In order for it to be effective, it must encompass several key components that support the follow-on data management activities. These components include:
Data standards
It should set explicit data standards for consistency across the entire organization. These standards should assess and verify data quality and should be transparent to everyone in the company. As a result, they should help teams better comprehend and use data.
Data standards should also allow any third-party auditors to easily see how the organization handles sensitive data, how that data gets used, and why it gets used in that way. This transparency is essential for compliance, especially in the case of a data breach.
Data integration
Data integration brings together data from diverse sources to make data more readily available and power deeper insights. Good data governance requires a complete understanding of how data gets integrated across systems and processes. Specifically, the data governance program should define the tools, policies, and procedures used to pass data across systems and combine information.
As a best practice, these data integration guidelines should be clear and easy to follow to ensure every new system adheres to them. Additionally, the team responsible for data governance should assist in reviewing these guidelines during any new technology implementations.
Data security
Protecting the security of data is essential, as any unauthorized access to data or even loss of data can pose serious risks – from dangers to the subjects of data to financial loss to reputational damage. A data governance framework outlines a variety of elements related to data security, including where data is stored, how it’s accessed, and what level of availability it has.
Specifically, it should detail defenses like authentication tools and encryption algorithms that need to be implemented to protect the data network. Then, any teams working on data governance should partner closely with IT security to ensure adequate protection measures are in place based on those guidelines.
Data lifecycle management
Understanding the organization’s data lifecycle means knowing where data resides at any given time as it moves through systems until it eventually gets discarded. Good data governance allows you to quickly discover and isolate data at any point in the lifecycle.
This concept, also known as data lineage, allows analysts to trace data back to its source to confirm trustworthiness.
Data observability
Data observability allows you to understand the health and state of data in your system to identify and resolve issues in near real time. It includes a variety of activities that go beyond just describing the problem, providing context to also resolve the problem and work to prevent it from recurring.
Data governance helps set the framework for data observability, setting guidelines for what to monitor and when and what thresholds should set off alerts when something isn’t right. A good data observability platform can handle these activities, making it important to choose a platform that can meet the requirements for identifying, troubleshooting, and resolving problems outlined in your strategy.
Metadata management
Another critical component of data governance is metadata management, which focuses on maintaining consistent definitions of data across systems. This consistency is important to ensure data flows smoothly across integrated solutions and that everyone has a shared understanding of the data.
The framework should include details on data definition, data security, data usage, and data lineage. In doing so, it should make it possible to clearly identify and classify all types of data in a standardized way across the organization.
Data stewardship
Data stewardship is the practice that guarantees your organization’s data is accessible, usable, secure, and trustworthy. While the data governance strategy determines your organization’s goals, risk tolerance, security standards, and strategic data needs to set high-level policies, data stewardship focuses on making sure those policies get implemented.
To achieve this follow-through, data stewardship assigns clear roles and responsibilities for various initiatives outlined in the strategy.