Data Governance

What is Data Governance?

The key components of data governance are standards, policies, and people.

Data governance describes how an organization manages its data-related processes, rules, standards, and practices. It is a calculated strategy that guarantees data is used, accessed, and managed properly, securely, and in accordance with applicable laws and policies. Data governance's main objective is to create a structure that enables businesses to get the most out of their data assets while reducing the risk of regulatory violations, data misuse, and security breaches.

Data Governance Framework

Every organization will have a unique set of guidelines and practices, but all will establish a foundational framework for data governance based on eight elements. Each of these eight complements the others.

Data Availability and Accessibility: The availability and accessibility of data are its first two crucial components. The information must be available and simple to retrieve. The techniques, length of time, and places that affect how the data can be recovered decide this.

Data Standards: Standards help organizations establish the minimum acceptable data quality level and the criteria for data access. Standards also help ensure that all users have a consistent experience while accessing data within the organization.

Data Integration: Integration determines how data is pulled from various sources into a unified view. It includes weeding out duplicated information and transforming the data to fit a unified format. A comprehensive integration strategy makes it easy for users to not only share data but efficiently use it as well.

Data Quality and Consistency: Closely related to integration, this part of the framework ensures data is accurate and complete. Systems and processes must be uniform, and parameters must be established to verify quality.

Data Compliance and Audit: Data-related procedures must be monitored regularly for compliance with internal and external regulations. This lessens risks and identifies potential improvement areas.

Data Security and Privacy: Safeguards must be put in place to protect data from dangers, including breaches, unauthorized access, and cyberattacks. This entails configuring authentication, encryption, and access controls. Enterprises must also ensure that the handling of information complies with applicable data protection requirements, including the Health Insurance Portability and Accountability Act (HIPAA) in the United States or Europe’s General Data Protection Regulation (GDPR).

Data Responsibility, Accountability, Consultation, and Informed Identification (RACI): This pillar of a data governance framework assigns stewardship roles to responsible individuals or teams and clearly defines the duties required for data ownership.

Data Change Management: The final pillar involves managing changes to data-related processes, technologies, and policies. This ensures all data governance practices are effective and current.

The Significance and Challenges of Data Governance

Data governance creates an organized and secure method for handling information. But it doesn’t just happen at one point. The governance must be in place at every stage of the data lifecycle, from generation and capture to archival and disposal. Doing so empowers business users to make better decisions, lower risk, and uphold stakeholders' trust. Decision-makers are more successful when they have access to accurate, timely, and relevant data. Furthermore, when an organization has a strong data governance structure in place, it can easily adapt to shifting business conditions and seize new possibilities. Strategic reactions and future innovations are possible with trustworthy and well-organized data.

There are, however, several common challenges when implementing a data governance program. They include:

Decentralized data with no single source of truth — As mentioned earlier, a company's departments often collect their own data and develop individual data governance plans at the department level. The disparate data sets mean teams may not know other data exists. Or they may not realize other sets could hold value for them.

Lack of dedicated ownership — Centralized and cross-functional collaboration is critical. Every organization needs a governance leader. Without one, each department will have its own data governance plan rather than the entire organization having a common goal. This situation requires alignment between team leadership and C-level management, plus the willingness to put resources and enforcement authority behind the data governance leader’s role.

Unstructured data and data volume overload — As an enterprise grows, so does the volume and variety of its data, creating challenges and increasing the complexity of data governance. This complexity increases even further for organizations that operate in several countries, especially if data silos exist.

Cultural resistance — Putting data governance into place frequently necessitates changes in organizational culture. Employees who are accustomed to handling data in a particular way may be resistant to change, which could delay progress.

Sustainable data governance program — Many data governance initiatives begin as projects, but keeping up the pace and sustaining the program over time can be challenging. Teams must develop the conviction to stick with the program and uphold its processes.

A comprehensive strategy that includes adjustments to company culture, policies, and processes will solve these challenges. Collaboration between departments and continual leadership commitment is essential for effective data governance, which will lead to long-lasting advancements in data management techniques.

Data Governance Maturity Stages

To help guide companies in the process, data governance can be split into stages of maturity. These stages describe the extent to which a company has developed the process, but more importantly, they can be used as a checklist before moving on to the next level. For each next stage to be reached, more rigorous policies are required.

Commence – Organizations in this beginning stage do not have formal tracking plans and either have very few or no data management processes.

Attain – Companies in this stage have employed at least some data governance practices. Formal documentation standards are now defined in this stage. Additionally, they are more conscious of the breadth and depth of their data. As they embark on a real data governance journey at this point, most will concentrate on simultaneously developing expanded data policies and compliance.

Formalize – At this point, the organization as a whole has implemented data governance guidelines. Data managers and data owners have been assigned. At this level, the company uses technology for data management, and some governance activities are automated.

Compute – An enterprise-level structure for data governance is now evident. Every project and division adheres to standards that are fully documented and regularly audited.

Continuous improvements – Processes are automated, and fine-tune adjustments are made continuously at this stage. 

Data Governance Tools and Platforms

Technology related to data governance is advancing. In the past, companies purchased components of their data governance function from multiple vendors. Today, several features (such as workflow management, reporting, and data and policy catalogs) can be supported on a single platform by a single provider. There are two types of data governance platforms: one offers services specifically for data governance activities; the other combines governance with data management and includes analytics features to provide a comprehensive picture.

Data Governance vs. Data Management vs. Data Stewardship vs. Master Data Management

Data Governance in Healthcare and Life Sciences

Data governance in the health and life sciences sector is a broad and continuing undertaking that calls for cooperation among numerous stakeholders, including healthcare providers, researchers, regulators, and technology specialists. It seeks to strike a balance between the requirement for data-driven insights and the need to safeguard personal information and follow ethical data practices.

Policymakers can use healthcare data to produce evidence-based regulations that improve the quality of and access to healthcare services. Use cases for healthcare organizations may vary depending on their requirements, regulatory context, and technological infrastructure. Once tailored to their needs, a data governance program enables healthcare organizations to uncover impactful decisions from their data. Moreso than any other industry, however, poor data governance can have dangerous results. For instance, a doctor may treat a patient based on dated or inaccurate allergy information, resulting in an unfavorable reaction. Or an AI algorithm trained on patient data might begin making false diagnoses due to biases in the training data. When implemented effectively, data governance can transform healthcare organizations into data-intelligent enterprises.