A Study on the Intersection of Human and Artificial Intelligence through Augmented Analytics


  • Manisha Yadav Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai, Maharashtra, India
  • Amit Yadav Research Scholar, MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Mumbai, Maharashtra, India


Artificial Intelligent – AI, Machine learning – ML, Augmented Analytics, Bayesian Inference, Probabilistic Programming


The amount of exertion put in analytics by humans is colossal. Analytics today predominantly consists of performing the same steps repeatedly manually. Augmenting practices leverage the idea of automating the steps which are repeatedly done by humans and coalesce it with decision making capabilities of mankind. This will furnish us with applications where machines will understand data and make automated business analytics with the help of machine learning and AI. Furthermore, using AI we can amalgamate the science of decision-making capabilities of humans and automated analytics to takeover many of the repetitive procedures in data science and optimize the decision-making capability of machines resulting in dignitary business decisions.


Khalvati K, Park SA, Mirbagheri S, Philippe R, Sestito M, Dreher JC, Rao RP. Modeling other minds: Bayesian inference explains human choices in group decision-making. Sci Adv. 2019 Nov 27; 5(11): eaax8783.

Ruttenberg BE, Pfeffer A. Decision-making with complex data structures using probabilistic programming. arXiv preprint arXiv:1407.3208. 2014 Jul 11.

Brooks-Bartlett J. (2018 Jan 5). Probability concepts explained: Bayesian inference for parameter estimation. by Towards Data Science. URL: https://towardsdatascience. com/probability-conceptsexplained-bayesian-inference-for-parameter-estimation-90e8930e5348.

Murphy KP. Machine learning: a probabilistic perspective. MIT press, United States; 2012 Sep 7.

Chen H, Chiang RH, Storey VC. Business intelligence and analytics: From big data to big impact. MIS Q. 2012 Dec 1: 36(4): 1165–88.

Howson C, Idoine CJ, Sallam RL. Augmented analytics is the future of data and analytics. Gartner.

Hiroshi K, Makoto T, Kenji I. Nanoindentation hardness test for estimation of Vickers hardness. Trans JWRI. 2006; 35(1): 57–61.

Huang Y, Rao RP. Reward optimization in the primate brain: A probabilistic model of decision making under uncertainty. PloS one. 2013 Jan 22; 8(1): e53344.

Guest. (2019 Jan 14). Probabilistic Programming in Python. [Online]. Available from: https://www.marsja.se/probabilistic-programming-in-python/

Bain R. Are our brains Bayesian? Significance. 2016 Aug; 13(4): 14–19.

Pomerol JC. Artificial intelligence and human decision making. Eur J Oper Res. 1997 May 16; 99(1): 3–25.

Prat N. Augmented analytics. Bus Inf Syst Eng. 2019 Jun 1; 61(3): 375–380.

Sjögren C. (2019). Survival Analysis Using Time-Frequency Analysis of Heart Rate Variability During Exercise. [Online]. https://lup.lub.lu.se/luur/download?func=downloadFile&record OId=8975358&fileOId=8975367

Li M, Tsien JZ. Neural code: neural self-information theory on how cell-assembly code rises from spike time and neuronal variability. Front Cell Neurosci. 2017 Aug 30; 11: 236.