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Research Report

A Common Currency

This study outlines 20 hallmarks of a data-driven university and provides implementation guidance to help CIOs, their IT teams, and the broader institution better leverage institutional data.

Becoming "data-driven" is increasingly a top priority for higher education executives across the country and a major initiative for chief information officers and their teams.

This study outlines the best practices of how to:

Develop a sustainable data governance initiative Proactively detect and fix data quality problems Get the right data to the right users Mass-customize reporting and analytics to drive user adoption Structure BI efforts for sustained impact

Through the use of case studies, content examples, and best practices identified by leaders in higher education, these resources will help accelerate your institution’s analytics strategy.

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Executive Summary

Intuition-driven decision making no longer sufficient

Increases in higher education costs and greater competition for students, donations, research grants, and other revenue sources have necessitated data-informed decision making at many institutions. Pressure to be more analytical comes from many places, from state legislatures to boards of trustees, and often winds up at the CIO’s door because business intelligence is perceived by campus members to be a technical capability.

Data goverance is crucial to BI success, but often low in maturity

Data governance, the process of creating standards for data elements (e.g., data definitions, potential values, security levels), promotes consistency that enables reliable data comparison across an organization—a fundamental input to BI. Many organizations, however, suffer from low accountability for data governance responsibilities and poor campus engagement in related discussions. Institutions with successful data governance efforts have created sustainable models for ongoing data governance efforts, achieved consistently defined performance metrics across the institution, and created more mature BI efforts.

Data quality everyone's problem, no one's…

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