Examples of Data Governance Frameworks
- The data management maturity (DMM) model helps identify and improve data processes that are vital to governance compliance.
- Similarly, the data management capability assessment (DCAM) model focuses on ownership and metadata quality.
- The data management body of knowledge (DMBoK) encompasses data architecture, security, and core intelligence development functions.
Enterprises require an optimized framework to adopt standard practices based on their unique data requirements and in-house workflows. If required, they can also collaborate with a data governance company to dedicate internal resources to more business-critical tasks and handle IT-related compliance burdens.
10 Best Practices for Effective Data Governance
- Specifying What You Expect to Achieve Through Governance
Goal determination can target addressing regulatory expectations or combating digital corporate espionage attempts. Otherwise, some brands might be concerned about data breaches or ransomware attacks. Likewise, governance objectives might involve improving data quality for more reliable insight discovery and reducing issues pertaining to metadata handling. In other words, each governance goal will necessitate a distinct set of strategies, tools, and expertise. That is why confirming them from the get-go is recommended.
- Documenting Data Ownership and Role-Tied Responsibilities
Successfully implementing a data governance framework requires clarity about user roles, related responsibilities, and data ownership record-keeping. For example, some users will oversee business intelligence and reporting activities, while others will have authorization to process a few enterprise databases with data analytics solutions for specific insights. Therefore, documenting the origin of data, user-made modifications, and role-based responsibilities is essential to trace those activities.
- Finalizing and Communicating Company Data Policies
Data governance policies increase awareness of responsible data management. Those policies also offer more reliable guidance when data issues arise. That is why specifying and communicating data governance policy updates is vital for stakeholder coordination. Particular data policies can also help demonstrate a company’s commitment to upholding stakeholders’ privacy rights and embracing advanced technologies with an ethical approach.
- Ensuring Consistent Data Quality
High-quality data allows for actionable insight exploration. In other words, it facilitates robust decision-making and risk analyses. As a result, effective data governance practices involve validation rules and bias reduction methods. In addition to standardization processes, frequent audits of quality assurance metrics are beneficial. Moreover, rectifying data quality issues due to inconsistent operations or human errors will be vital.
- Implementing Strict Data Protection Technologies
Storing data with adequate encryption tech tools assures improved resilience against cybercriminals’ attempts to steal critical information. At the same time, protecting data from unauthorized access or data breaches can necessitate smart firewall management strategies. Besides, periodically examining the physical and cloud-powered enterprise data repositories for suspicious metadata or malware presence is among the best practices.
- Establishing Data Classification Norms
Data classification lets organizations specify rules about analytics, access, sharing, and record updating based on sensitivity. After all, businesses cannot treat public and confidential details in identical manners. Unsurprisingly, customizing role-defined user access controls with the right data privileges demands appropriate data classification. Furthermore, context can offer more insights into data types and necessary tools to process each data asset. For illustration, think of some organizations restricting the tools employees can use to modify specific files.
- Developing a Data-Driven Workplace
The data governance best practices empower managers to create data-centric environments. For instance, employees can upgrade their decision-making skills using actual evidence of which ideas work the best instead of relying on intuition. Such trends also help avoid confusion in business correspondence and reliance on legacy reporting methods. However, enterprises must invest in encouraging employees to get accustomed to newer data-driven workflows.
- Integrating Automation for Metadata and Compliance Insights
Automated metadata sorting saves time and resources. Whether you want to accelerate cataloging or estimating compliance metrics, prioritizing automation techniques will be a must. The related decrease in manual tasks allows for more efficient governance implementation. The pros of automated governance often increase agility. Nevertheless, the role of human supervisors is also crucial to avoid some downsides of automation tools.
- Observing Performance Indicators
Key performance indicators (KPIs) describing cybersecurity effectiveness and data quality assurance let leaders make necessary changes to current methods. Usage-focused KPIs might also help identify whether employees have utilized the right channels to exchange sensitive information.
On a related note, leaders will likely want to know how governance compliance efforts might impact their finances. Therefore, performance-tracking insights describing the respective return on investment (ROI) are preferable. In the case of data quality assurance, completeness, freshness, reliability, and accuracy are some of the popular KPIs.
- Review and Revise Frameworks
Data governance obligations undergo transition due to regulatory shifts or changes in a company’s business model. For instance, regional expansion or digitalization initiatives impact legal and technological liabilities. When major changes occur, leaders must revisit their governance frameworks and policies. Modifying them will let corporations stay up to date and avoid non-compliance risks.
Conclusion
Adopting the top 10 best practices will aid organizations in developing and deploying an effective data governance framework. Doing so will boost their resilience to unwanted interferences hurting data operations. That is why every leader concerned about enterprise data integrity, security, and governance compliance must find the best tools and strategies.
Seeking external experts’ assistance and investing in training in-house teams to excel at data protection and user access management are some ways to boost governance metrics. At the moment, the data governance market size is poised to grow beyond the 5 billion USD mark. In response, more stakeholders will continue to study prevailing frameworks, and brands will want to find the best talent to assist them in compliance initiatives.