Data Governance#
Prerequisites#
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Summary#
As researchers and data practitioners, we need to consider how to make decisions as individuals and within teams and communities for how we collect, manage, apply, and grant access to the datasets we work with.
In this chapter, we give an overview of data governance best practices for different contexts such as machine learning. We will provide guidance for different workflows and give examples of tools demonstrating how different teams have approached data governance activities such as data collection and data processing, which can support the adoption of practices for reproducible and responsible work with data.
In this chapter we will cover:
Motivation#
Teams working with or managing datasets can benefit from tools and best practices for making decisions about the data. To address the array of challenges associated with managing data, data governance tools and frameworks can support this process and also afford more rights to individuals who may be the subjects in or creators of the data as well as communities represented in the data.