Data governance involves managing tangible risk.
The data governance practitioner has to balance privacy, security, and access in ways that the organization feels supported in its initiatives and progress.
Regulatory compliance will guide the minimal requirements for access control, lineage, and retention policies, but even these can pose challenges for companies that thrive in organic growth. Laws change, processes are slow to adjust, and risk appetites differ.
The public cloud and data governance go hand in hand.
The public cloud has several features that make data governance easier to implement, monitor, and update. In many cases, these features are unavailable or cost-prohibitive in on-premises systems.
In “Is the Cloud Secure?” Gartner makes the following predictions:
Data governance sets the course and gives the blueprint to well-designed analytics practices.
Through data governance, organizations set rules to manage different types of business data, such as data related to viewer metrics, customer behavior, shopping data, and so on.
The ultimate goal of Data Governance is to ensure that all data are reliable and consistent before they can be used for performance evaluations or competitive insights.
According to Dataversity: Data Governance in the Cloud,some of the more pressing reasons why business data in modern organizations need to be governed are:
So how and where do you begin?
Dataversity: 12 Step Guide for Data Governance in a Cloud-First World suggests identifying a subject matter expert (SME) for developing strong Data Governance policies for data sets. The SME’s job will be to ensure balanced risk management vis a vis the needs of the business entities who access such data. It lists the following as the seven best practices.
“7 Data Best Data Governance Practices in a Cloud-First World”
- Setting a data lifecycle with finite time limits
- Retaining the security context with the moving data to ensure uniform implementation
- Tracking of metadata for enhanced data value resulting in quick access to data
- Tracking of multiple instances of same data
- Developing policies for data integration and data transformation
- All data has an assigned SME
- Managing developed data models