Key Attributes of Effective Data Governance
The data governance strategy must be structured and contain four key components: a shared responsibility, iterative principles, documentation, KPIs, and business metrics. These elements ensure that the data produced is clear, consistent, and manageable. Standardization of naming conventions is a vital part of data governance, as it prevents isolating and redundant data. For instance, analyzing data requires having the correct version of a sign-up.
The Agile methodology promotes a process that is iterative and flexible. Instead of drafting policies and processes, the project team focuses on curating and documenting data assets, such as those that provide 80% of business value. As a result, the project team progressively reaches different data segments and redefines roles and responsibilities. In this way, the organization does not place theory before practice or bureaucracy before tangible returns.
Eventually, enterprise-wide data governance should be an enterprise-wide effort, not a one-off project. Often, it begins as a project, but the processes developed should be repeatable and reusable. In addition, the project budget should be set to sustain the governance effort long term. Finally, the IT team should focus on the long-term benefits of data governance to ensure that it remains an ongoing success.
Data governance is essential to preventing data errors and blocking the misuse of sensitive information. Data governance can also help you create, monitor, and enforce uniform policies for data use. Data governance can help you strike the right balance between data collection practices and privacy mandates. It is a critical component of digital transformation initiatives. The ability to leverage data from different sources has the potential to drive tangible business benefits for an organization. However, effective data governance requires a strong shared responsibility between all business units. For example, while data governance used to be an IT responsibility, it is now a cross-functional issue. In other words, it should be implemented across the entire organization, not just IT. Data governance is more effective than you may think, and it can help you make better decisions.
One key attribute of effective data governance is documentation. Leading organizations create tangible metrics that measure the impact of data governance, such as the time spent on data science and dollar losses resulting from poor-quality data. In addition, by tying data governance efforts to the ongoing transformation effort of the organization, top management will stay interested and supportive. Data governance must be linked to ongoing transformation efforts such as omnichannel enablement and digitization to be effective. Enterprise resource planning modernization, for example, is often dependent on data availability.
Effective data governance requires the participation of various stakeholders. The process of requesting data from different sources must be reviewed and approved by the governance team. The data management team must understand what constitutes an effective compliance process, including standards and processes and metadata capture. It is important for compliance purposes.
For data governance to be successful, specific business indicators must be addressed. Data-quality concerns, domains, and use cases must all be considered by organizations. They should also follow iteration principles and use needs-based governance. They can also reprioritize their data-quality issues daily to maximize the benefits to the business. In addition, they should establish a data governance steering committee that includes the senior executive leadership and executives from different functional areas. As a starting point, prioritize data assets by domain and criticality. Critical data represents 10 to 20 percent of an organization’s total data. It should be treated with care, ongoing quality monitoring, and precise tracking of its flow. Less important elements can be handled more lightly with ad-hoc quality monitoring. The data-quality metrics of a data governance program should measure progress and value to the business.
The goals of effective data governance should align with the enterprise’s strategic direction. Data and digital assets serve as the fuel that drives business. The governance team must determine the data necessary for the various processes to ensure compliance with data regulations. However, compliance should be an outcome, not the sole driver of the governance strategy. So let’s examine what should be the KPIs of effective data governance and what are the benefits associated with it? A good data governance project should have standard processes and a common language. A business glossary, for example, provides a knowledge base for all employees. The data model shapes the structure of the company’s data and guides how data should be processed. Data governance also requires the development of a data literacy campaign. Finally, data governance involves defining data quality and implementing processes and policies that ensure that the company’s data is accurate and reliable.