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Gartner conflates DI with large scale automated decision making and augmented intelligence but ignores the fundamental and well tested decision support role ML plays in all sorts of large and small decision making (see the category list in Machine Learning above).īy apparently focusing only on very wide application of automated decision making, Gartner concludes the DI will take another 5 or 10 years to reach the Plateau of Productivity.
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In fact these are the only two true data science techniques in this category, wrapped in general management disciplines of “decision support, continuous intelligence and process management”. Gartner says predictive and prescriptive analytics have been subsumed into this more general category of ‘Decision Intelligence’. Only to reappear in 20 moved back to an immature status? No explanation ever given for this reversion.Īsk your data science team and they will confirm for you that ML is a fully mature set of techniques and worth your immediate investment.
#GARTNER HYPE CYCLE EXPLAINED FULL#
We wrote about this over two years ago, as Gartner had consistently promoted predictive analytics down the curve until 2014 when it graduated completely off the curve, a measure of full maturity. It’s positioning, falling into the trough of disillusionment would certainly mislead non-data scientists into believing this is still a high risk set of tools. The Gartner narrative correctly identifies ML as “ one of the hottest concepts in technology, given its extensive range of impacts on business.” As we all understand ML can be used to address the lion’s share of data science opportunities in this Gartner list ranging from “automation, customer engagement, supply chain optimization, predictive maintenance, operational effectiveness, workforce effectiveness, fraud detection, and resource optimization”. Here’s how this bedrock mature set of techniques ends up at four different locations on the maturity curve with three of the four showing still too early for adoption. If you’re a definitional purist you might want to put reinforcement learning in here as well but as Gartner points out and as we practitioners are aware, RL is too immature for wide business adoption. To illustrate, take a look at our foundational techniques of machine learning, predictive and prescriptive models. All these heavily overlap in the content they review and not infrequently would lead you to draw different conclusions about the maturity of each option.Įven within a single hype cycle analysis there is easily confusion over the mix and overlap of tools versus techniques versus application categories. You might be unaware that there is a completely different Hype Cycle for Data Science and Machine Learning (a little more nuts and bolts) or you might come across the Hype Cycle for Emerging Technologies. There has been such a proliferation of hype cycles and magic quadrants that you could easily be looking in the wrong place.įor example, in the case of trying to select and prioritize an AI strategy you might logically look at the Hype Cycle for Artificial Intelligence (2019 the most current is above). What you might not know is that you now need an expert just to guide you through the expert literature. It’s likely that like many of us the first thing you’d reach for would be one of Gartner’s many hype cycle or magic quadrant analyses. Supposing you’re a business leader and supposing you’re trying to make an intelligent decision about prioritizing your AI adoption plans. Read the research, then consult with your own data scientists for a better evaluation of risk. Summary: If you’re planning your AI/ML business strategy watch out for the confusion in categories and overly risky ratings given by some research and review sources.