April 02 to 04 2018, Santa Clara, USA.


Speaker "Kaan Katircioglu" Details

Name :
kaan katircioglu
Company :
Title :
Director, Data Science
Topic :

Embedding Data Science in Business Decision Making Through Analytic Agents

Abstract :

Cloud computing has been fueling the emergence and wide use of “analytic agents”. Simply defined, an analytic agent (AA) is an algorithmic representation of how someone makes decisions in a process. Examples include algorithmic trading in Finance industry, machine learning for autonomous cars, algorithmic diagnosis in health care. This technology can have equally disruptive impact on the way business processes are designed and executed. It is not hard to predict that, in a few years, all industries will have to use AAs in their planning and decision-making processes to stay competitive. Early adopters will gain substantial competitive advantage. Some leading tech companies are already moving in the direction although it has not been established as a scientific discipline yet. There are significant benefits of using AAs in process management. They bring a discipline to process owners to define the rules, constraints and objectives of their planning decisions comprehensively, unambiguously and accurately. Therefore, they enhance interactions and communications amongst teams that contribute to a process. When developed well, AAs can manage routine parts of a business process autonomously and therefore can help business make instant decisions in planning and execution. Since AAs are well defined computable objects, their behavior can be analyzed to identify factors that impact business performance. Extensive “what-if” analyses can be conducted through AAs to answer key questions and therefore identify best actions to improve business metrics. In this presentation, we will articulate how AAs can work as building blocks for business process design through illustrative examples.

Profile :
Dr. Kaan Katircioglu is currently the senior direction of data science at Microsoft. He manages the statistical demand forecasting practice for the entire portfolio of Azure cloud products for infrastructure planning. Prior to Microsoft, he held a senior analyst position at Google as member of the cloud infrastructure planning organization. He started his career at IBM Watson Research as a research scientist, performed as people manager, relationship manager for travel and transportation industry, and research consultant for IBM customers. In 2011, he was selected to be a member of the Academy of Technology, IBM’s think-tank organization. In 2012, he led the incubation of “Supply Chain Scenario Modeler” solution that helped McKesson Corporation reduce its working capital by $1 billion. He was recognized as a Franz Edelman Prize Laureate at INFORMS 2013 competition. Throughout his career, he conducted 50+ projects, published 30+ scientific papers, and received 20+ patents for his inventions. He appeared at several conferences as invited speaker, panelist, and session chain. His experience spans areas such as analytics and optimization, supply chain management, IT services management, demand and supply planning, process design, statistical modeling and forecasting, stochastic processes, and simulation. He holds a bachelor’s degree in Industrial Engineering, Master’s in Statistics, and Ph.D. in Operations Research / Management Science.

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