So you’ve decided that your organization needs data scientists (whether to determine which metrics to optimize for, how to learn from data and suggest new innovations or addressable markets, or to develop machine learning models and implement predictive analytics). You’ve created scaleable infrastructure and distributed systems for them to use to process data. You’ve figured out who to hire and how.
Now, what do you do with them? In this session we’ll discuss some examples of how companies like LinkedIn make product and business decisions from the utilization of data.
We’ll discuss the spectrum of data science used within an organization, and organizational needs such as democratization of data via self-serve data platforms for experimentation, monitoring, and data exploration — and the challenges that come with such systems. We’ll cover important foundations such as data quality and how to track and merge disparate data sources. We’ll review some examples of how data scientists can drive the art, science, and politics of defining which performance measurement metrics should be used to drive the business.
Participants will leave this session with the ability to identify opportunities for data scientists to contribute within their organization, and to drive transformation into a data-driven organization.