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Reactive Summit 2018: Operationalizing Data Science

October 26, 2018

As more companies embark on data science and machine learning projects, more companies are bumping up against obstacles in achieving success with these efforts. That’s putting it mildly: even after developing successful mathematical models in the lab, there are some significant hurdles to transporting those results into a live production system. And according to some estimates 85% of big data projects fail.

In spite of these failures being categorized as “big data” failures, big data is so central to data science that an 85% rate of failure in such an important aspect of operational data science points to a wider issue in the operational challenges common across all data science projects. As an organization becomes more mature in undertaking data science projects, the cause of failure tends to progress from non-technical to technical in nature. Early failures are almost always non-technical in nature, while later failures are almost always due to technical reasons as the complexity of projects within an organization increases.

We travelled to Montreal to speak at Reactive Summit 2018 in order to showcase a path of how to move from the 20th century of expert-driven decision making systems to the 21st century of automated decision making systems.

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Stay tuned for more on this exciting topic, including a full reference architecture that we are diligently working on.


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