Description:

Whether you are new to data science or already deal with it every day, you probably have a pretty good idea of what questions you are asking. But what about the most important question of all: is your data good enough to answer those questions? Before you even get to the considerations of statistical significance and so forth, the data itself must be of sufficient quality to be useful. But what exactly does that mean, and how do we assess quality? Typically we discover data quality issues as we go along, which makes for repeated and time-consuming re-work as we run analyses, fix data and re-run. This webcast introduces a simple conceptual framework for thinking about data quality and strategies for evaluating quality proactively to improve results and reduce unnecessary repetition.

About Ken Gleason

Ken Gleason's technology career spans more than 20 years, including real-time trading system software architecture and development and retail financial services application design. He has spent the last 10 years in the data-driven field of electronic trading, where he has managed product development and high-frequency trading strategies. Ken holds an MBA from the University of Chicago Booth School of Business and a BS from Northwestern University.

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