The Impact Of Contemporary Trends On Data Science

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Analysis of Contemporary Trends in Data Science Introduction It can be said that Big Data is one of the hottest topics in the contemporary information technology. Hot topics in IT comes out every 1 or 2 decades, such as personal computer, internet, become the compulsory transformation choice to organisations. However, big data seems different from the above, partly because big data is not about technology as much organisation transformation. By utilizing the big data, organisations should transform themselves from a history-viewing, batch, data constrained into a predictive, real-time, data hungry environment. Data are created by countless devices including cameras, microphones, mobile devices, remote sensing, software logs, RFID…show more content…
Traditional monitoring uses decision support systems (DSS) which began in the 1960s and developed throughout 1980s. DSS originated from the computer models. After DSS, Data Warehouses, Executive Information Systems, Online Analytical Processing (OLAP) came into prevail from the late 1980s. However, the demands of higher level of intelligence never cease. Campbell, Don (2009) argued that the intelligence trends from 2010s are "green computing, social networking services, data visualization, mobile business intelligence predictive analytics, composite applications, cloud computing and multi-touch." 2. Better perception A better perception of an organisation is a proper way that ‘cook’ the new collected or unstructured data with statistics, analytics to integrate the materials into ideas. For example, The customer support of a retail company observe that certain VIP card members’ purchase and engagement activities have dropped below a certain level of normal activity, it is a recommendation that it is the time to e-mail them a discount coupon. 3. Better efficiency After transformation, organisations could use programmed analytics to optimize every steps of their operations automatically. For examples, from a retail company perspective, they can allocate resources based on the customers’ purchase history, buying behaviour, and even local weather and events directly from the data warehouse, adjust product price based on current buying patterns, inventory levels,

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