Paper Example on Business Analytics in Product Management

Introduction

Globalization has revolutionized how business is conducted which means business internationally have accessibility to similar resources such as products, components, materials and even human resources (Sprongl, 2013). This means that businesses utilize similar technologies due to the increased competition. Therefore the only means that businesses can utilize to increase their competitive edge is through the business decision making process (Business Analytics, 2016). Businesses have been using analytics in the decision making process. Business analytics can be described as how data is collected, stored, analyzed and interpreted to ensure better decisions and the enhancement of the organizational performance (Groene & Nene, 2017). This paper discusses the role played by business analytics in product management.

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The business analytic process entails the sequential application of three major steps to a source data. The aim of this process needs to be related to the business and try to enhance the product performance in various ways. These three major components of the business analytic process include descriptive, predictive and prescriptive analytics (Corcoran, n.d). Descriptive analytics entails the utilization of simple statistical methods in the description of the details contained in the data. For instance, an age bar chart can be utilized in depicting the consumers that want a specific product. The purpose is usually the identification of trends that might exist in large data sets. It tries to provide a picture of the data and the criteria possible for the identification of product trends (Schniederjans et al., 2014). Another type is predictive analytics which entails the utilization of advanced statistical methods or information software in establishing predictive variables and developing predictive models. For instance, multiple regression is utilized in showing the presence or lack of relationship between weight and age on the sale of diet products. The knowledge of the existence of relationships is essential in explaining why independent variables impact on dependent variables. It is essential since it develops predictive models appropriate for the identification of predictive future product trends. Lastly, there is predictive analytics which entails the utilization of decision science and management science in addition to operation research methods to optimally utilize the available resources. For instance, if a business has limited advertising budget for its products, linear programming models can be applied in allocating the budget.

Business analytics is essential in the decision-making process. Many well-established businesses have developed an analytical capability. There has been a demonstration of a clear relationship between business performance and the effective utilization of data to gain the insight necessary for the decision-making process (Sharma et al., 2010). The business analytic process helps in solving problems, and there is the identification of opportunities for the improvement of business performance. In the process, there is possibility that the business will establish strategies necessary for guiding operations and the identification of strategic opportunities for the achievement of competitive advantage. It is the tasks of the decision-making process to solve problems and determine strategic opportunities to be followed by the business. Business analytics helps in enhancing the decisions making by aiding the organization unlock value of the business. This entails identification of new market opportunities for the products and the improvement of customer responsiveness. It also aids in the improvement of financial discipline and integration of business performance across various producing generating operations, supply chain and logistics (Mahajan et al., 2017).

There are notable benefits of analytics in product management. The utilization of analytical tools helps in the improvement of business operations. The achievement of larger market shares compared to the competitors require the use of tools that promote the integration of the current trends and technologies employed by the competitors (Jiang, 2017). An organization that embraces business analytics tends to be a leader in all aspects. This is because it is possible to accurately establish the necessary changes with the relevant and accurate data. One of the significance of business analytics is that it helps in reduction of costs in product management. The application of these tools guides the organization in employing resources in areas that are only necessary. This helps in the elimination of wastage of resources which drains the profits realized. It means the appropriate application of business analytics has the capability of minimizing labor costs and operation costs in the production process. Concerning product management, analytics tools promote faster and better decision making which ensures the relevance of the organization based on the decisions that have been made. The institution of effective decisions helps in moving the business forward unlike ineffective decisions. It means the analytical tools avail data that has been appropriately evaluated and conclusions derived from it (Jiang, 2017). This leads to the elimination of errors introduced by unsupported decisions.

Additionally, the analytics tools also promote the proper utilization of the new products in an organization. The organization needs to ensure that the products and services availed to the clients completely satisfy their needs and are value for money. The products offered by the organization should strive to provide solutions to a specific problem that have been identified. The analytical tools, therefore, helps product management in the establishment of correct products and services that are highly demanded by the customers and the business.

Conclusion

In conclusion, analytics are important since they inform on the status of the products. Organizations that focus on analytics entails the deployment of tools and implementation of a process that tries to enhance the quality and availability of appropriate information. This ensures that the organization makes appropriate decisions concerning the products and services. Ultimately the consumers get products that satisfy their needs, and the business improves its competitive advantage.

References

n.a (2016). Business Analytics and decision-making: The Human Dimension. Cgma.org. Retrieved 27 March 2018, from https://www.cgma.org/content/dam/cgma/resources/downloadabledocuments/business-analytics-briefing.pdf

Corcoran, M. (n.d). The Five Types of Analytics. Informationbuilders.es. Retrieved 27 March 2018, from http://www.informationbuilders.es/sites/www.informationbuilders.com/files/intl/co.uk/presentations/four_types_of_analytics.pdf?redir=true

Groene, F., & Nene, D. (2017). The five commandments of digital product management. Strategyand.pwc.com. Retrieved 27 March 2018, from https://www.strategyand.pwc.com/media/file/Experience-matters.pdf

Jiang, F. (2017). Data Analytics Helps Business Decision Makin g. Digitalcommons.wou.edu. Retrieved 27 March 2018, from https://digitalcommons.wou.edu/cgi/viewcontent.cgi?article=1004&context=computerscience_studentpubs

Mahajan, S., Saha, S., & Macias, A. (2017). Analytics: Laying the Foundation for Supply Chain Digital Transformation. Thehackettgroup.com. Retrieved 27 March 2018, from https://www.thehackettgroup.com/wp-content/uploads/2017/12/hackett-analytics-supply-chain-digital-1711.pdf

Schniederjans, M., Schniederjans, D., & Starkey, C. (2014). Business Analytics Principles, Concepts, and Applications. Ptgmedia.pearsoncmg.com. Retrieved 27 March 2018, from http://ptgmedia.pearsoncmg.com/images/9780133552188/samplepages/0133552187.pdf

Sharma, R., Reynolds, P., Scheepers, R., Seddon, P., & Shanks, G. (2010). Business Analytics and Competitive Advantage: A Review and a Research Agenda. Pdfs.semanticscholar.org. Retrieved 27 March 2018, from https://pdfs.semanticscholar.org/6df7/265255e9551962d06f76477226bf544c26c8.pdf

Sprongl, P. (2013). Gaining Competitive Advantage through Business Analytics. The Stern Stewart Institute, 61(7), 2779-2785.