Abstract
Analytics today is an area whose demand has reached a boom with every other organization using it to ponder upon major decisions. The data is growing exponentially day by day. The future of businesses is very much dependent on big data. This chapter reflects on the three types of analytics techniques used while discovering, interpreting, and communicating the meaningful patterns and trends in data, i.e., descriptive, predictive, and prescriptive analytics.
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Descriptive: Analytics technique that uses data mining to get insights on what has happened in the past.
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Predictive: Analytics technique that uses statistical methodologies and forecasting to know what is likely to happen in future.
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Prescriptive: Analytics technique that uses algorithms to know what should be done to affect what is likely to happen in future. Beginning with the brief idea of analytics, the chapter reflects on data mining along with the role of ML and AI in analytics.
Techniques are compared stating the purposes they are used for. The big firms using them as a combination to grab every possible opportunity is discussed. These techniques being unique in their own implications have both the advantages and disadvantages. The chapter also discusses the various statistical methodologies, tools, and programming languages being used in these techniques. The overall thrust is to reflect on how organizations can adopt the new trend in order to completely change their operations and strategies to match up with the era where data is playing a huge part in taking informed decisions.
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Roy, D., Srivastava, R., Jat, M., Karaca, M.S. (2022). A Complete Overview of Analytics Techniques: Descriptive, Predictive, and Prescriptive. In: Jeyanthi, P.M., Choudhury, T., Hack-Polay, D., Singh, T.P., Abujar, S. (eds) Decision Intelligence Analytics and the Implementation of Strategic Business Management. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-82763-2_2
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