The scope of data management is broader than data governance. It can be defined as the practice of ingesting, processing, securing and storing an organization’s data, where it is then utilized for strategic decision-making to improve business outcomes. While this is inclusive of data governance, it also includes other areas of the data management lifecycle, such as data processing, data storage and data security. Since these other areas of data management can also impact data governance, these teams need to work together to execute against a data governance strategy. For example, a data governance team may identify commonalities across disparate datasets, but if they want to integrate them, they’ll need to partner with a data management team to define the data model and data architecture to facilitate those linkages. Another example can include data access, where a data governance team may set the policies around data access to specific types of data (e.g. personally identifiable information (PII)), but a data management team will either provide that access directly or set the mechanism in place to provide that access (e.g. leverage internally defined user roles to approve access).  

Implementing a data governance framework can increase the value of data within your organization. Since data governance helps improve overall data accuracy, it also impacts outcomes based on that data, which can range from more simple day-to-day business decisions to more complex automation initiatives. Other key benefits include:

• Drive scale and data literacy – Limited data access across an organization can limit innovation and create dependencies on subject matter experts (SMEs) within business processes. Data governance practices create a pathway for cross-functional teams to come together to create a shared understanding of data across systems (e.g. reconciling differences domain-agnostic data). This shared understanding can then manifest itself through data standards, where data definitions and metadata are documented in a centralized place, such as a data catalog. This documentation, in turn, becomes the foundation for self-service solutions, such as APIs, which enable consistent data and federated access to it across an organization.   
• Ensure security, data privacy and compliance – Data governance policies provide a way to meet the demands of government regulations regarding sensitive data and privacy, such as the EU General Data Protection Regulation (GDPR), the US Health Insurance Portability and Accountability Act (HIPAA), and industry requirements such as Payment Card Industry Data Security Standards (PCI DSS). Violations of these regulatory requirements can result in costly government fines and public backlash. To avoid this, companies adopt data governance tools to set guardrails, which prevent against data breaches and the misuse of data.
• High-quality data – Data governance ensures data integrity, data accuracy, completeness and consistency. Good data allows companies to better understand their workflows and customers as well as how to optimize their overall business performance. However, errors in the performance metrics can steer an organization in the wrong direction, but data governance tools can address potential inaccuracies. For example, data lineage tools can help data owners trace data through its lifecycle; this includes any source information or data transformations that have been applied during any ETL or ELT processes. This enables close inspection of the root cause of any data errors.
• Promote data analytics – Quality data lays the foundation for more advanced data analytics and data science initiatives; this can include business intelligence reporting or more complex predictive machine learning projects. These can only be prioritized when its main stakeholders trust the underlying data; otherwise, they may not be adopted.

Although the benefits of data governance are clear, data governance initiatives have a number of hurdles to overcome to achieve success. Some of these challenges include:

• Organizational alignment: At the onset of a data governance program, one of the largest challenges will be to align stakeholders across the organization around what the key data assets are and what their respective definitions and formats should be. Regulatory policies can put some structure around conversations around customer data, but it may be more difficult to agree on other datasets that fall under master data management (MDM), such as more product-specific data.
• Lack of appropriate sponsorship: Good data governance programs generally require sponsorship at two levels—the executive level and the individual contributor level. Chief Data Officers (CDOs) and data stewards are critical in the communication and prioritization of data governance within an organization. The Chief Data Officer can provide oversight and enforce accountability across data teams to ensure that data governance policies are adopted. Data stewards can help promote awareness of these policies to data producers and data consumers to encourage compliance across the organization.
• Relevant data architecture and processes- Without the right tools and data architecture, companies will struggle in their deployment of an effective data governance program. As an example, teams may discover redundant data across different functions, but data architects will need to develop appropriate data models and data architectures to merge and integrate data across storage systems. Teams may also need to adopt a data catalog to create an inventory of data assets across an organization, or if they already have one, they may need to set up a process for metadata management, which ensures that the underlying data are relevant and up-to-date.