Data governance is essential for all businesses, but especially for enterprise companies with their petabytes of data. Properly governing your data can ensure it is accurate, consistent, and secure. This helps to protect your company from data breaches and other security threats. This blog post will discuss eight data governance principles that you should live by.
Data Governance Principles
Let’s look at some top principles you can use to guide your data governance efforts.
- Data is a strategic asset of the enterprise. Data is a gold mine for data-driven organizations, as it remains the foundation for critical business decision-making. However, when data isn’t available when needed or has quality issues, it affects the quality of business decisions. Hence, to maximize data value, data governance policies should promote the easy accessibility, processes, and safety of data as it passes through the entire data lifecycle.
- Approach governance with a democratic, integrated approach. Data governance represents and affects organizational processes. The top-down approach, which is the traditional method of governance and involves leaving the establishment of governance policies to the top-level of organizations, can be tough to implement. And though this approach may be centralized, the various domains across an organization use data differently. Therefore, a top-down approach to governance may lead to policies that stifle data access in one domain while improving another. On the other hand, adopting a decentralized or bottom-up approach shares the data governance responsibility across the organization, eases the implementation of policies, eliminates silos, and empowers organizational members to do more with their data. However, this democratic approach makes it easy for teams to establish self-serving policies without considering their effect on the business goal. Hence, before making such policies, the data governance team should define, develop, and communicate a common set of definitions, policies, and standards to guide the process.
- Centralized or decentralized, you need a data governance team. It is important to establish clear roles and responsibilities for data governance within your organization. This will ensure that everyone knows who is responsible for what, and it will help to keep your data organized and consistent. Teams may be as simple as a steering committee composed of executives, data owners who are responsible for a specific data domain, and data stewards who are SMEs in data management.
- Your data governance team must have integrity. Maintaining integrity among data governance participants means that every member practices truthfulness when communicating drivers, constraints, and impacts that affect governance policies. In addition, data governance participants must not seek gain only for their domain, but look towards the success of the whole organization.
- Data governance policies must be transparent. The data governance policy-making process should follow a transparent and ethical approach to ensure easier future policy audits. Hence, while creating and communicating policies, data governance teams must be able to explain how, why, and when they introduced data-related controls and processes to governance policies.
- Clearly define accountability for data. Accountability in data governance means that tasks, duties, and processes are well-defined. The data governance team should record, manage, and communicate rules for the production and use of data to improve the value of data and avoid risk.
- Incorporate metadata management and observability. Metadata helps organizations unlock the potential of their data by providing information and context to the data present and existing relationships among them. For example, metadata presents information on data elements like title, description, tags, categories, author, and modification dates. With proper metadata management, organizations get an in-depth view of the data at their disposal and take deliberate steps towards defining processes that ease access and prevent a breach of compliance regulations. Another factor critical to data governance is observability. Observability helps monitor the health and state of organizational data in near real-time through monitoring, alerts, and troubleshooting. With observability, organizations gain insights into data issues, why these issues occur, and how to prevent these issues from reoccurring in the future. In addition, observability follows the set thresholds of data governance and puts out alerts when rules and policies are compromised.
- Data governance never ends. As the flow of data never stops, so too data governance. Data governance should not be seen as a single, one-time process but as a continuous one. Data continuously grows, and regulations built to protect consumer data safety continuously evolve. Data governance efforts must integrate seamlessly with all organizational processes that involve data. Organizations can leverage metadata to provide insights on data to help create policies that maximize data potential.
Choose a Platform That Enables Data Governance at Scale
Data governance can be challenging to start and maintain. By using these data governance principles and with DataOps platforms like StreamSets, organizations can streamline data governance efforts.
StreamSets uses an open metadata management system that provides organizations with an extensive view of their data, making it easier to create policies that ensure proper data management and align with business needs. In addition, StreamSets integrates with multiple access control and LDAP systems to provide safe and regulated access to data.
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