Your company’s data governance is failing. What can you do to fix it?

Addressing data governance issues in a company requires a strategic and comprehensive approach. Fixing data governance issues is an ongoing process. Regularly reassess and adjust strategies to adapt to changing business requirements and technological advancements.

Data governance is an ongoing journey, a discipline, and a habit, rather than a one-time setup. It requires continuous improvement and amendments, in my opinion. The initial step involves assessing your current level of data maturity. Data maturity measures how effectively a company collects, processes, analyzes, and utilizes data. It ranges from low, where data is siloed, inconsistent, and unreliable, to high, where data is integrated, standardized, and trusted. Utilizing a data maturity model or framework, such as the Data Governance Maturity Model (DGMM), can help evaluate your data capabilities and identify gaps. This assessment enables you to pinpoint strengths and weaknesses and prioritize areas for improvement.

Following the assessment of data maturity, the subsequent step involves defining your data vision and goals. It is crucial for these objectives to align with the overall business strategy and company goals. Obtaining stakeholder buy-in is essential to support your specific, measurable, achievable, relevant, and time-bound (SMART) objectives that are in line with your data vision. This ensures that efforts are focused on tangible outcomes that contribute to the organization's success.

The third step involves establishing data roles and responsibilities within the organization. Data roles refer to the specific functions performed by different groups concerning company data, while data responsibilities outline the duties and expectations associated with each data role. Common data roles include data owner, data steward, data analyst, data engineer, and data consumer. Correspondingly, typical data responsibilities encompass areas such as data quality, data security, data access, data documentation, and data reporting. Clarifying these roles and responsibilities ensures accountability and promotes effective management and utilization of data assets across the organization.

The fourth step involves implementing data policies and processes to ensure consistency, accuracy, security, and compliance. Data policies establish rules such as data classification and retention, while processes like data collection and analysis operationalize these policies. Effective implementation maintains data integrity and regulatory compliance.

In the fifth step, it's crucial to monitor and measure data performance to track progress, identify issues, and evaluate results. This involves defining data metrics, indicators, and dashboards to quantify and visualize data performance. Examples of data metrics include data quality score, data usage rate, data value contribution, and data governance maturity. By monitoring these metrics, organizations can gain insights into the outcome and impact of their data activities.

To fix failing data governance, assess the current state, define objectives, establish a framework, engage stakeholders, develop policies, implement processes, educate employees, monitor and measure, continuously improve, and foster collaboration.

Use data governance platforms and tools to enable data governance automation, integration, and monitoring. Overall Reintegrate the data governance processes and technologies plus Streamline and automate the data governance workflows and tasks, such as data discovery, profiling, cleansing, standardization, security, and compliance. Could significantly help improve and fix the data governance framework Of course after an assessment to objectives and ensure identify the main cause of failure