disaster-hit areas to prioritise worst-hit areas so that the relief aid can be triaged efficiently (Bruns Liang, 2012) (Imran et al., 2015). Once the data is in a consistent format, analytical techniques are applied to it. Some of these techniques are association rule learning, classification tree analysis, genetic algorithms, machine learning, regression analysis, sentiment analysis, and social network analysis (Stephenson, 2013). How it is used or will be used Big data provides huge amounts of data to play with, and this quantity of initial sample increases the accuracy and the depth of research when analytics are performed on it (Russom, 2011). Big data analytics provide opportunities to uncover trends and patterns where none could have been identified before. Big data is analysed and used by governments, non-government organisations, researchers, businesses, and individuals. The goal always is to end with better results than would be without big data analysis. The users just mentioned use big data for goals of their respective domains. Examples include military goals, monitoring and predicting terrorist activities, as an aid to form policies, finding new correlations, getting a competitive advantage in business, optimising resources in general and specifically in emergencies, improving medical analysis, monitoring and predicting the health of the population, identifying times for social media posting, and so on. A couple of examples are presented next in which big data has been analysed to provide actionable and novel information. For America, data about the home address of prisoners in America, and the home addresses of those inmates who are released was plotted against the government expenditure on those prisoners on the map of America. The resulting figure indicated tight clusters and neighbourhoods where crime and attendant symptoms of poverty were present. This proof gives the policymakers information to utilise public money better and eliminate the crime at the root level (Bayless, 2016). In the medical field, an illustration is a real-time analysis of prematurely born babies vital signs to predict the onset of infections much before the physical signs manifest. This forecast is helping save lives of these exceptionally weak infants (Reddy, 2014). At an individual level, private companies like Buffer can analyse the social media sites' activity level for a particular user and identify the times of the day and the days of the week when a posting by the user is likely to receive maximum attention (Lee, 2016). The advantages or benefits compared with existing technologies (if any) Big data provides huge amounts of data to play with, and this quantity of initial sample increases the accuracy and the depth of research (Russom, 2011). There are lots of benefits of big data analytics. As mentioned earlier in the report, the benefits touch every domain of human living, and big data finds usage from an individual to a nation. Data scientists have likened the analysis power of big data as looking down on humanity with a microscope (Bayless, 2016). Benefits include anything to do with people, general business intelligence, automated decisions for real-time processes, better planning and forecasting, and many others. The examples of benefits of big data analytics include predicting deteriorating health of premature babies, proving the link between broken street lights and higher crimes in certain localities, predicting terrorist movement, identifying the worst-hit areas in case of disasters, identifying the busiest airplane routes, building smart local bus transportation systems, and many other unfolding examples. The disadvantages or potential disadvantages Data scientists agree that there cannot be a world-transforming technology that can only work for the better or worse of humanity. They believe that we cannot have one without the other (Bayless, 2016). True to this belief, there are certain disadvantages or risks involved with the powerful tool of big data analytics. The concerns are mainly about privacy. Data about the weather is one thing, but personal data is another. Also, most of the personal details are being tracked and uploaded onto the servers of private companies without the knowledge of the people. For example, Android mobiles track the whereabouts using Maps app if GPS (Global Positioning System) is enabled. TrueCaller, Facebook, Twitter, and LinkedIn apps on smartphones (after due consent of user) regularly upload the user's address book, messages and other details. On top of all this, is the constant lure of the rewards of big data for criminals. Data hacks are common and both small and big names are attacked. References Andrews, S., Gibson, H., Domdouzis, K. Akhgar, B., 2016. Journal of Intelligent Information Systems, 47(2), pp. 287-312. Bayless, j. H., 2016. The Human Face of Big Data 2O14.(Documentary). [Online] Available at: https://www.youtube.com/watch?v=Ho5CNu6U3lU [Accessed 01 04 2017]. Beal, V., n.d. big data analytics. [Online] Available at: https://www.webopedia.com/TERM/B/big_data_analytics.html [Accessed 1 4 2017]. 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