Focus Areas for a Data Governance Board
A data governance board is a group of people who work together to develop an organization’s policies and practices in order to treat data as a strategic asset. The following represents a collection of topics that this group should consider supporting, executing, and/or developing policy around. Not all topics will apply to every organization, and it’s unlikely that more than a few can be done in parallel. The collection below is not in any particular order.
These topics help you focus on the people in your entire organization, ensuring they have the valuable skills to help solve challenges and use data effectively.
- Governance Committee Participation. Although a governance group can have many members, a core team that includes a delegate of the chief executive, a lawyer, an information technology professional, and a geospatial information systems professional help ensure that a large number of the data governance efforts can be accomplished.
- Hiring and Retaining Talent. Work with hiring managers to ensure potential employees who need to work with data are properly screened for appropriate skills and, if necessary, security clearances.
- Partnerships. Identify and connect with outside organizations to help increase the value of internal data by getting their assistance with the data collection, analysis, and decision making.
- Stewardship. Identify key individuals within your organization who are responsible for maintaining data which is needed to conduct business and ensure they are included when it comes to conversations about the data they take care of. Every major data asset (see “Inventory,” below) should have a data steward.
- Staff Skills. A small investment can go a long way when staff are trained in data skills. Begin by assessing the current state of employee skills. Then, develop and execute a plan to increase their experience and expertise, even if it means using free online video tutorials.
These topics help you focus on the data within your organization and optimize it to provide the greatest benefit.
- Inventory. Perhaps the most important aspect of treating your data as a strategic asset is knowing what you have, where it is, and who maintains it. Many other data management activities depend heavily upon this knowledge.
- Risk Management (Security & Privacy). Develop or leverage an information classification policy to begin understanding what data can be public and what data can put the public (or your colleagues) at risk if it were widely available. This classification scheme informs a variety of technical mechanisms to protect the data and also helps you react appropriately to inevitable breaches.
- Quality & Standards. Undertaking efforts to improve quality makes data more consistent, trustworthy, and reliable. Standardizing data (e.g., how addresses are stored) makes it considerably easier to connect data from multiple sources together. Both of these aspects make analyses and decision making exponentially more effective.
- Disaster Recovery, Archiving, & Retention. Ensure that plans and practices are in place for backing up, restoring, and storing data for long periods of time. This helps your data-dependent organization stay operational in a crisis and in compliance with statutory requirements.
These topics help you focus on where and how data is stored, and who in your organization (and beyond) has access to it.
- Infrastructure. Identify the mechanisms by which data is collected, stored, and maintained to help in standardizing best practices and technologies across your organization.
- Internal Access. Improve internal access by identifying needs for data access, especially between different departments or divisions. Then implement tools and provide training to ensure that staff can collaborate effectively.
- Public Access & Engagement. Public access to non-sensitive data will help increase trust, support dashboards, and enrich relationships with partners outside of government. The data governance group directs these efforts and ensures they are aligned with strategic priorities.
- System Integrations. Enable data to flow between different information systems. This work requires extensive coordination and planning. It benefits greatly from a group who has a solid understanding of your organization’s array of technologies and data.
- Warehouses and Lakes. Beyond system integrations: plan, implement, and maintain large-scale data stores that are used for reporting and/or analytical purposes. Coordinate separate efforts that may be underway in different agencies or departments.
These topics help you focus on using data as a strategic asset to achieve the objectives of your organization. They become significantly easier when activities in the other sections above have been completed.
- Outcomes & Prioritization. The solutions for many challenges can involve the use of data. Staff focused on this type of problem solving are often overwhelmed with demand, so managing this work across the organization helps focus on important successes and set expectations for those who may be delayed.
- Performance Analytics. Although organizations may often have a dedicated person to lead performance measurement efforts, many of the individual measures can be drawn from data sources across the organization. Key organizational decisions are often made when reviewing performance information, so ensure it is of the highest possible quality.
- New Initiatives. New or re-engineered business processes will have new data needs. Use these opportunities to align transformative changes with your organization’s strategic ownership of data. Ensure procurement contracts have language that empowers your organization to own and use its own data.
- Analytics, GIS, & Data Science. Establish a distinct team or identify existing talented individuals who can dig deeply into your data to identify opportunities for improvement. These projects are generally started off as experiments, though it’s quite common for them to be continually used in service of improved efficiency, better prioritization of issues, and so on.
Lessons From Leading Chief Data Officers (January 25, 2017)