Big Data and Organization Management 

Big Data and Organization Management

Abstract

This paper reviews the current legislature on the impact of big data on organization decision making. At the begging, the paper argues that increased information sharing platforms on the internet have exposed all the previously known private information. Researchers have identified that such increased exposure to private information is a risk to using data as an asset and forces originations to use information as an input in decision making.  An increase in information availability will, on the other hand, shift the quality of decisions to a high level due to the ability to provide knowledge to managers. With information being readily available and in plenty, businesses will be able to carry out experiments about different variables such as customer behaviors to have more knowledge when making decisions. The review informs that there are approaches to a revolution where organizations will depend more on data analysis systems compared to managers in running organizations. Businesses that will properly employ data analytics will have more advantages over others by tapping the best product and service development approaches.

Keywords: Information, Bid Data, Decision Making, Management

 

 

 

 

Introduction

In the last few years, digital platforms, including Google and Facebook, have led to a significant increase in data. Furthermore, information has lost security, and what was private to organizations and individuals is no longer private (Brown et al., 2011). People and organizations can no longer boast of having an asset in the information. However, increased information accesses open better opportunities to have organizations relying on valuing addition instead of persuasion based on information asymmetric. Today, the public can fully understand the working of organizations and businesses to evaluate their or products’ real values. Big data is, therefore, changing the use of data from an asset to a valuable input in strategy development. This paper explores current literature on big data, evaluating ways in which the idea will impact the use of information and the decision-making process in organizations.

Literature Review

Big Data as an Input to Strategy

Big data is changing the way organizations use information as an end product to compete with an input in modeling different strategies. According to Casaca & da Gama (2013), the proliferation of online data-sharing platforms has developed new challenges to marketers due to increased communication between customers and the organization.  There is increased data transparency not only to organizations but also to their competitors and customers.  Most organizations used to either hide or provide unique information about their products and services to offer, which led to brand development. However, it is no longer to have such information as a unique positioning strategy. The increased transparency is forcing organizations to use the data in developing their strategy and products uniquely because the public will be aware of all the ingredients, including the cost of production and quality.  According to Sahay & Ranjan (2008), it is no longer possible for businesses to gain an advantage in using business intelligence through information integration but instead use the information to develop the efficiency of their supply chain.  Since the public will be having in-depth information about services, products, and organizations, persuasion through data cannot offer a strong competitive advantage in the current and future times.

The term predictive analytics emerges to show a shift in using the information as a final product in deciding to an input that creates knowledge. In an exposition to predictive analytics, Hair (2007) observes that there is an increasing trend in utilizing big data and data analytics due to their ability to create new knowledge. Information about customers, markets, public perception has become a valuable input that adds knowledge to make more effective decisions. There is, therefore, a general trend that is gaining momentum into using data as a decision making raw material that will raise the value of decisions.

 

Causation and Experimental Decision-Making Process

There is an approaching possibility of having a unique approach to organizational decision making based on experiments.  In a research study, Miles (2014) found that marketing analytics provides a significant prediction about the behaviour patterns of different customers. The findings offer that increased data allows organizations to move away from modeling to carrying out experiments about product and customer behavior in the future. Initially, the business couldn’t experiment with their customers since they hand limited information about the market and other products. However, increased data transparency allows companies to explore and exploit knowledge leading to a better predictive business (Jalonen & Lönnqvist, 2009). Due to increased data supply, it will be possible for organizations to carry out controlled experiments. The data will help in the testing hypothesis, which will inform operational changes, among other decisions (Brown et al., 2011). There are many forms through which organizations can employ to experiment with sufficient data. Online companies, for example, Facebook, and Amazon, have been doing such experiments by setting aside a web section to analyze user engagement. Big data, therefore, promises to change decision-making models to involve experiments leading to increased competition.

Murray and Wardley (2014) also find a similar impact of big data in supporting businesses’ experiments, especially in the development of customer experiences. The researchers argue that sufficient data places the marketing department in a position where it can apply the outcome-focused analysis of the market as opposed to gut feel. Outcome focused analysis depends on extensive data from customers and the market, which enables organizations to predict changes in areas such as taste and preference. Big data increases the utility of information since there are many expressions from customers about their future demands alongside evidence-based findings of the next big thing. Brown et al. (2011) provide an example that large retailers have started monitoring customer and product movements in stores alongside interaction between the two variables. Marketing Management Analytics (n.d), on the other hand, presents a case of small scale businesses and organizations where they claim that they have not had adequate data like large companies to employ an experimental approach to decision making. An increase in data, as a result, will lead to increased competition in strategies as organizations turn to empirical researches to inform decisions.

