Technological evolution
In the current organizations, technological evolution provides enablements that make organizations more productive and more efficient. Data is the driver of business operations, and organizations continually realize ways they can use data to improve their operations, increase customer satisfaction, and gain a competitive advantage. Due to the migration of their operations to online platforms, organizations generate a lot of data from the activities that take place on those platforms. Globalization had led to the increased interconnection of devices across the internet that facilitates the sharing of data across devices to find a variety of information that provides insights on better ways that firms can manage their operations. This interconnection is referred to as the Internet of Things (IoT) and is the leading generator of big data. Characterized by high velocity, different varieties, and high volume, big data is data that is rapidly growing, generates in unstructured formats, and exceeds the processing capabilities of the traditional tools to collect, store, and process in real-time. Artificial intelligence (AI) is another growing and related technology to big data and entails the simulation of human intelligence in machines. AI, IoT, and big data use in radical platforms are influencing the operations of a business and driving operational improvement and competitive advantage.
Applications of Big Data on Radical Platforms
Radical platforms are open platforms that allow replication of services, co-design of services, and the use of added value services to deal with sustainability analysis and the governance of the value-added services (Psomakelis et al., 2016). Sustainability, on the other hand, seeks to make the business viable and promote environmental sustainability. This environmental sustainability is evident in the development of smart cities. However, there are other applications of big data in radical platforms to create value and development. Firstly, big data enhances the inferences made from data by providing a large pool of information to add value to a business. Data analytics on these large pools of information guide business on making the decisions that lead to better performance, market satisfaction, and competitive advantage. The results from these analyses help businesses to segment their markets more to meet the varied demands created by individuals from different social classes and personalize their interfaces to avail most searched products. This process improves customer experience and creates their satisfaction, making the firm more profitable. Secondly, big data support for business decisions leads to the creation of insights on how to improve the internal operations of a business. More specifically, where the business applies intelligent software, it facilitates the development of new models that make both the private and public services more efficient.
The growth in technology has led to a drastic reduction in the cost of computing resources, and the increased affordability of storage and processing infrastructure allows organizations to store information at lower costs (Dimitrov & Osman, 2012). Specifically, the lower costs of storage and processing equipment allow firms to purchase more and adequate infrastructure to support the growing needs for data storage. The savings on storage support and maintain the existing infrastructure. It is essential to note the trend in virtualization where organizations are continually virtualizing their storage to leverage the scalable storage offered by cloud vendors. However, cloud storage is also getting cheaper and convenient as organizations save on the costs of physical infrastructure. Also, the increased transparency and efficiency of data sharing improves the analysis of organizational performance, primarily by allowing a better and timelier analysis of the current performance to aid the organization in deciding the best measures it should take for effective integration of the chains in an organization to achieve both economic and environmental benefit.
In macroeconomics, big data is essential in producing intelligent information and converting unstructured data into actionable information in a particular aspect. Internal and public policies rely on the available statistics that inform what is expected, and organizations evaluate the results they obtain from analytics to formulate the policies that suit the best needs. As such, intelligent information assists organizations by making these statistics faster and simpler, saving on resources used by a firm in formulating these policies. Also, big data allows for timely decisions regarding policies by making data analytics and reporting faster. More specifically, it reduces delays when translating from design to execution and evaluation (Peres, 2014).
Similarly, while formulating policies to reduce poverty, data from handheld devices is an indicator of socio-economic levels. This data is generated in real-time and supports real-time decisions on policies that should be modified of implement to cater to arising economic situations to the population.
Netflix is one of the companies that used big data to provide a hundred percent subscription-based model to maintain its sustainability (Dubbeldeman, & Stephen, 2015). Data analytics at Netflix allow the organization to continually understand the needs of viewers and determine the measures to take to ensure that their subscribers keep consuming its services. Netflix used bid data analytics to optimize its markets and make decisions on how to improve their services. More specifically, Netflix offers movies on cloud platforms and whenever one is watching a movie, it can see the content that the user is viewing. From the views of the movie, for example, from the first to the last season to understand the engagement of users. Analyzing that data and comparing with that from users who watched up to a certain season and stopped in the mid-way makes Netflix seek to understand the potential causes of such viewing trends to close the gap and make the viewers like the movie. The company can choose to restart the movie to provoke interest in watching the movie. Besides, Netflix collects the data on the day that one watches a movie and the time that they watched (Castro, Rigby, Cabral, & Nisi, 2019). It also collects data on the pauses that the user made while watching. Through these and detailed analytics of the frequently searched movies, Netflix is able to customize the content it provides to the users. For the company to make the most effective recommendations, it segments the interests of its viewers and tailors content using different determining criteria. Once users access the site, they find content of their interest. This convenience makes the customers satisfied by the experience they get from interacting with the site. There are other companies like Nike, Hitachi, and British Telecom. British Telecom judges the information available online and collaborated with Carbon Trust to reduce the available emissions its value chains. Besides this usage of big data at BT and Carbon Trust, the IT industry has also seen some industries using big data analytics to lower the environmental impacts. These include CISCO that has adopted green computing (Peres, 2014).
Conclusion
Big data is integral to the contemporary organizations because of the enablement it provides to firms for becoming more productive and competitive. The combination of artificial intelligence and big data analytics leads to maximum data utilization to drive business in an organization. In radical platforms, there is co-design of services and use of added value services to drive the sustainability of businesses. Big data has a wide range of applications such as in customer evaluations to inform marketing decisions to improve customer experience and increase satisfaction. Netflix and British Telecom are some of the organizations that leverage big data for sustainability. In addition to these, CISCO is also a user of big data to create a smart city