The best data governance frameworks are built with business objectives in mind. Creating and implementing these frameworks requires a multi-faceted team of stakeholders.
A centralized model offers consistency and accountability but may result in longer decision-making processes. A decentralized model provides flexibility and empowers departments but needs more transparency and could lead to efficient control processes.
Studies show that employees who feel committed to their organizations perform better. They believe in the goals and values of their employer on both a professional and personal level, are active members of the organization’s community, and actively support its products, services, policies and procedures.
Achieving these levels of commitment can be challenging and takes time. Many factors impact organizational commitment, including job satisfaction, work stress, and perceived fair treatment by coworkers.
To implement a successful data governance framework, it is critical to create accountability and transparency among stakeholders. Establishing transparent processes around how information is collected, stored, accessed, and shared within your organization will help to build trust and promote collaboration while ensuring that sensitive data is protected and maintained at all times. It will also help to foster a culture of integrity, ensuring that the information gathered and shared is accurate, consistent, and reliable throughout its lifecycle. This will, in turn, allow you to make informed decisions based on high-quality data and avoid costly mistakes. Having clear KPIs that measure the success of your data governance framework, such as compliance rates and information quality metrics, will provide you with a strong foundation for future improvements.
Stakeholder engagement is gaining buy-in and awareness from individuals or groups affected by a company’s activities. This is a crucial component of the data governance framework because change can be met with skepticism and apprehension, especially when it involves altering established processes. You can ease these concerns and increase participation by consistently communicating the value of data governance and demonstrating that it aligns with organizational goals.
Begin by creating a list of stakeholders and breaking them into internal and external groups. Then, for each group, determine their influence and power and organize them into a stakeholder matrix. For example, a community leader with much influence would go in the top right corner. At the same time, an individual with very little power in your organization could be placed in the bottom left.
Once you have your stakeholder map and prioritized key stakeholders, plan regular communication via newsletters, social media updates, quarterly town hall sessions, meetings, etc. This will help you maintain transparency, ensure that your key stakeholders know what is being done to address their issues and celebrate wins.
A data governance framework should align with business objectives and your company’s current business landscape. For example, suppose you’re undergoing a digital transformation and want to implement new processes for employees accessing and using your data. In that case, your governance framework should support those changes.
This will allow you to ensure that all the right people have the information they need at the right time without any bottlenecks or friction. It’s also important to ensure that your governance framework is aligned with existing processes and regulations. For instance, when Varonis was creating its data governance framework, it worked with legal experts to ensure the framework was in line with compliance laws and standards.
Another essential component of a solid governance framework is the creation of processes for monitoring and measuring data quality. This includes defining metrics for assessing data accuracy, consistency, and completeness. It’s also necessary to implement processes for granting and managing data access based on roles and responsibilities, preventing unauthorized access to sensitive data.
A robust data governance framework requires the right technology. After a people-driven structure and processes are in place, the right software tools must be implemented to support these new procedures. The most important component of this technology is a system that provides the transparency and accountability necessary to promote accountable behavior when handling information.
The system must also be capable of managing the data quality improvement aspects of the governance process, such as cleansing, correlating and removing duplicate instances of customer or product records from different systems. Additionally, the system should be able to identify data anomalies and provide alerts.
In addition to these technical features, a good governance system should be able to track user satisfaction and provide feedback on the framework’s success. This is critical for ensuring the framework meets end-user needs and aligns with the organization’s strategic objectives. The best way to achieve this is through communication strategies, change management techniques and leveraging technology tools for streamlined processes. By implementing these tools, an organization can successfully implement a data governance framework that aligns with its business goals.
Data governance initiatives require tools and technology to support the framework, including BI/reporting, ML/AI and advanced analytics. These technologies help to standardize data and create a single source of truth while breaking down organizational silos for greater collaboration amongst business stakeholders.
This includes metadata management, data lineage and access control for centralized visibility into the enterprise’s data assets. It also involves security software that adheres to regulations and shields against breaches. Data governance efforts should also include data cleansing to remove inaccurate, corrupted or duplicate data that can cause serious problems.
Training and communication programs are necessary for promoting data governance across the enterprise. These initiatives provide education on data usage rules and privacy mandates to help employees understand their responsibilities in managing an organization’s information.
Creating a governance framework requires a great deal of work. However, the right strategy can minimize the time and resources needed for successful implementation and ongoing operations. Start with a high-level roadmap for your organization’s governance framework and carve out smaller projects to weave data governance into your existing business processes, IT applications and IT infrastructure.