What is Data Governance? In brief, it’s a decision-making process aimed at improving the quality and trustworthiness of enterprise data. It also includes the control of access and the creation of value. To understand this process, here are some key points:
It improves the quality and trustworthiness of enterprise data
A key component of data governance is creating policies, standards, rules, and controls to protect data from unauthorized access. This ensures data is used consistently across applications and complies with applicable regulations and internal policies. In addition, effective data governance should be well documented so that employees and management can review it for accuracy and completeness. This will increase the trustworthiness and quality of data across the enterprise.
Extensive data systems introduce new governance challenges and requirements. Traditional data governance programs focus on structured data stored in relational databases. However, big data environments often contain unstructured, semi-structured, and unstructured data on various platforms.
It is a decision-making process.
Data governance begins with a strategic plan for how to use the collected data. This plan is driven by a steering committee, a high-level group that manages all data governance efforts. The committee should include stakeholders from all top-level organizations, including leadership, and has the authority to allocate budget, develop policies, and push projects to the top of the priority list. However, the process cannot be too complex and must be flexible enough to accommodate exceptions.
The framework for effective data governance includes clearly defined authority for creating and enforcing policies. According to Panian, an effective data governance practice begins with establishing data policies. These policies should be based on business principles and identify the scope of data as an asset. The guides help determine what procedures are needed, and the process is typically collaborative. Data governance aims to help businesses make decisions, improve operations, and protect assets.
It involves access control.
Data Governance sets the rules and procedures governing the use, distribution, and ownership of data assets. It involves all aspects of data – people, processes, and technologies. As the concept encompasses multiple aspects of data, it can be challenging to implement in a single project. According to Business Application Research Center (BARC), starting small with a manageable prototype project is best.
Often, a steering committee is involved, with the responsibility of guiding the entire data governance process. The steering committee should consist of senior executives or other top-level organization representatives, business managers, and data stewards. This committee should also involve the executive team, who have the power to set policies and allocate budgets. This committee should also be able to push projects up the priority list.
It creates value
How does data governance create value? First, it’s essential to link government to ongoing transformation efforts. Digital transformation and omnichannel enablement are just two examples. Both require modernizing enterprise resource planning (ERP) and improving data quality. To ensure continued support from senior management, governance efforts should be linked to transformation themes. This can be as simple as reshaping the organizational structure to move governance to the product team or an internal transformation process. By identifying data domains and prioritizing data deployment, DMOs can accelerate use cases that require high-quality data.
When Data Governance is effective, it can boost your reputation with customers. Good data quality can lead to improved customer interactions, leading to higher customer satisfaction and loyalty. The data should also drive business performance improvements. This is why data governance should be aligned with business requirements. Iterate daily to review and adjust priorities to maximize the value of data. Incorporating this principle is vital. Adapting to change and incorporating iterative principles is crucial to successful data governance.
