Data is a crucial asset for businesses today. Ensuring that high-quality data is readily accessible can drive competitive differentiation, healthy business operations and regulatory compliance. Therefore, data governance is a critical initiative for many organizations.
One data governance definition is that data governance (DG) enables control over the creation, distribution, use, maintenance, security and availability of business data. A DG program involves people, processes and technologies for establishing and implementing formal data management policies and procedures to manage internal and external data flows.
In this article, we will explore the importance of data governance and provide tips for developing and implementing an effective DG program.
Importance of data governance
Organizations need good data governance at every stage of their digital transformation. The DG program needs to cover all types of data the company stores, including both unstructured data like documents and pictures, and structured data stored in databases. As data volumes require data warehouses or big data repositories, modernizing your data strategy with a strong data governance capability is an increasingly important step to take.
Here are the top reasons why data governance is important:
- It helps enhance data quality. By implementing effective data governance initiatives, companies can make data more accurate, complete and consistent. High-quality data is essential to effective data analytics and enabling business users to easily access data right at the time they need it.
- It helps ensure data security. DG also helps reduce security risks across the enterprise data architecture by enabling organizations to locate critical data, identify data owners and data users, and assess and remediate risk to critical data.
- It helps with compliance. Regulations and standards set strict requirements for data security, and organizations that fail to follow those information management rules are subject to stiff fines and other penalties. DG programs enable organizations to identify regulated data and ensure data privacy by establishing proper controls.
- It supports business operations. At organizations with poor data management, knowledge workers struggle to find the data they need to gain insights and make decisions, and IT cannot manage data properly because they cannot identify data owners, data users and other stakeholders. DG helps remediate these issues and enable far more effective business operations.
Data governance plan: things to consider
A data governance plan defines data governance efforts. It sets a foundation by defining goals, metrics, roles and responsibilities, guiding principles, and communication strategies. To establish a data governance program, it’s necessary to understand your organizational structure, available resources and decision-making processes.
The best way to start a data governance initiative is to refer to industry best practices and guidance sources, such as the Global Data Management Community or the Data Governance Institute (DGI), which provide expert advice on program development and program implementation.
Key components of a data governance program
Goals: Clearly define the wants and needs of your business, such as improved customer satisfaction or retention, or a reduced number of security incidents. You can identify both quick wins and long-term improvements.
Metrics: Specify how you will assess your program’s effectiveness.
Roles: Include a definition of each role (e.g., data owner, data steward) and organizational body (e.g., Data Governance Council), and specify the associated responsibilities. Here are some examples:
Data stewards are responsible for managing corporate data projects. Data stewardship is an operational duty that focuses on implementing and coordinating policies and procedures — data stewards make data-related decisions, issue recommendations and develop policies. There is an incorrect perception that data governance is only an IT job. But while IT teams are responsible for providing solutions and developing infrastructure services, data stewards must help the IT team by providing guidance on data governance policies and rules.
Data Governance Council is the team responsible for setting up the data governance program, measuring success and gathering metrics.
Data stakeholders are the people who own and use specific data assets. They usually include individuals and teams in human resources, IT, risk management, compliance and legal departments. Their insights and needs should be considered in decisions about policies, procedures, business rules and technology approaches. Involving all key stakeholders is essential to success.
Data inventory: Describe all data domains, e.g., customer data, product data, financial figures.
DG rules: Create rules for controlling the quality, integrity, security and use of data throughout its lifecycle, including rules for engagement between different organizational bodies. Rules are included in policies, requirements and controls.
DG processes: Detail your risk management, regulatory compliance, business process management, e-discovery and other DG processes. Consider including master data management (MDM),which governs master data assets that are critical to business operations and analytics, such as data about customers, finances, products and services, and organizational structure.
Technologies: Specify the technologies to be used to support the data governance program. Include tools for data quality assessment, automated data discovery and classification, data cleansing, storage, and retrieval.
Implementing data governance, step by step
Clear documentation is the foundation for successful deployment of a DG program and will support your program as it matures. The implementation process can be divided in the following steps:
- Define the mission, vision and goals. A good data governance program starts with defining what data governance is for your business. The DG plan should align with the company’s organizational hierarchy, resources, and current data management activities. Develop value statements for each major goal, such as increasing profits. Work with stakeholders to develop metrics to measure long-term value, and be sure to get sign-off from senior executives.
- Assess your current data architecture and DG maturity level. Establish which DG activities are already going inside your organization and which ones need improvement. This analysis will help to determine which areas to focus on.
- Develop policies and procedures. Create and publish a data management policy, a program development schedule, and education campaign, and get confirmation from management. Policies should establish rules of data ownership and requirements for data quality audits, and they need to be updated as the company’s strategies and goals change. Also be sure to specify how policies will be enforced, since failure to follow policies and procedures can lead to the same data quality errors the policies were designed to prevent.
- Define and assign roles and responsibilities. Finding the right teams is critical to success. Because DG is a strategic initiative, it should include a separate strategic unit (a DG council) as a decision-making body. Describe the data management functions of other teams, such as IT, human accounting, customer services and sales, and detail their responsibilities. Different data types require different owners, and these differences also need to be defined.
- Identify and deploy tools. Effective data governance tools are essential to ensuring data integrity and meeting evolving compliance standards and security requirements. Consider tools for data analytics, metadata management, data lineage, data protection, and so on.
- Measure performance and monitor feedback. Every program should include assessment and performance monitoring mechanisms. Effective data governance performance measures can include:
- Measures based on objective criteria, such as a reduction in the number of data issues or user complaints
- Multi-dimensional measures, such as stakeholder satisfaction or issue resolution metrics
- Data quality measures like a data quality index by application or by critical data element
Conclusion
Effective data governance is critical for ensuring data quality, security and compliance, as well as for boosting productivity and cost efficiency. By taking a staged and systematic approach, companies can achieve improvements without embarking on expensive projects. With data governance as a foundational component of their data strategy, organizations make their data a competitive business advantage.
