Using Data at Syracuse

Make Data-Informed Decisions

Data is a powerful tool for informing decisions, and across Syracuse University, faculty and staff are thoughtfully using it to support teaching, research, operations, and planning. Using data often involves navigating important questions like:

  • Am I using the right data for this decision?
  • What does this data really mean in context?
  • Can I trust the quality and source of this information?
  • How do I move from insight to informed action?

Whether you're exploring a question, interpreting data, or planning next steps, you'll find practical guidance here to help you move from information to meaningful action.

Data Access and Availability

Access to data sources is role-based, ensuring that users engage with the information most relevant to their work. Contact dataconnection@syr.edu if you have any questions.

DataInsights is the University’s platform for accessing interactive dashboards and reports that support data-informed decision-making. To access DataInsights off campus, connect to the Syracuse University VPN before navigating to datainsights.syr.edu.

Responsible Data Use

Apply these commitments whenever you use data:

  • Protect the privacy and confidentiality of individuals represented in the data
  • Use data from credible, appropriate sources
  • Avoid selective use of data or conclusions that distort the full picture
  • Seek guidance from data stewards or request support when you are uncertain about interpretation or access

University Data-Related Policies

Using AI with Institutional Data 

Artificial intelligence can support data-informed work by helping users summarize information, identify patterns, generate ideas, and communicate findings. Like any analytical tool, AI should be used thoughtfully and in accordance with University guidance and data privacy requirements. Institutional data should only be used with University-approved AI tools. Human judgment remains essential for interpreting results, considering context, and making decisions.

Learn more about using AI with institutional data

Putting It All Together: The Data-to-Decisions Framework

The 5A Data-to-Decisions Framework provides a practical approach for using institutional data to inform decisions. It complements the guidance above by encouraging users to consider both the technical and human dimensions of data use.

The framework can be applied in both academic and administrative contexts and is grounded in two complementary perspectives:

Technical/Operational
The practical side—how data is defined, sourced, structured, and what it can (and cannot) tell you.

Values & Context
The human side—how data is interpreted and applied, whose perspectives are reflected, and how its use aligns with our mission and supports continuous learning and improvement.

Explore each of the five practices below to strengthen your approach to using data effectively and responsibly.

Assumptions

Clarify data definitions and look beyond first impressions.

  • Data definitions
  • Use of historical or real-time data
  • Avoid assumptions that data are inaccurate
  • Consider other explanations beyond initial interpretation
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Access

Locate and obtain relevant data in a timely and usable format.

  • Location of data and access methods
  • Available filters, tools, and documentation
  • Identify who is and is not included in the data
  • Accessibility of data to appropriate stakeholders
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Analysis

Examine, interpret, and synthesize data to generate insights.

  • Use of appropriate tools
  • Observations overall and by subgroups
  • Context, patterns, trends, and anomalies
  • Acknowledge perspectives shaping interpretation
  • Collaborate to strengthen understanding
  • Recognize confirmation or deficit thinking
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Action

Translate insights into concrete decisions and engage stakeholders.

  • Evidence-based action
  • Assignment of responsibility and resources
  • Feasibility of actions and impact awareness
  • Engagement in implementing actions
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Accountability

Ensure transparent, responsible data use and monitor outcomes.

  • Timelines and checkpoints
  • Impact over time
  • Transparency
  • Institutional alignment
  • Culture of reflective improvement
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