Data Quality and Software Process Standards: Making Quality Measurable in Information Technology

Ensuring data quality and robust software processes has become a defining factor for business success in today’s digital landscape. As organizations embrace digital transformation—integrating cloud computing, AI, IoT, data analytics, and automation—reliable, measurable quality standards are needed to guarantee trusted data, efficient operations, and scalable systems. This article explores four influential international standards: ISO/IEC 25012:2008, ISO/IEC 25024:2015, ISO/IEC TS 33060:2025, and ISO/IEC TS 33061:2021. These specifications form the backbone of quality in information technology, providing essential requirements and models for data quality and process assessment across the system and software life cycles.

Adopting these standards is now essential for any business implementing new technologies or seeking to scale operations. They guide organizations to measure, monitor, and improve data quality and process maturity—directly impacting security, compliance, productivity, and long-term competitiveness.

Overview / Introduction

Across every sector, organizations are generating and relying upon massive volumes of data and increasingly complex software systems. Whether you’re building customer-facing apps, automating internal workflows, or mining business insights, the integrity, reliability, and measurability of your data and processes are critical drivers of performance—and risk. Poor quality data and ad-hoc software processes can lead to costly rework, security vulnerabilities, regulatory penalties, and lost opportunities.

International standards for data quality and software process assessment bring structure, transparency, and credibility to the way organizations handle information and manage their technology investments. Standardized models and metrics:

• Enable objective, repeatable measurement of quality
• Clarify requirements and best practices for development and maintenance
• Help organizations achieve compliance with global regulations
• Facilitate safe scaling, digital transformation, and innovation
• Reduce business risks by uncovering weaknesses or inefficiencies early

This article provides an accessible overview of four foundational ISO/IEC standards that enable businesses to make quality measurable in information technology: two centered on data quality (ISO/IEC 25012 and ISO/IEC 25024), and two dedicated to process assessment for both system and software life cycles (ISO/IEC TS 33060 and ISO/IEC TS 33061).

By the end of this guide, you’ll understand what each standard covers, who should apply them, and why they are indispensable for building trustworthy, high-performing digital businesses.

Detailed Standards Coverage

ISO/IEC 25012:2008 – Data Quality Model

Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — Data quality model

ISO/IEC 25012:2008 lays the foundation for how organizations define, measure, and evaluate the quality of data retained in structured formats within computer systems. Rather than viewing data as an afterthought, this standard places it at the heart of quality management, acknowledging its central role in supporting business processes and decision-making.

ISO/IEC 25012 defines a comprehensive data quality model comprised of 15 characteristics, each viewed from inherent (intrinsic) and system-dependent perspectives. These include accuracy, completeness, consistency, credibility, currentness, accessibility, compliance, confidentiality, efficiency, precision, traceability, understandability, availability, portability, and recoverability.

Key requirements and specifications:

• The model enables organizations to specify data quality requirements, map out quality measures, and plan or conduct data quality evaluations.
• It supports assessing data in diverse settings—from production to integration and ongoing improvement.
• It assists in evaluating compliance with legal and contractual obligations.
• The standard is designed to work alongside others in the SQuaRE series and remains relevant for structured data held in all types of computer systems.

Who should comply?

• Businesses and IT departments managing databases, data warehouses, distributed systems, and legacy data.
• Any organization relying on accurate, reliable, accessible data.
• Teams responsible for regulatory compliance, data governance, or quality management.

Practical implications for implementation:

• Establishes shared terminology and objectives around data quality.
• Drives error reduction and targeted improvements by making quality attributes measurable.
• Ensures that different stakeholders (IT, business, compliance) can prioritize the most relevant quality characteristics.

Notable features:

• 15 categorizable data quality characteristics
• Inherent vs. system-dependent perspective clarifies responsibility and context
• Support for re-engineering and data life cycle management

Key highlights:

• Defines a clear, multi-dimensional model for data quality
• Enables mapping and evaluating quality requirements across data life cycles
• Facilitates communication and alignment between business, IT, and compliance functions

Access the full standard:View ISO/IEC 25012:2008 on iTeh Standards

ISO/IEC 25024:2015 – Measurement of Data Quality

Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Measurement of data quality

Building upon ISO/IEC 25012, ISO/IEC 25024:2015 provides a robust set of quantitative measures for evaluating data quality according to the characteristics defined in the data quality model. This standard acts as the practical toolkit that turns abstract quality objectives into actionable metrics, allowing organizations to systematically evaluate, monitor, and report on data quality at every stage of the data lifecycle.

