Does Data Science Render Science Management Redundant?

Abstract

The overall purpose of this study is to understand whether or not data science will render management science redundant. it includes the importance of data science in the business industry and the world today and also discusses the importance of scientific management and management theory. As the world is constantly improving with technology, we need to keep up with the new and improved abilities, whilst still remembering the original theories of management so that companies do not get completely taken over by technology and computers. It was investigated whether or not this new data analytics and data mining is making original management theories pointless. The design of this study is in a report form, where you can find debates for and against the argument. You can find an introduction which will explain what data science is and which will explain what science management is. You will also find an analysis and a discussion of the findings regarding the topic. This includes Taylors principles of management and includes key factors of management theory that is highly needed in the business world. The major findings of this report are that many businesses rely on data science and that it is extremely important in today’s worlds. It also finds the risks and benefits of using data science. My interpretation of these findings is that management theory will always be needed within a business. However, data science does help businesses succeed quickly and more efficiently.

Introduction

In this report you will find a discussion on whether data science will render management science redundant. Basically, in simple terms, is data science making science management pointless. This will include different management theories but mainly management science theory. Data science is the study of the generalizable extraction of knowledge from data. a common epistemic requirement in assessing whether new knowledge is actionable for decision making is its predictive power, not just its ability to explain the past (Dhar, 2013). It can be viewed as an amalgamation of classical disciplines like statistics, data mining, databases, and distributed systems (Van Der Aalst, 2016). Whereas management science, an approach to decision making based on the scientific method, makes extensive use of quantitative analysis (Anderson et al, 2018). Management theory also falls under the topic. Management theory is based on the idea that management activities can best be analysed in terms of four essential groups of activities, namely planning, organising, motivating and controlling. The main debate comes from the opinion that data science is more profitable. On the other hand, theorists believe that that sound research should always start with a hypothesis, which cannot be done with data science (Cole, 2004)

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In the modern world, 2020, technology has been advancing rapidly. The requirement to learn to use new technologies is becoming pervasive in the lives of adults, young and old. For example, computer systems of various forms are prevalent in nearly every aspect of our lives, including video-cassette recorders, computerised library catalogues, electronic banking, information kiosks, multi-function answering machines. This includes the new and better software that is being produced every day helping businesses. Businesses are constantly looking at new technology and software that’ll help improve their business and make things more effective. This includes different practices of data science.

Analysis and discussion

The purpose behind Data Science is to discover patterns within data (Gour 2019). It utilises different measurable strategies to break down and draw bits of knowledge from the information. From information extraction, fighting and pre-preparing, a Data Scientist must investigate the information altogether. At that point, the scientist has the obligation of making expectations from the information. The objective of a Data Scientist is to get ends from the information. Through these ends, the scientist can help organisations in settling on more astute business choices.

Data science is important in businesses today. One of the reasons it is important is because it helps mitigates risk and fraud (Monnappa, 2020). Data scientists are trained to identify data that stands out in some way. They create statistical, network, path, and big data methodologies for predictive fraud propensity models and use those to create alerts that help ensure timely responses when unusual data is recognized (Monnappa, 2020). This is important to businesses because it prevents businesses from getting frauded by other businesses. Another reason why data science is important is because it allows for ‘smarter’ thinking (Dataflair Team, 2019). Data science allows companies to analyse information on larger scales. Data science strategies allows companies to understand the problem, then to explore the quantity and the quality of the data. To decide whether the data is valid or not. It then allows tools to be implement. This means that finding the correct solution to solve the problem.

However, a problem with using data science in business is that the tools that are used for data science and analytics are more expensive to use to obtain information (intellspot, 2020). This means that businesses will be spending more money in order to develop the best results. However, despite data science being a smarter thinking technique, some researchers believe that scientific management theory is the best type of technique to use. Taylor (1911) recommended that “each worker be assigned a specific amount of work, based on time and motion studies.” He believed that if employees have specific quantitative challenging goals, their performance results are better (Taneja et al, 2011). He advocated that workers be given feedback daily as the extent to which they achieved their respective assigned tasks. This implies that workers and businesses can do really well, however they need a manager to do so. The statement is also implying that managers can do the same at what data scientist can do but involving the workers and looking at the issues and situation from a face to face perspective.

