Which executive is responsible for data governance?

This will vary by organization, but it may be the chief data scientist, the head of analytics, or a chief operations officer, to name a few.

Data Governance

Q: What is data governance?

Data governance is the organizational framework that applies to how data is obtained, managed, used, and secured by your organization. Having a strong data governance strategy in place empowers your organization to trust the integrity of their AI and machine learning models by ensuring that their data originates from reliable sources.

Q: Why is data governance important?

These structures ensure that your machine learning models are instructed to follow your organization’s key principles and values. With data originating from multiple sources that rarely, if ever, follow the same data science practices, a strong data governance framework is the key for your organization to follow best data science practices.

What Is Data Governance?

Data governance is defined as the organizational framework that applies to how data is obtained, managed, used, and secured by your organization. Having a strong data governance strategy in place empowers your organization to trust the integrity of their AI and machine learning models by ensuring that their data originates from reliable sources. These structures also ensure that your machine learning models are instructed to follow your organization’s key principles and values. With data originating from multiple sources that rarely, if ever, follow the same data science practices, a strong data governance framework is the key for your organization to follow best data science practices.

Why Is Data Governance Important?

First, data governance is one of the four key principles of AI ethics – which also include ethical purpose, fairness, and disclosure. Governance (the fourth principal) is necessary to study and understand the outcomes of AI failures and push your organization to apply high standards of risk management to your models. This is especially important in situations where life and death are stake, such as hospital treatments and healthcare.

Second, as businesses, organizations, and agencies often rely on data from numerous sources with different workflows and data-handling practices, governance is essential to reaching best data science practices. Establishing a reliable governance framework can provide your company with assurances that the data used by your models is reliable and consistent – and that the model reflects and follows your organization’s internal values. Understanding your own data governance definition enables your organization to more easily trust your machine learning models. It will also enable you to more easily explain relevant findings to others and how the model arrived at its conclusions.

Finally, following existing regulatory compliance obligations is another important motivation for firms to craft and adhere to a data governance model. Recent data breaches have prompted many organizations to make security an integral part of their own data governance frameworks. Having a clear data governance framework in place can help your organization remain compliant with existing laws like the European Union’s Global Data Protection Regulation (GDPR).

Traditional approaches to data governance are often based on corporate policies that are implemented across organizations in a top-down fashion. However, the reality is that these policies do not accurately reflect the way business teams or individuals interact with data. As data and analytics becomes increasingly democratized, it is important for data governance policies to reflect the day to day activities and interactions with data. This notion of bottoms-up data governance is often referred to as emergent data governance.

Data Governance + DataRobot

Poor data governance can lead to unreliable conclusions being generated by your machine learning models. It’s important for you to understand where your data originated, how it was handled, and the goals that your AI platform and machine learning models set out to achieve.

The first step in establishing a data governance framework is understanding your organization’s priorities and values, as well as what you expect your internal policies to ultimately achieve. DataRobot offers a free online white paper to help your organization understand a wide range of AI ethics topics and abide by best data science practices. Our enterprise AI platform can also offer your company an alternative to black box-sourced AIs that might not share your organization’s values and that may prevent you from explaining how your AI reached its conclusions. DataRobot MLOps, a product that is also available through the AI platform, offers users production model governance capabilities that help users oversee the lifecycle of their machine learning models from deployment to production. MLOps also allows you to test and update models currently in production, as well as access full controls and log information to ensure that you are meeting legal and regulatory compliance.

The AI platform also provides extensive capabilities to manage the data you use in your AI initiatives. The platform includes access to AI Catalog, a centrally managed location where datasets are collected and where access to data, models, and deployments can be regulated and tracked.

Data Prep, DataRobot’s data preparation solution, is also available through the AI platform. Using Data Prep, data preparation projects can automatically record every action performed on a dataset to provide a full lineage of actions, along with explanations. Data Prep directly integrates with the AI Catalog to publish curated data back into the catalog where it is consumed and managed for machine learning model creation and deployment.

Learn More About AI