Key requirements and specifications:

• Defines measurement functions and provides sample metrics (Quality Measures, QMs) for each of the 15 data quality characteristics.
• Recommends a basic set of data quality measures and explains their application across different entities and lifecycle stages (such as data files, databases, or datasets).
• Guides users in customizing or expanding the measurement set based on specific business or regulatory needs.
• Offers annexes with detailed tables mapping measures to quality attributes and target entities.

Who should comply?

• Anyone responsible for data management, including Data Stewards, Quality Managers, IT departments, data acquirers, evaluators, developers, maintainers, users, and owners.
• Organizations managing business-critical or regulated data in areas like finance, healthcare, supply chain, government, and more.

Practical implications for implementation:

• Allows organizations to baseline current data quality, identify areas for improvement, and track changes over time.
• Supports regulatory and audit requirements by providing objective, transparent evidence of data quality.
• Enables improved decision-making and trust in automated processes, analytics, and AI applications.

Notable features:

• Guidance for defining and applying custom quality measures
• Compatibility across legacy, warehouse, distributed, and web data systems
• Focused on practical, rather than purely theoretical, application

Key highlights:

• Actionable quantitative measures for all 15 data quality attributes
• Guidance for real-world measurement, reporting, and continuous improvement
• Supports a wide array of business functions, from compliance to analytics

Access the full standard:View ISO/IEC 25024:2015 on iTeh Standards

ISO/IEC TS 33060:2025 – Process Assessment Model for System Life Cycle Processes

Information technology — Process assessment — Process assessment model for system life cycle processes

Quality operations do not stop at the data level. ISO/IEC TS 33060:2025 extends measurable quality to the life cycle of entire systems through a rigorously defined process assessment model. Grounded in the requirements of ISO/IEC 33004 and referring to ISO/IEC/IEEE 15288 (the leading standard for system life cycle processes), it provides both the structure and methodology for organizations to objectively assess, benchmark, and improve their system engineering and management processes.

Key requirements and specifications:

• Outlines a two-dimensional assessment model: the process dimension (covering process purposes and outcomes) and the quality dimension (with process attributes and quality indicators).
• Describes process groups (agreement, organizational, technical management, and technical) each with defined purposes and outcomes, addressing all phases—from acquisition and supply to operation and disposal.
• Includes both process performance and process quality indicators, enabling robust internal and third-party assessments.
• Conforms to ISO/IEC 33002 for assessment processes and ISO/IEC 33003 for measurement frameworks.

Who should comply?

• Organizations developing, maintaining, or operating complex systems (manufacturing, transport, healthcare, defense, utilities, telecommunications, etc.).
• Businesses seeking to demonstrate process maturity or capability during supplier selection, audits, or compliance checks.
• Any entity aiming to elevate process quality and operational efficiency while mitigating risk across the system life cycle.

Practical implications for implementation:

• Facilitates continuous process improvement and benchmarking against industry best practices.
• Helps uncover process bottlenecks, redundancies, or non-compliance issues early.
• Supports preparation for regulatory audits or customer/partner assessments.

Notable features:

• Based on the globally recognized ISO/IEC/IEEE 15288 system life cycle model
• Process-centric assessment focused on both outcomes and quality attributes
• Adaptable and extensible to suit organization and industry needs

Key highlights:

• Comprehensive coverage of all system life cycle processes
• Enables both internal improvement and external demonstration of process quality
• Aligned with the latest process assessment and measurement standards

Access the full standard:View ISO/IEC TS 33060:2025 on iTeh Standards

ISO/IEC TS 33061:2021 – Process Assessment Model for Software Life Cycle Processes

Information technology — Process assessment — Process assessment model for software life cycle processes

Focusing specifically on software, ISO/IEC TS 33061:2021 establishes a process assessment model referencing ISO/IEC/IEEE 12207, the internationally recognized software life cycle processes framework. It provides the foundation for organizations to evaluate the maturity, efficacy, and conformity of their software engineering processes, whether developing in-house, outsourcing, or integrating software-intensive solutions across the enterprise.

