[ESSEC Knowledge] by Nicolas Prat - Professor at ESSEC Business School
The dramatic progress of artificial intelligence (AI) is affecting many sectors and functions of business. In his research paper “Augmented analytics”, to appear in 2019 in the journal Business & Information Systems Engineering, Professor Nicolas Prat focuses on the impact of AI on analytics. How is AI revolutionizing analytics, and what are the opportunities and challenges for managers?
“Analytics” refers to the technologies and processes for collecting, blending, modeling, analyzing and visualizing data in order to gain insights and make better decisions. Different types of analytics have been defined: descriptive, diagnostic (inquisitive), predictive, and prescriptive.
The term “analytics” became popular in the mid-2000s. In the early 2010s, the big data revolution gave rise to big data analytics. Self-service business intelligence (BI) empowered business users to analyze their data and generate their own visualizations without systematically resorting to the IT department. Today, AI (more specifically, machine learning and natural language processing) is bringing about a new revolution in analytics. Gartner calls this revolution “augmented analytics”. Others talk about “the cognitive generation of decision support”, “smart analytics” or, more simply, “AI-powered analytics”. Predictive analytics (and, more generally, advanced analytics) has traditionally relied on machine-learning algorithms, like neural networks, for the development of models. However, what is new with AI-powered analytics is the scope of applications of AI throughout the analytics cycle. This includes, for example, the application of machine learning to find the best machine-learning model (applying machine learning to the automation of machine learning…).
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