Abstract: For many organizations, market intelligence is a key function that is ripe for the introduction of data science techniques. Many market intelligence organizations rely heavily on data to describe historical trends and to drive the production of judgmental, expertise-driven forecasts. These forecasts often create substantial business value and influence significant decisions. While leadership often recognize the value of automation to standardize reporting and to enable rigorous, data-driven forecasts, organizations also face many challenges on their digital transformation journey. These challenges span from designing and implementing a data architecture that can store multiple forecast iterations to the integration of models that produce forecasts at different levels of granularity and at various time horizons. Connecting these forecasts in a reasonable way can require considerable effort, but can streamline business forecasting processes that ultimately result in a cohesive, integrated ecosystem.
Over the last several years, GE Aerospace has made significant progress digitizing the market intelligence space, transforming the business forecasting process from one that is highly manual to one that is integrated, automated, standardized, and reproducible. These efforts are part of an overall digital transformation in the market intelligence space that has centered around a transition from human-in-the-loop, manually-generated source data and spreadsheet calculations to one driven by robust data science methodologies. This talk will discuss both the challenges and successes we have experienced introducing automation and time-series forecasting methodologies in this space, as well as differences in strategy and approach when producing forecasts two weeks, two months, two years, and 20 years in advance.
Bio: Alex Antony is a senior staff data scientist at GE Aerospace where he leads modeling, analytic development, reporting, and forecasting for the market intelligence function. He has 10 years of experience in the data science field and 15 years of experience working with the Department of Defense. He holds a MS in Applied Statistics and a PhD from Indiana University where he focused on Computational and Quantitative Social Science.