Another way that data science may cause science management to become redundant is because it can predict customer churn. The involuntary customer churn is referring to those customers whose contract terminated, or service disconnected by the company, while voluntary customer churns are those customers who switch the company without any prior information (amin et al, 2018). Although this is something the management theory can do, data science can do it a lot better with more specific readings and quicker. Therefore, businesses are choosing to use this rather than management theory so that they can understand trends better and prepare for them sooner in order to achieve good results.

One argument against the statement data science render management redundant is that according to Domino (2018) there is an industry shortage of data scientist managers. the ratio of individual contributors to managers is 15:1. Therefore, this shows us that data science is not taking over. However, as we are living in a forever advancing technological world, more and more people will be looking into job regarding data analytics and data mining. Therefore, in the near future we are likely to see a massive rise in the amount of data scientists’ managers.

One risk of using data science according to Marr (2018) is bad analytics. For example, misinterpreting the patterns shown by your data and drawing causal links where there is in fact merely random coincidence is an obvious pitfall. Sales data may show a rise following, say, a major sporting event, prompting you to draw a link between sports fans and your products or services – when in fact the rise is purely down to there being more people in town, and the rise would be equally dramatic after a large live music event. This shows us the need for management rather than just statistics because they will be able to rule out anomalies such as this. They will also be able to prepare themselves for situations like this and do things such as order more and relevant stock depending on the situation.

Another issue with using data science also according to Marr (2018) is the cost. Not only does a company have to pay for a data scientist, they will also have to pay for the equipment and software for the data scientist to be able to do their work. The better the quality of the equipment and software, the more expensive that it is. Moreover, companies will have to then pay for the data collection, the aggregation, the storage and then the actual analysis. This can be managed with budgeting techniques produced by a data scientist. However, it still could go wrong losing the company a lot of money. Where is if they stuck to a scientific management technique, it would be down to the manager to come up with more basic analysis and budgeting techniques, that would not cost the company any extra money.

Using data science, cannot solve all issues within a company. For example, staff motivation. It is all well and good staff members getting told the targets they need to reach and achieve. However, without proper leadership, staff will not have good morale and therefore will not likely meet the aims and targets set out by the company. Bande et al (2016) discovered that according to cognitive evaluation theory (Deci and Ryan, 1985), challenge-seeking individuals tend to exhibit an attribution basis for their behaviours and to work harder when approaching tasks. Individuals who enjoy task-related activities feel rewarded by performing a particular task (Deci and Ryan, 1985). In these respects, intrinsic motivation has been found to be a strong predictor of task performance (Kuvaas and Dysvik, 2009) and organizational citizenship behavior (Chiu and Chen, 2005), as well as the strongest determinant of employee creativity Industrial sales setting (Amabile et al., 1996). Further, several studies suggest that intrinsic motivation is related to learning orientation (Wang and Guthrie, 2004) and that intrinsically motivated individuals learn from the feedback that they receive. However, data science does not provide feedback on individuals, only on how the company does as a whole. If data science did completely take over, individual would not receive feedback, there for their motivation will be lower meaning that they would not perform as well as they could. Whereas in scientific management, managers are able to set certain tasks and aims for certain individuals which they will then be able to praise and give feedback on promoting encouragement and a positive workforce.

Despite this argument Sequoia (2019), says that A company’s ability to compete is now measured by how successfully it applies analytics to vast, unstructured data sets across disparate sources to drive product innovation. This therefore shows us that companies are now being judged on the data science that they collect and how well they use the data collected. Even though this does not fully support the statement ‘Data science will render management science redundant’, it still shows that we are moving towards a data science world where management theory may not be needed.

Conclusion

To conclude, data science has taken over the business world by storm. Data science is needed and does help create helpful and useful information for companies. However, there should always be a place for management theory as computers cannot do everything.

As mentioned in the debate, there are certain situations that need managers in order for sales to be achieved as well as possible.

However, data science does find the best techniques and methods for a business. Management theory will always be important for businesses, but businesses need to adjust to this forever changing world and find a way data science and management theory can correspond and work together.

Data science will continue and continue to grow and become more advanced so although data science has not currently rendered science management redundant, there is no way of saying that it will not in the future.

Refrences

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