Welcome back. Previously, we discussed data governance committees in health care. In this lesson, we'll look at data governance systems. After this lesson, you'll be able to describe the systems that help us support data governance processes and explain how they're used. I hope you recall our previous introduction to metadata, data dictionaries, and the description of data. In order to govern data effectively, we need to understand and document the data. That's what we will consider in this lesson. Over the course of the development of data systems, many systems allow us to manage our data, to understand what our data is, and understand how we can use it. There are systems for documenting data and these are key to being able to have a good data governance program. Descriptions of metadata, what the data is, where it's collected, when it's collected, and how it's collected, it's formats, are all parts of the underlying structure that support data governance. Having data dictionaries with good definitions that can be used by technical people and less technical people really enable us to have the data used broadly. Definitions of fields that move from operational systems to the warehouse, descriptions of any transformations that we do between operational systems in the warehouse are all described in data governance documents. Having these documents allows us to understand how to work with and aggregate the data in meaningful analytics. These need to be maintained and under the purview of the data governors and the data owners, to ensure that we're able to do that. The mapping of data between systems is something that needs to be documented and described. Knowing source systems and destination systems again, allow us to know the context where the data was collected and ensures our ability to use it later. Having that well documented is the key to the reuse of data. Our data governance policies ensure that we follow procedures that make this consistent. Similarly, knowing where our data is mapped to industry standards and what industry standards are being used is something that data governance captures and describes. At many sites, we see evolving systems that not only capture all of this data and metadata that provide workflow processes for managing it over time. Those workflow processes can route proposed changes to the metadata, to the data dictionaries, through the data governance committees, so that it can be approved and agreed to by all parties that need to be involved. Typically, when we make changes to data and modifications to our datasets, we look for subject matter experts to review those changes and make sure that that data is vetted, and appropriate folks are able to clearly identify where the data can be used for which particular uses. Using systems that automate the workflow help us manage the process for reviewing and improving data definitions, reviewing and approving policies around data, and ultimately drives us towards having well-documented quality standards that make our data easier to use. We've come to the end of this module. In it, we've talked about data governance. We've talked about why data governance is important, how data governance supports the culture of data quality, which ensures that the data we collect is appropriate for the uses we intend to use it for. We've talked a bit about data governance structures, we've talked a little bit about data governance committees and how committees are composed of groups of folks interested in the data. We've talked about what a data steward is, and how they maintain the flow of data and its accuracy, and how they follow instructions and the needs of the folks that identify as data owners who are responsible for creating and managing data. Ultimately, the folks who use it the most. We've talked about how data governance supports the collection of data, the use and reuse of data, and how these policies and procedures are critical to ultimately managing the value of a data asset. Thank you. It's been fun. Good luck.