Operationalizing Data Science

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In the video presentation below, Joel Horwitz, Vice President, Partnerships, Digital Business Group for IBM Analytics, discusses what it means to “operationalize data science” – basically what it means to harden the ops behind running data science platforms.

Over the past 3-4 years, IBM has partnered and invested in helping its clients marshal their valuable data and then to use Data Science to build insights and models that can create business value. The market is shifting to operationalizing these Data Science investments into production applications. The demands created by the vast volume and the blinding velocity of data can only be addressed via the reactive principles. IBM and Lightbend are working with clients who are ready to make strategic investments in Cognitive applications with the latest architectures for building and running distributed Reactive systems using Akka, Kafka, Spark, and more.

Joel Horwitz graduated from the University of Washington in Seattle with a Masters in Nanotechnology with a focus in Molecular Electronics. He also hails from the University of Pittsburgh with an International MBA in Product Marketing and Financial Management. Joel designed, built, and launched new products at Intel and Datameer resulting in breakthrough innovations. He set and executed upon strategies at AVG Technologies that led to accretive acquisitions. He established a big data science team and the first Hadoop cluster in Europe. Most recently, he spearheaded new branding, positioning and business development strategies for several startups in area of Data Science and AI including Alpine Data Labs and H2O.ai. He launched IBM | Spark and the Watson Data Platform and is now focused on building strategic partnerships and ecosystem for the IBM Watson and Cloud Platform businesses.


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