An Overview of the Data Science Process and Data Analytics Within Organisations

Introduction

Human society is always striving for efficiency, whether it's through complicated algorithms, multitasking, the sharing economy, or life hacks. One of the great promises of the internet and big data revolutions is that we can better our work and personal procedures and routines to do more in less time than ever before in the effort towards industry 4.0. Data has been acknowledged as the driving force behind the 3rd Industrial Revolution – the Information Age – when computers and the Internet revolutionized the globe. In this Age of Innovation, the 4th Industrial Revolution, when machine-human interaction is revolutionizing the way we live and work even more swiftly, data is being hailed as the “new oil.” It's hardly surprising, considering that data and analytics are at the heart of every new technological trend - Internet of Things, Artificial Intelligence, Robotic Process Automation, and so on. With practically every device creating and collecting data, data preparation and management is one of the most difficult tasks for data analysts and scientists. This is the process of filtering and sorting data from diverse sources and formats in order to categorize and prioritize it based on business needs.

However, static data - statistics alone - will persuade only a small number of people. The visualization must depict a situation that has an impact and necessitates careful consideration. There must be some movement in the visualization. In a nutshell, the visualization must convey a story, to capture the interests of the masses, especially stakeholders (Bogza & Zaharie, 2008), and this can be achieved through the data science process of data analytics. Furthermore, certain sectors are experiencing disruption as a result of rapid advancements in analytics technology, such as Artificial Intelligence and chatbots (Millar et al., 2018).

There are main issues surrounding today’s context that warrants this paper. Firstly, is the high failure rate of organizations, including small and medium-sized enterprises or ‘SMEs’ (Ahmad & Seet, 2009; Beh, 2013), micro-enterprises (Blanco-Oliver et al., 2014; O’Dwyer & Ryan, 2000), as well as established organizations such as large corporations (Khan et al., 2019; McGovern, 2007).

Regardless of SMEs located in either developed or emerging economies, it is found that SMEs everywhere have a high failure rate. For instance, in Asia, SMEs experience failure for the first five years (Abdul Rahman et al., 2016; Ahmad & Seet, 2009), whereby the failure rate stands at an approximate 60% and can potentially be worse (Chong, 2012; Husin & Ibrahim, 2014). In Africa, it is believed that the failure rate of SMEs in South Africa is between 70% and 80% (Adeniran & Johnston, 2011), whereby around 440,000 small companies in South Africa have closed in the previous five years (Fatoki, 2014). Micro-enterprises, on the other hand, is also experiencing stunted growth to point of failure. Despite the help of micro-finances, micro-enterprises can potentially close down even in less than 5 years. This is due to various barriers stunting growth (Omri & Frikha, 2011) as well as management issues (O’Dwyer & Ryan, 2000). Above all, entry-level businesses are most affected including those who have just started up. The lack of experiences is also a major hindrance (Watson, 2003).