Abstract
Today, automation of business processes and devices like IoT for monitoring/activating services generate massive raw data, though they stand alone may not look useful but together carry domain specific signatures that are immensely useful for decision making. The problem of deducing strategic information in detecting patterns, analyzing, reasoning over it, and learning on business trends is popularly known as business analytics and uses artificial intelligence and machine intelligence techniques. This chapter while introducing basics of characteristics of business data analytics, presents types and uses of analytics, and standard processes. Further, this chapter would include an approach to design a recommendation system (with techniques such as content-based filtering, collaborative filtering, and Hybrid recommendations methods). This chapter would do a comparative analysis as well between process of business analytics, various types, and choice of recommendation systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cross-industry standard process for data mining. Wipkipedia, December 16, 2019. Available https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining.
Introduction to SEMMA. SAS Institute Inc., August 30, 2017. Available https://documentation.sas.com/?docsetId=emref&docsetTarget=n061bzurmej4j3n1jnj8bbjjm1a2.htm&docsetVersion=14.3&locale=en.
What is the team data science process? Microsoft, January 10, 2020. Available:https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/overview
Analytics solutions unified methods. IBM. March 01, 2016. Available ftp://ftp.software.ibm.com/software/data/sw-library/services/ASUM.pdf.
MacKenzie, I., C. Meyer, and S. Noble. 2013. How retailers can keep up with consumers. McKinsey & Company. Available https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers.
Wu, J. 2019. Types of recommender systems. Medium. Available https://medium.com/@jwu2/types-of-recommender-systems-9cc216294802.
Recommender system. Wikipedia, July 22, 2020. Available https://en.wikipedia.org/wiki/Recommender_system#Hybrid_recommender_systems.
Heckert, A. 2017. Minkowski distance. National Institute of Standard and Technology. Available https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/minkdist.htm.
Sharma, N. 2019. Importance of distance metrics in machine learning modelling. Towards Data Science. Available https://towardsdatascience.com/importance-of-distance-metrics-in-machine-learning-modelling-e51395ffe60d#:~:text=A%20distance%20function%20provides%20distance,formula%20used%20by%20distance%20metrics..
Mahalanobis distance. Wikipedia, July 31, 2020. Available https://en.wikipedia.org/wiki/Mahalanobis_distance.
Guo, G. 2012. Resolving data sparsity and cold start in recommender systems. In 20th international conference, UMAP 2012, Montreal, Canada.
Polat, H., and W. Du. 2003. Privacy-preserving collaborative filtering using randomized perturbation techniques. Electrical Engineering and Computer Science, 625–628. https://doi.org/10.1109/ICDM.2003.1250993.
Mobasher, B., R. Burke, R. Bhaumik, and J.J. Sandvig. 2007. Attacks and remedies in collaborative recommendation. Intelligent Systems, IEEE, 22: 56–63. https://doi.org/10.1109/MIS.2007.45
Richardson, J., K. Schlegel, B. Hostmann, and N. McMurchy. 2020. Gartner magic quadrant for analytics and business intelligence platforms. Gartner Research
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Gupta, A.K. (2021). Business Analytics: Process and Practical Applications. In: Rautaray, S.S., Pemmaraju, P., Mohanty, H. (eds) Trends of Data Science and Applications. Studies in Computational Intelligence, vol 954 . Springer, Singapore. https://doi.org/10.1007/978-981-33-6815-6_15
Download citation
DOI: https://doi.org/10.1007/978-981-33-6815-6_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6814-9
Online ISBN: 978-981-33-6815-6
eBook Packages: EngineeringEngineering (R0)