Issues happen. It’s an unavoidable component of modeling complex systems and decisions. I had the chance to really think about this while taking cover in a bus shelter during a sudden Austin deluge.
While weather forecasts driven by advanced modeling systems are quite useful, a part of me knows to always hedge against their inherent unreliability.
In this sense it’s not surprising that most of the early success of machine learning in the enterprise has clustered around low-error-cost problems. Models for targeting ads, or recommending products, friends or connections, do not wreak havoc when they misfire. Most end users of the system are not attending closely to the advises.
And even if they do see an issue it’s trivial enough to be amusing, why was I recommended a meat grinder with my book of vegan recipes? The occasional success — an excellent suggestion that inspires someone to click — is far more important than frequent misfires.
But what about problems where an error is costly, such as supply chain optimization, trade planning and perioperative care? As we integrate data science and machine learning into the enterprise, better approaches to error mitigation are required to operationalize and scale analytics. Central to this effort is an acknowledgement of the distinct ways in which humans and machines err.
We must build analytic systems that effectively combine the domain knowledge, world knowledge and intuition of an organization’s people with the vast data context of its machines.
Classic decision support systems represent a version of this approach, and business intelligence tools are their modern instantiation. By summarizing and visualizing business data, BI software assists executives and decision makers in their reasoning by giving them an accurate view of the past. The difficult work of connecting an understanding of the past to action in the present is left as an exercise for the decision maker.
How to solve errors on Mac? You can refer to this tech site.