Analytics Institutional Self Assessment

Published:
Briefs, Case Studies, Papers, Reports
Author(s) and Contributors:
Source(s) and Collection(s):
Sources(s): EDUCAUSE Publications
ParentTopics:
Analytics Assessment and Evaluation

Abstract

Understanding your institution's current capabilities is an important step in advancing your analytics initiatives. This self-assessment is designed to help analytics professionals understand the level of their analytics capabilities and get some ideas about how to improve those capabilities.

 

Definitions for terms found in rubric:

  • Data literacy: The ability to understand and communicate about data in context, including the range of skills and knowledge necessary to find, manage, evaluate, create, and communicate about data. The depth of data literacy required for each person depends on the role or position.
  • Data users: Anyone at the institution who manages or uses institutional data for institutional reasons such as providing information for decision-making.
  • Data governance: The processes, policies, and goals for managing institutional data, typically including policies and standards for data access, data quality, security and privacy, compliance, and retention and archiving. Data governance fosters collaboration, transparency, and communication throughout the institution.
  • Change management: The business practices and procedures that address the human and cultural aspects of organizational change.
  • Role agility: Designing positions or roles so that they can more easily drop obsolete practices and pick up new ones as work evolves. Role agility may also involve cross training for more seamless workflow during vacations or turnover. Job descriptions are reviewed regularly to add or remove responsibilities and competencies as organizational needs change.
  • Workforce: Non-management employees both in central IT and across the institution.
  • Analytics-focused staff: An employee in any campus unit whose position primarily focuses on managing or analyzing data. Examples include business intelligence analyst, data analyst/scientist, and reporting analyst.

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