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Speaker Information

 Ari Caroline
Ari Caroline Chief Analytics Officer Memorial Sloan Kettering Cancer Center
Ari Caroline is the Chief Analytics Officer at Memorial Sloan-Kettering Cancer Center (MSK). The MSK Analytics group, which Ari established, leverages the power of data science to empower clinical insights and to help shape strategic decisions at MSK. Over the past ten years, MSK Analytics has pioneered the healthcare application of a wide variety of analytical methods, including natural language processing and machine learning.

The Data Products team is the latest addition to the group. Data Products brings the culture of a tech startup to MSK, with data science, engineering and interface design skills. These skills will allow MSK to translate its vast data and its clinical and research expertise into intuitive software applications.

Ari’s team spearheaded efforts that lead to Memorial Sloan-Kettering receiving the 2012 INFORMS Prize, awarded by the Institute for Operations Research and the Management Sciences in recognition of organizations that have “repeatedly applied the principles of OR/MS in pioneering, varied, novel, and lasting ways.” He initiated MSKCC’s collaboration with IBM to adapt IBM’s Watson technology for oncology and to create an adaptable, evidence-based clinical decision support system.

Ari is now leading MSK's Knowledge Engine Initiative, with a goal of building a fully-integrated analytics platform that will make insights from the treasure trove of MSK data immediately accessible for clinicians and clinical researchers.

Ari holds an MBA from Yale University, with coursework towards a PhD in Financial Economics. His undergraduate degree is from the University of Michigan in Russian, Mandarin and Development Economics.

January 29th, Day 3

9:40 AM Managing the Shift from Big Data Analytics to Data Products

"One-off" data science models can be helpful in answering "one-off" questions. However, in most large organizations, the same questions can be repeated over and over, only with varying inputs and parameters. In these circumstances, data science models that are focused on a single expression of a problem are inefficient. Under these circumstances, one-off models should be replaced by robust, flexible data products that allow the end user to ask many different permutations of a similar inquiry. At Memorial Sloan Kettering (MSK), one of these products recently released across all MSK sites is a tool for intuitively searching clinical trials using natural language and for matching patients to these trials using similarity algorithms.

Topics discussed will include:
  • When is a data product appropriate?
  • What does a data product team look like?
  • How does a data product team interact with other parts of the organization?