Big Data in the Management Position

Increased efficiency of deciding big data reduces the need for professionals in management whose function is to use information in guiding organizations. Lapointe (2012) observes that organizations no longer need controllable factors and the relationship existing between strategies and tactics. Big data and information technology are sophisticated that with proper analysis, organizations get diverse information about public perception and customer behaviors. As a result, the positions of experienced and educated managers are narrowing with the replacement of more effective and efficient big data analytics. Gantz and Reinsel (2012) point out that in the coming days, data requests will be coming from any device and network, making it easy for any layperson with analytical knowledge to make an organization decision. Data analytics, as a result, will improve decisions made in organizations by replacing managers with the best data analysts and analysis systems.

Steve (2012) demonstrates that big data is not only taking away the position of managers but also other workers due to artificial intelligence such as robotics. Artificial intelligence is a result of increased data that helps to predict the behavior of human beings. Big data is enabling people to predict behaviors and store the system in software to develop robotics. As a result, there is a replacement of many workers in organizations with the machines sue to big data. According to Chen et al. (2012), increased data availability in social media and other web pages is strengthening the use of e-commerce and marketing. Organizations do not need a lot of people who correct data from customers about products and disseminate persuasive messages. Today, there are live campaigns on social media that attracts as many people as roadshows and other open-air promotions.

Discussion and Managerial Implications

This literature review reveals a new form of competition between organizations alongside unique decision-making processes. Researchers in the area demonstrates that big data presents organizations with opportunities to more knowledge about management and strategies. Increased availability of data will make organizations increase utility in decision making through evidence about the market, public perception, and customer taste. Businesses have been using the information as an asset to develop unique selling points. However, increased polarity in digital information platforms makes that information available to everyone exposing the competitive advantage. As a result, businesses will shift towards using the increased information in developing their products and supply chains. Competition changes from persuasive through areas such as packaging and promotion to the actual quality of a product or service because customers will be aware of the value. Increased information will also lead to the development of more valuable decisions based on evidence. The level of decisions which big data brings is higher than what current managers are making due to increased knowledge about the market and other factors.

The implication of the various changes is the shift of management from people to system dependent. Data analysis takes place through computer software such as excel. The systems provide simple data that is easy to understand and hence, apply. As a result, there will be a minimal application of too much managerial knowledge of data analysis. The people who will be on-demand are those with knowledge on how to use the various data analysis software. However, managers will still be significant to help in selecting the needed information. Managers, as a result, should upgrade their knowledge on data analysis skills to accommodate the bid data as quickly as possible and gain a competitive advantage over other organizations.

References

Hair, J. F. (2007). Knowledge creation in marketing: the role of predictive analytics. European Business Review.

Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future2007(2012), 1-16.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.

Jalonen, H., & Lönnqvist, A. (2009). Predictive business–fresh initiative or old wine in a new bottle. Management Decision.

Casaca, J. A., & da Gama, A. P. (2013). Marketing in the Era of Big Data. HASSACC 2013.

Sahay, B. S., & Ranjan, J. (2008). Real-time business intelligence in supply chain analytics. Information Management & Computer Security.

Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data.’ McKinsey Quarterly4(1), 24-35.

Lapointe, P. A., T. (2012). The dog ate my analysis: The hitchhiker’s guide to marketing analytics. Journal of Advertising Research52(4), 395-396.

Marketing Management Analytics (n.d). Marketing Analytics: Not Just for Packaged Goods Anymore. Retrieved from http://mktg.uni-svishtov.bg/ivm/resources/Marketing%20Analytics_Problems.pdf

Murray, G., & Wardley, M. (2014). The math of modern marketing: How predictive analytics makes marketing more effective. IDC White Paper. reterived from http://www. sap. com/bin/sapcom/en_ us/downloadasset.

Steve, L. (2012). The Age of Big Data. Retrieved from http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html?_r=1&scp=1&sq=Big%20Data&st=cse

Miles, D. A. (2014). Measuring customer behavior and profitability: Using marketing analytics to examine customer and marketing behavioral patterns in business ventures. Academy of Marketing Studies Journal18(1), 141-165.

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