Introduction
Over the past two decades, access to ‘big data’ has significantly become a strong source of competitive advantage for some firms within the global business environment (Conklin, 2017). As expected, the volume of data and information created by multiple business activities, has been continuously increasing as it is captured by innovations and inventions in the field of computer technology (Davenport, 2013). Despite the large data volumes captured in the processes, one key challenge arises; how can the data be managed for positive results? In this regard, this paper focuses on the means in which predictive big data analytics can be utilized to foster business growth. It argues that predicative analytics should be considered as a vital “hot-issue” within the contemporary environment for entrepreneurs wishing to engage in digital marketing.
Labeling Big Data and Predictive Analytics
Several terms have been invented to label the concepts of ‘big data’ and ‘predictive analytics’. To begin with big data has been labeled as a very big amount of data usually exclusively structured for particular system (EMC corporation, 2013). According to Rainer (2010), big data contains multiple structures and is generally used in database programs to yield the required outcomes. Secondly, predicative analytics has been labelled as the use of statistical data and advanced algorithms to forecast impending events (Conklin, 2017). It involves drawing meanings from data and big data to draw conclusions regarding multiple issues (Sharda et al., 2013). In this regard, both big data and predictive analytics go hand in hand in making business decisions.
Utilization of Predictive Analytics in Established Businesses
Established business have invested in gaining meaningful intelligence from the big data in several areas. To begin with, predicative analytics have been merged into decision-support systems (DSS), where firms ensure that they effectively and efficiently rely on data-driven strategies to achieve competitive advantage (Stair & Reynolds, 2015). Several companies such as Tesco, McDonalds and Unilever have utilised predicative analysis to segment their customers according to changing global trends and ensure that products and services are engineered to meet specific customer needs (Davenport, 2013). More specifically, McDonalds has aligned its customer’s needs with changing global lifestyles and select suitable restaurant locations. Tesco’s centralised DSS system also allows it to seamlessly operate within huge geographical regions following tough financial conditions within the last decades (Boyer & Verma, 2009). Unilever’s DSS system has been used to back global expansion strategies accounting for 57% of its share (Green, 2013). The three companies showcase the effective use of data and predicative analytics within modern businesses.
Utilization of Predictive Analytics in Management Information Systems
Additionally, predictive analytics have been utilised in the creation of management information systems (MIS). More specifically, MIS have been used in the levels of senior, middle and operational management and has been integrated into already existing traditional systems to increase flexibility (Laudon & Laudon, 2012). In the manufacturing sector, this is achieved through the production of reports detailing the number of employees and their work hours, the expected productivity from business operations and expected revenues and financial implications of the operations (Rainer & Cegielski, 2010). In the service sector, organisations have learnt to manage their resources, and meet client needs. In particular, application in the digital marketing sector can allow businesses to be aware of pricing trends (Conklin, 2017), thus enabling them to position their products at an optimum to foster customer satisfaction.
Utilization of Predictive Analytics in Emerging Business Fields
Within the modern business environment, predictive analytics have also been considered as valuable tools for entrepreneurs intending to engage in the emerging business fields. As illustrated by Conklin (2017), predictive analytics have been useful in the fields of content marketing, e-commerce and social media marketing. In content marketing, data-driven campaigns ensure that the right customers are targeted. With respect to e-commerce, enterprises have increasingly engaged with customers to anticipate their needs and act accordingly. Furthermore, analytics have enabled businesses to understand the history of their customers via their purchases (Sharda et al., 2013). In social media marketing, entrepreneurs can utilize data-mining to identify the interests, preferences and purchasing behavior of customers.
Challenges and Concerns
Even through the utilities of big data and predictive analytics have been considered as vital for organizational competitive advantage, it is key to note that their success is dependent on their usage and application. Boyer & Verma (2009) posit that the institution of big data systems and predictive analytics is resource-consuming and requires a disciplined and skilled workforce. Furthermore, drawing competitive advantage from the aforementioned technologies is contingent upon managerial determination and appropriate strategic direction to suit the business intentions. Davenport (2013) argues that in most cases, organizations willing to apply data analytics must be willing to undergo massive environmental change. Other concerns have also emerged. For instance, critics have raised questions regarding the management of increasingly cumulative data. In particular, legal and ethical concerns regarding the usage of data have inhibited the level to which organizations may harness the power of predictive analytics.
Adoption of Predictive Analytics
Despite the issues mentioned above, the adoption of predictive analytics in organizations has been impressive. Businesses continue to invest heavily in systems that can be integrated with the already existing conventional systems used to promote business operations. Even small and medium enterprises have adopted the technologies to gain competitive advantage in various business operations. Given the rapid and dynamic changes in the global industry, combinations of traditional and new analytics have been key for business flexibility to the new global conditions.
References
Boyer, K. " Verma, R., 2009. Operations and Supply Chain Management for the 21st Century. Boston: Cengage Learning.
Conklin, E. (2017, December). How Predictive Analytics Can Help Your Business See the Future (Infographic). Retrieved from https://www.entrepreneur.com: https://www.entrepreneur.com/article/305460
Davenport, T. H., 2013. Process Innovation: Reengineering Work Through Information
Technology. 1st ed. Boston: Harvard Business Press.
Davenport, T. H. " Prusak, L., 2013. Working Knowledge: How Organizations Manage What They Know. 1st ed. Boston: Harvard Business Press.
EMC corporation, 2013. Big Data: Big Opportunities to Create Business Value, Hopkinton,
Massachusetts: EMC2.
Lake, P. " Drake, R., 2015. Information Systems Management in the Big Data Era: Advanced
Information and Knowledge Processing. Illustrated ed. London: Springer.
Sharda, R., Delen, D., " Turban, E. (2013). Business intelligence: a managerial perspective on
analytics. Prentice Hall Press.
Stair, R. " Reynolds, G., 2015. Fundamentals of Information Systems. 8 ed. Boston, MA: Cengage Learning.
Tesco PLC, 2018. Five year record. [Online]
Available at: http://www.tescoplc.com/index.asp?pageid=30
Unilever, 2018. About Unilever. [Online]
Available at: https://www.unilever.com/about/who-we-are/about-Unilever/
