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Our purpose
Main Objective
Establish the authority, roles, responsibilities, and control mechanisms for the comprehensive data management of the University of Cundinamarca, in order to ensure that academic, administrative, financial, research, and extension information is of high quality, reliable, integrated, timely, secure, and used in an ethical, transparent manner and in compliance with current regulations, supporting decision-making, institutional planning, accountability, and the fulfillment of the University's missionary functions and its Transmodern Digital Educational Model (MEDIT).
Scope
Data Governance applies to all academic, administrative, financial, and mission-critical data of the University of Cundinamarca, covering its complete life cycle and involving all areas responsible for its generation, management, and use, under policies of quality, security, and regulatory compliance. Likewise, other aspects will be covered, such as: data analysis, data mining, emerging technologies, Python programming, data visualization and cleaning, data architecture, database design, legal regulations and data protection regulations, and information security.
Address
Data governance will be led by the Institutional Planning Directorate and is composed of the Director of Institutional Planning, the Director of Systems and Technology, the Information Security Management System Coordinator, a professional appointed by the Institutional Planning Office, and the Data Protection Officer.
Data Governance Principles
Data Quality
The data that make up the data infrastructure of the University of Cundinamarca must meet quality characteristics such as: accuracy, completeness, integrity, timeliness, consistency, relevance, accessibility, confidentiality, and reliability, for which procedures for the maintenance and management of datasets, especially the university's master data, must be executed.
Availability and usability
Ensure that data is accessible and usable whenever they require it.
Integrity
Maintain data accuracy, completeness, and consistency.
Security and privacy
Data must be protected against unauthorized access, misuse, loss, or alteration, while also ensuring compliance with personal data protection regulations, respect for the confidentiality of the information, and adherence to regulations.
Confidentiality
Ensure that data is accessible and usable only by authorized individuals or processes.
Leadership and strategy
Data Governance begins with visionary and committed leadership. Data management activities are guided by a data strategy which, in turn, is driven by the entity's strategy.
Guidance
Data Governance must provide clear guidelines, standards, procedures, guides, and instructions that direct all areas of the organization in the management, use, quality, and protection of data.
Shared responsibility
In all areas that manage data, data governance is a shared responsibility among data administrators and the various professionals who manage them, taking into account the MEDIT principle of translocal and transdisciplinary openness, highlighting the importance of integrating students (Opportunity creators), knowledge managers, and the general community.
Multi-level
In entities that cover a wide geographic range, data governance occurs both at the general and local levels, and often at intermediate levels if any, since at each level data can be created, analyzed, processed, and decisions can be made based on it.
Operational framework
Since data governance activities require the coordination of all functional areas, an operational framework must be established that defines responsibilities, security, quality, and interactions regarding the data or dataset.
Based on principles
Principles are the basis of data governance activities, and especially of data governance guidelines. These principles can mitigate potential resistance that may arise to maintaining data accountability, security, confidentiality, availability, safety, and quality.
Institutional indicators
13.291
Total students
37
Academic programs
7
Regional units
Undergraduate
22
Programs
12,811
Students
Graduate studies
15
Programs
480
Students
Geographical distribution
Students by regional campus
Source: SNIES - 2025-2
Programs by Enrollment
Source: SNIES - 2025-2
Theoretical framework
Data Governance
Definition of guidelines, roles, and responsibilities for managing data as a strategic asset.
Master and Reference Data Management
Definition and management of key and shared data (enrollments, teachers, students, programs, etc.) ensuring consistency and quality.
Data Quality
Ensure the data is accurate, complete, consistent, and reliable.
Data Security
Protection of data against unauthorized access and ensuring its confidentiality, integrity, and availability.
Data Storage and Operations
Define the set of practices, processes, technologies, and controls aimed at managing the storage, maintenance, availability, performance, and continuous operation of data within an organization. Its purpose is to ensure that data is correctly stored, protected, available, and accessible, supporting both business operations and analytical processes.
Data Integration and Interoperability
Combine, consolidate, and enable the exchange of data between different systems, applications, and organizations, ensuring that information flows consistently, timely, and reliably. It ensures that data can be shared and understood across multiple platforms, eliminating information silos and enabling a unified view for operations and decision-making.
Metadata Management
Manage information about the data (name, origin, creation and modification dates, author, size, storage location, security, format, etc.).
Document and Content Management
Comprehensive management of unstructured and semi-structured information (documents, images, emails, videos, case files, among others) throughout its lifecycle, ensuring its organization, access, security, traceability, preservation, and final disposition. Documentary information must be made accessible, reliable, and aligned with legal, administrative, and operational requirements, supporting institutional management and decision-making.
Data Modeling and Design
Define, structure, and represent an organization's data (conceptual, logical, and physical models) in a way that correctly reflects business reality and efficiently supports information systems and analytical processes. It establishes how data is organized, related, and stored, ensuring consistency, integrity, semantic clarity, and reusability across the organization.
Data Architecture
Define the structure, organization, flow, and integration of data within the university, establishing the principles, models, standards, and components necessary to manage data as a strategic asset. It acts as a framework that guides how data is captured, stored, integrated, shared, and consumed, ensuring consistency across systems, alignment with the business, and support for digital transformation.
Analytics and Business Intelligence
It supports data analysis for decision-making through analytical repositories and disruptive tools such as big data, artificial intelligence, and machine learning. It is the process of examining, transforming, and interpreting data through the application of statistical, logical, and analytical techniques in order to extract useful information, identify patterns, generate knowledge, and support organizational decision-making.
Artificial Intelligence
Artificial intelligence in Data Governance is important because it can be used to automate, optimize, and strengthen data management processes through advanced storage, processing, and prediction techniques that help improve the quality, security, integration, and analysis of information, contributing to strategic decision-making in the organization.
Integrated Management Systems
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Data Governance
Data Governance
Data Governance
Data Governance
