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MaheshCV
Explorer
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AI is a relatively new buzzword but there has been a lot of talk around data analysis and analytics for over a decade. Herein we will present a perspective that AI is a continuum of data analytics.

Large portions of data science and analytics originated in the field of statistics. On the other hand, AI emerged from computer science as the practice and science of studying "intelligent agents." 

AI can be looked at as conventional analytics with the added dimension of cognitive analytics. Where conventional analytics stops at the ability to be prescriptive, AI enables technology to mimic human cognitive function, most notably with the ability to make future inferences via self-learning feedback loops.

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Conventional analytics focuses on information levels and extends to the extraction of patterns and predictions i.e. conventional predictive analytics mainly leveraged by business intelligence professionals. Taking this discipline to the next step to where the technology can display characteristics of wisdom is where we enter the realm of AI. By making inferences from raw data and producing automatic decisions, AI provides autonomy with wisdom thereby taking conventional data science to the next level.

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AI and analytics can therefore be seen as on the same continuum rather than separate fields altogether. There should be a strong push for AI and data scientists to codify interdisciplinary techniques to educate stakeholders and deliver solutions to customers.

Closing thoughts:

AI is an extension of analytics. Where analytics stops at the inferential decision making process, AI pushes the envelope by displaying wisdom and autonomy. AI is grounded on the same algorithms which are core to data analytics but with additional algorithms at the root of machine learning based on classification, regressions and clustering. Only AI is more powerful on the unstructured data via its vision and NLP algorithms.

This blog serves as an open call for a unified approach to analytics and AI.