Education Data Governance Maturity Assessment Model

The Education Data Governance Maturity Assessment Model was developed by IDC in collaboration with Millennium@EDU SUSTAINABLE EDUCATION. The assessment for Education Data Governance is a framework of five stages, critical measures, outcomes, and actions that education-related organizations should consider to effectively advance along the successive stages of data governance competency.

The “Education Data Governance Maturity Assessment Model” Overview

Reveal of the Education Data Governance Maturity Assessment Model developed by IDC in collaboration with Millennium@EDU SUSTAINABLE EDUCATION
Education-related Organizations are under pressure to transform. They are often decades or century-old institutions that need to reinvent themselves to further their role in society. Education leaders are at the center of this transformation and are investing in virtual reality, 3D printing, learning management systems, and big data and analytics (BDA) — technologies that will become cognitive companions in a blended learning environment for both teachers and students. They are also investing in cloud computing, Internet of Things (IoT), and mobility to increase efficiency and collaboration in administrative processes as well as to enhance operational effectiveness and the resilience of Education ecosystems. The big data phenomenon has shed new light on the importance of data, and lots of it. At the core of digital transformation is data, the 1s and 0s that are collected and processed in big data repositories, analyzed, and delivered to applications on mobile devices, in the cloud, and on-premises. Data governance is hard to implement; not worth the time, effort, or cost; and for many organizations, a fourletter word that has bad implications due to past failed attempts. Yes, data governance is hard to implement, consumes resources, and is known for failure; but data without quality will not be able to support digital transformation, and data governance must help achieve the levels of quality required. Data with quality is trusted, available, secure, and compliant, providing the foundation for high-value Education Information for better decisions and outcomes. Data governance can be achieved through Organizations that can put together innovation, integration, and incorporation of people, process, technology, data, and vision into everyday operations.
The Education Data Governance Maturity Assessment Model was designed to support Senior Executives, CIO, Chief Data Officer and Information and Technologies (IT) management roles that are associated with data management in Education-related Organizations and provides a framework of stages, dimensions, outcomes, and actions required for organizations to advance along the successive stages of data governance competency and maturity. The model will help Education-related Organizations to: Assess their current level of data governance competency and maturity; Enable a dialog among Education and Technology executives about goals and actions relative to data governance initiatives; Use the baseline to define short- and long-term goals and plan for improvements across all the dimensions of data governance capabilities.

“The big data phenomenon has shed new light on the importance of data, and lots of it.”

Gabriel Coimbra, group vice president & Country Manager, IDC

“Education Data Governance Maturity Assessment Model” Stages Overview

Reveal of the Education Data Governance Maturity Assessment Model developed by IDC in collaboration with Millennium@EDU SUSTAINABLE EDUCATION
The Education Data Governance Maturity Assessment Model will enable an Education-related Organization to assess its data governance competency and maturity; prioritize data governance technology, staffing, and other related investment decisions; and uncover maturity gaps among Education core areas and between these areas and Information and Technologies (IT) groups. All this in the quest to achieve higher levels of data quality in support of digital transformation and desired outcomes based on organizational goals, specifically to enhance the Education-related Organizations ’s capacity to leverage its data for delivery of better educational services. Education-related organizations will use the Education
Data Governance Maturity Assessment Model to maximize returns on investments (ROIs) in data stewardship, data quality, master data management, data integration, and related technology, people, processes, and organizational structures. In addition, Education-related Organizations will be able to use this tool to encourage and improve intra- and intergroup collaboration in defining and executing the data governance strategy, focused on student and faculty needs; and in promoting and encouraging higher levels of data integrity, resulting in data being efficiently used as a tool for improvement of educational outcomes.

DIMENSIONS OF EDUCATION DATA GOVERNANCE ASSESSMENT MODEL

At each stage of the assessment model for education data governance, education-related organizations should consider five dimensions, each of which contributes to the ability to advance toward higher levels of Education Data Governance maturity, with each stage improving the quality of data: trusted, available, secure, and compliant for better education decisions and outcomes. Successful deployment and use of education data governance depends on a multipronged approach guided by a strategy that accounts for not only technology but also human and capital resources, business and IT processes, and the data. These dimensions are: Vision, People, Process, Technology, and Data.

Includes attributes such as strategy, capital and operational budgets, performance metrics, sponsorship, and project and program justification.

Includes attributes such as organizational structures, business-IT collaboration, accountability skills, and training.

Includes attributes such as the planning for data governance processes and definition and implementation of policies to control governance activities. It also includes establishment of metrics, measurement, and monitoring to evaluate program and data quality.

Includes attributes such as the appropriateness, integration, support for standards, and performance of technology and IT architecture to all the relevant data processing and governance workloads.

Includes attributes such as the architecture, development and operations management, trust, security, and compliance of multistructured data.