Data-driven Approaches to Forecasting
Data-driven Approaches to Forecasting


How do we know which forecasts to trust for our most critical business decisions? When stakes are high, big data and machine learning techniques can drive significant value across a wide variety of applications. However, finding the right approach is difficult. A tempting solution may perform well in one context but poorly in others, rely on unavailable information, or incur impractical costs. Whether it’s demand forecasting, supply chain management, or any other application, getting it right requires balancing the need for performance with the constraints of implementation and complexity.

We will discuss why organizations are turning to data-driven approaches to forecasting, applications and types of solutions, and challenges (both technical and practical) that arise during implementation. Attendees will leave oriented towards:
- Identifying types of forecasting applications and issues;
- Understanding the range of techniques available and related challenges;
- Evaluating potential data-driven approaches for your business;
- Measuring performance in the context of business objectives.


Javed is an economist and data scientist with experience in banking, finance, forecasting, risk management, consulting, policy, and behavioral economics. He has led development of analytic applications for large organizations including Amazon and the Federal Reserve Board of Governors, and served as a researcher with the Office of Financial Research (U.S. Treasury). He holds a PhD in financial economics and MA in statistics from U.C. Berkeley, as well as undergraduate degrees in operations management and systems engineering from the University of Pennsylvania. Currently, Javed is a Senior Data Scientist on the Corporate Training team at Metis, where he works with companies to upskill their staff in data science and analytics.

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