Overcoming External Data Hurdles and Enriching Predictive Forecasts at Scale

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Topic: “Overcoming External Data Hurdles and Enriching Predictive Forecasts at Scale”

Speaker#1: Baylen Springer, CEO of Ready Signal
Baylen has over 20 years of experience in marketing, analytics, and product development. Prior, Baylen was the Chief Product Officer at Leavened, leading product development for a platform delivering Marketing Mix Modeling and Television optimization solutions. Additionally, he was an SVP of Media Analytics for Ford Motor Company. Baylen received his B.S. in computer science from the University of Michigan and an MBA from the University of Michigan Ross School of Business.

Speaker#2: Matt Schaefer, Chief Growth Officer of Ready Signal
Matt is an accomplished SaaS technology executive with a seasoned background in Data Analytics, Artificial Intelligence and Machine Learning at hyper-growth technology companies. During his evolving roles, he has built transformative value in the mid-market and Fortune 1000 space, operationalizing data into industry vertical solutions and helped scale organizations from start-up to successful IPO. At Ready Signal, he is responsible for leading the strategic growth efforts for the disruptive technology company that streamlines the integration of data from external factors to drive real-time, contextual decisions and forecasting solutions for any organization.

Abstract:
A well-structured plan for using external or “alternative” data in your data science and analytics practice can be a competitive advantage. The problem is the external data ecosystem is fragmented and expanding rapidly. Thousands of data outlets that are not normalized or easy to procure makes the data wrangling task even more challenging, but not insurmountable.

Join this interactive session with experts from Ready Signal as they explore strategies to mature your data integration and decision intelligence processes. Session takeaways include:
- Evolving your data strategy: The Importance of Alternative Data
- Overcoming challenges in building quality alternative data sources into analytics workflows and predictive models.
- Real-world use cases in scaling external data sources to optimize cost and maximize business value

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