Thesis and antithesis — Innovation and predictive analytics: Σ (Past + Present) Data ≠ Future Success

Ted William Gross
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Ted William Gross: AI Technologist and Data Theorist, Ituran Ltd, Israel

Applied Marketing Analytics: The Peer-Reviewed Journal, 2021, vol. 6, issue 3, 230-243

Abstract: Predictive analytics (PA) is a tool routinely used by companies to help chart a future product path. It makes extensive use of algorithms and data mining to sort out market desires and trends. It also combines a robust host of artificial intelligence tools, including machine learning, pattern recognition, natural language processing, sentiment analysis and emotion recognition, among others, to achieve more precise results. PA, though, is imperfect, as it is often subject to the whims of the marketplace. Analysing past and present data does not, in any manner, guarantee positive results. Indeed, when it comes to innovation, particularly ‘disruptive innovation’, relying on PA can lead a company down a disastrous path. Data analytics requires a method that validates innovation and uses PA as something other than an infallible crystal ball. But does the possibility of innovation automatically disavow any insights into future market trends that PA may supply? This paper attempts to place both innovation and PA into proper perspective. It considers when, where, how and why PA and innovation are paramount, but reiterates the importance of instinct, originality and creativity. To illustrate its argument, the paper draws on the history of the Sony Walkman and Apple iPod.

Keywords: innovation; predictive analytics; disruptive innovation; disruption; market analytics; data analytics; artificial intelligence (AI) (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2021
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