Quattrone Center · UPenn Law

Data Collection & Analysis

An interactive guide for CIUs & Innocence Organizations

Home

Start Here

  • Why Collect Data
  • Quick Start
  • The Four-Step Framework

Interactive Tools

  • Self-Assessment
  • Which Platform Is Right?
  • AI Data Health Check

Core Modules

  • Privacy & Security
  • AI & Modern Tools
  • Digital Evidence
  • Dashboards & Reporting
  • Data Fields

Modular and regularly updated. Built for CIUs and Innocence Organizations of all sizes.

Quattrone Center · UPenn Law

Data Collection & Analysis

Quattrone Center · UPenn Law

Data Collection & Analysis

An interactive guide for CIUs & Innocence Organizations

Home

Start Here

  • Why Collect Data
  • Quick Start
  • The Four-Step Framework

Interactive Tools

  • Self-Assessment
  • Which Platform Is Right?
  • AI Data Health Check

Core Modules

  • Privacy & Security
  • AI & Modern Tools
  • Digital Evidence
  • Dashboards & Reporting
  • Data Fields

Modular and regularly updated. Built for CIUs and Innocence Organizations of all sizes.

Start Here

Why Collect Data

Five reasons measurement is worth the effort, even for a small unit with limited time. Data turns scattered case files into a tool you can act on, point to, and improve.

1

Informed Decision-Making and Evidence-Based Practice

Data lets you see which case factors recur, where reviews stall, and what actually drives exonerations, so decisions rest on patterns rather than anecdotes.
2

Accountability and Transparency

Clear records let you show your work to courts, boards, and the public, and hold systems to a measurable standard.
3

Documenting Injustice and Patterns

Aggregated cases expose systemic problems, like a recurring lab, an unreliable informant, or a single detective, that no single file reveals on its own.
4

Securing Funding, Support, and Advocacy Impact

Funders and legislators respond to numbers. "We reviewed 240 cases and freed 12 people" opens doors that a narrative alone cannot.
5

System Improvement

Data closes the loop: measure outcomes, find what is broken, fix the process, and confirm the fix worked.

Treat inconsistency as information

If your data comes back inconsistent, treat it as a signal, not a failure: what does it tell you about your intake or workflow?