UNE specifications - Government, management, and data quality

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Fecha de la noticia: 31-03-2023

Motivation

According to the European Data Proposal Law, data is a fundamental component of the digital economy and an essential resource for ensuring ecological and digital transitions. In recent years, the volume of data generated by humans and machines has experienced an exponential increase. It is essential to unlock the potential of this data by creating opportunities for its reuse, removing obstacles to the development of the data economy, and respecting European norms and values. In line with the mission of reducing the digital divide, measures must be promoted that allow everyone to benefit from these opportunities fairly and equitably.

However, a downside of the high availability of data is that as more data accumulates, chaos ensues when it is not managed properly. The increase in volume, velocity, and variety of data also implies a greater difficulty in ensuring its quality. And in situations where data quality levels are inadequate, as analytical techniques used to process datasets become more sophisticated, individuals and communities can be affected in new and unexpected ways.

In this changing scenario, it is necessary to establish common processes applicable to data assets throughout an organization's lifecycle, maximizing their value through data governance initiatives that ensure a structured, managed, coherent, and standardized approach to all activities, operations, and services related to data. Ultimately, it must be ensured that the definition, creation, storage, maintenance, access, and use of data (data management) are done following a data strategy aligned with organizational strategies (data governance), and that the data used is suitable for the intended use (data quality).

UNE Specifications for Data Governance, Management, and Quality

The Data Office, a unit responsible for promoting the sharing, management, and use of data across all productive sectors of the Spanish economy and society, in response to the need for a reference framework that supports both public and private organizations in their efforts to ensure adequate data governance, management, and quality, has sponsored, promoted, and participated in the development of national UNE specifications in this regard.

The UNE 0077:2023 Data Governance, UNE 0078:2023 Data Management, and UNE 0079:2023 Data Quality Management specifications are designed to be applied jointly, enabling the creation of a solid and harmonized reference framework that promotes the adoption of sustainable and effective data practices.

Coordination is driven by data governance, which establishes the necessary mechanisms to ensure the proper use and exploitation of data through the implementation and execution of data management processes and data quality management processes, all in accordance with the needs of the relevant business process and taking into account the limitations and possibilities of the organizations that use the data.

Each regulatory specification is presented with a process-oriented approach, and each of the presented processes is described in terms of its contribution to the seven components of a data governance and management system, as introduced in COBIT 2019:

Process, detailing its purpose, outcome, tasks, and products according to ISO 8000-61.
Principles, policies, and frameworks.
Organizational structures, identifying the data governance bodies and decision-making structures.
Information, required and generated in each process.
Culture, ethics, and behavior, as a set of individual and collective behaviors of people and the organization.
People, skills, and competencies needed to complete all activities and make decisions and corrective actions.
Services, infrastructure, and applications that include technology-related aspects to support data management, data quality management, and data governance processes.

UNE 0077:2023 Specification_Data Governance

The UNE 0077:2023 specification covers aspects related to data governance. It describes the creation of a data governance framework to evaluate, direct, and monitor the use of an organization's data, so that it contributes to its overall performance by obtaining the maximum value from the data while mitigating risks associated with its use. Therefore, data governance has a strategic character, while data management has a more operational focus aimed at achieving the goals set in the strategy.

The implementation of proper data governance involves the correct execution of the following processes:

Establishment of data strategy
Establishment of data policies, best practices, and procedures
Establishment of organizational structures
Optimization of data risks
Optimization of data value

UNE 0078:2023 Specification_Data Management

The UNE 0078:2023 specification covers the aspects related to data management. Data management is defined as the set of activities aimed at ensuring the successful delivery of relevant data with adequate levels of quality to the agents involved throughout the data life cycle, supporting the business processes established in the organizational strategy, following the guidelines of data governance, and in accordance with the principles of data quality management.

The implementation of adequate data management involves the development of thirteen processes:

Data processing
Management of the technological infrastructure
Management of data requirements
Management of data configuration
Historical data management
Data security management
Metadata management
Management of data architecture and design
Data sharing, intermediation and integration
Master data management
Human resource management
Data lifecycle management
Data analysis

UNE 0079:2023 Specification_Data Quality Management

The UNE 0079:2023 specification covers the data quality management processes necessary to establish a framework for improving data quality. Data quality management is defined as the set of activities aimed at ensuring that data has adequate quality levels for use that allows an organization's strategy to be satisfied. Having quality data will allow an organization to achieve the maximum potential of data through its business processes.

According to Deming's continuous improvement PDCA cycle, data quality management involves four processes:

Data quality planning,
Data quality control and monitoring,
Data quality assurance, and
Data quality improvement.

The data quality management processes are intended to ensure that data meets the data quality requirements expressed in accordance with the ISO/IEC 25012 standard.

Maturity Model

As a joint application framework for the different specifications, a data maturity model is outlined that integrates the processes of governance, management, and data quality management, showing how the progressive implementation of processes and their capabilities can be carried out, defining a path of improvement and excellence across different levels to become a mature data organization.

The Data Office will promote the development of the UNE 0080 specification to provide a data maturity assessment model that complies with the content of the governance, management, and data quality management specifications and the aforementioned framework.

The content of the guides can be freely downloaded from the AENOR portal through the links provided below. Download requires prior registration. The discount on the total price is applied at the time of purchase.