Understand your institution’s data assets and needs for resources, organization, and goals to maximize the value of those assets.
- Analytics Maturity: How well is your institution drawing on its data to inform decision making and achieve your goals? Are you leveraging your data to look ahead and be proactive, or primarily looking in the rear-view mirror? Establish a baseline in order to launch an analytics vision.
- Stakeholder Needs: Does your institution understand the needs of various end users? What processes are in place for identifying needs and updating a shared roadmap?
- Data Foundation: Does your current data architecture support your strategic goals? Are there opportunities to improve the standards for collecting, storing, and integrating data sources in the current environment? What are the untapped data sources, and are the systems able to utilize them?
- Data Quality and Quality Assurance: High data quality is a necessary condition for a strong analytics program, and data quality gets much lip service and often too little investment. Do your stakeholders trust institutional reports and analytical results? How many versions of “the truth” are there? Are processes in place to ensure data quality and analytical integrity, and how well are those processes being implemented?
- Personnel and Technology Resources: What are the capacities of your data and analytics tools and talent for data management, integration, exploration, visualization, and modeling? Does your institution have the right resources in place, and if not, does it have an achievable blueprint for assembling the right resources?
- Institutional Research: As an educational institution, is your institutional research function positioned to deliver insights and decision support?
- Performance Measurement: How does your institution – and particularly the data and analytical team(s) within the institution – evaluate performance and progress toward goals?
- Data Governance: What standards, processes, tools, and practices are in place to ensure effective governance of data, from identification and integration of data sources to production and sharing of outputs? How well are roles and responsibilities defined, and how are those roles and responsibilities communicated and incorporated into day-to-day activities? What is the state of organizational discipline with respect to data use?