Learning Experience



Throughout the Institute, you’ll build applied skills through interactive scenarios, small group exercises, facilitated discussions, and practical insights from experienced leaders—all designed to reinforce real-world application.

What to Expect:

  • Learning Activities – Interactive discussions, group work, and scenario-based learning
  • Assignments – Practice exercises and real-world applications
  • Recommended Resources – Readings, videos, and tools for deeper exploration
  • Reflections – Structured time to consolidate learning and set actionable goals

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Agenda

To complete the Institute and earn the microcredential, participants will commit approximately 16 hours over 4 days, including 12 hours of required live virtual sessions and 4 hours of applied learning activities integrated throughout the experience.

Please Note: To get the most out of this Institute, we strongly encourage you to step away from day-to-day responsibilities and, as much as possible, be fully present throughout the program.

All times listed in Eastern Time (ET).

Prework Monday, February 22
10:00 a.m.–2:00 p.m.		 Tuesday, February 23
10:00 a.m.–2:00 p.m.		 Wednesday, February 24 Thursday, February 25
10:00 a.m.–2:00 p.m.
~1 hour of preparation activities before the Institute begins.

Institute Welcome

Data Value + Data Life Cycle

Data Governance

Asking Research Questions

ID and Classify Data Sources

Interpreting Data Types

Evaluating Data Quality

Data Visualization

Break day with approximately 2 hours of independent work.

Data Storytelling

Institute Closing Ceremony

**Please note that sessions are not recorded. Dedicated break times are scheduled throughout.

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Competencies

Competency 1: Data Value, Life Cycle, and Governance

Learners will be able to: Recognize the value of data, explore the data lifecycle, and assess their institution’s data governance policies against higher education best practices.

  • Recognize and communicate the strategic value of data as an asset in advancing institutional goals with effective decision-making and institutional effectiveness.
  • Examine the full data life cycle—from collection to processing, storage, and preservation—while considering the varying stakeholder responsibilities at each stage.
  • Identify institutional data governance policies, examine leading practices in higher education, and evaluate how well the organization’s approach aligns with those standards.
Competency 2: Clarifying Research Needs and Identifying Data Inputs

Learners will be able to: Formulate purposeful questions, identify relevant data sources, and interpret key data elements to support effective analysis.

  • Explain how crafting clear, purposeful questions guides the selection of appropriate data sources, improves the efficiency and effectiveness of query and analysis, and ensures the deliverable satisfies the request.
  • Identify relevant internal and external data sources, including access procedures and timelines associated with each.
  • Recognize and apply functional and technical definitions to identify relevant data sources and accurately classify variables by data type for effective data use.
Competency 3: Evaluating Data Sources for Quality, Relevance, and Effective Use

Learners will be able to: Analyze data types and quality and refine data questions to support accurate, context-driven decision-making.

  • Identify and interpret different types of data by analyzing their sources, structures, and relevance to specific decision-making contexts.
  • Assess the quality of a dataset by evaluating its completeness, accuracy, and timeliness in relation to defined analytical goals.
  • Evaluate and refine the initial data question to ensure alignment with available data and the intended purpose of the analysis.
Competency 4: Visualizing and Presenting Data to Drive Decisions

Learners will be able to: Extract actionable insights and communicate them through tailored, context-rich data stories that drive informed decisions.

  • Evaluate findings to assure they address institutional needs and are accurate and reliable.
  • Design and recommend audience-specific communication strategies—using clear narratives and visualizations—to effectively convey those insights and support decision-making.
  • Construct data narratives that incorporate contextual background and institutional knowledge to enhance interpretation and inform strategic action.