Key requirements and specifications:

• Mirrors the structure of ISO/IEC TS 33060 but targets software-specific life cycle activities.
• Details process groups covering agreement (acquisition and supply), organizational project-enabling, technical management, and technical processes (including analysis, requirements, design, implementation, and validation).
• Each process includes performance and quality indicators for consistent, objective assessment.
• Conforms to ISO/IEC 33004 and aligns with the ISO/IEC 33020 model for process measurement and capability.

Who should comply?

• Software development companies, IT departments, DevOps teams, and systems integrators.
• Any organization building, maintaining, or acquiring software systems—especially those in regulated environments or seeking competitive differentiation through process maturity.

Practical implications for implementation:

• Proves capability and reliability to clients, regulators, and certification bodies.
• Identifies gaps, risks, and inefficiencies in the software development lifecycle.
• Facilitates smoother integration with partners, suppliers, or customers who require demonstrable software development standards.

Notable features:

• Tailored for the full software engineering lifecycle, from requirements to decommissioning.
• Enables scalability, audit readiness, and organizational learning.
• Compatible with agile, DevOps, and traditional development paradigms.

Key highlights:

• Objective, multi-dimensional software process assessment framework
• Supports continuous improvement and global best practices
• Empowers organizations to benchmark, certify, and optimize software operations

Access the full standard:View ISO/IEC TS 33061:2021 on iTeh Standards

Industry Impact & Compliance

Why Do These Standards Matter for Businesses in Today's Digital Landscape?

Adopting robust, internationally recognized standards for data quality and software process assessment is no longer optional—it's a competitive necessity for organizations embracing digital transformation. These standards deliver:

• Increased productivity: Clear quality objectives and process measurement frameworks eliminate ambiguity, reduce waste, speed up delivery, and foster sustained improvement.
• Enhanced security and risk management: Measurable data quality and documented processes reduce vulnerabilities, improve traceability, and strengthen incident response.
• Scalability and innovation: Standardized processes and data quality controls enable safe expansion, agile transformation, and integration of cutting-edge technologies such as AI and advanced analytics.
• Regulatory and contractual compliance: Objective metrics and mature processes support audit readiness and reduce the risk of legal or financial penalties.
• Improved customer trust and satisfaction: Clients, users, and partners gain confidence in the reliability of systems and integrity of data.

Compliance Considerations

• Many industries—financial services, healthcare, manufacturing, and more—are subject to strict data handling, privacy, and software validation requirements.
• International standards such as the SQuaRE series and process assessment models provide ready-made frameworks for demonstrating due diligence to regulators, customers, and partners.
• Regular self-assessments and third-party audits using these standards can uncover compliance gaps before they lead to larger issues.

Benefits of Adoption

1. Reduce costs by identifying problems early and improving process efficiency.
2. Accelerate innovation through repeatable, measurable, quality-focused processes.
3. Build organizational knowledge and support a culture of continuous improvement.
4. Facilitate collaboration and interoperability in increasingly complex, global ecosystems.
5. Protect reputation and business value by delivering quality products, secure data, and reliable operations.

Risks of Non-Compliance

• Increased IT and business risk—including data breaches, faulty analytics, and failed projects.
• Reduced trust from customers and business partners.
• Loss of competitive advantage due to lackluster process maturity or data reliability.
• Legal or regulatory sanctions, including hefty fines and forced remediation.

Implementation Guidance

Common Approaches to Implementing Data Quality and Process Standards

Organizations seeking to harness the value of these standards should take a structured, phased approach:

1. Assessment and Gap Analysis

• Review current data and process management practices against the requirements and recommendations of the relevant standards.
• Identify gaps, risks, and opportunities for improvement.

2. Set Clear Objectives and Priorities

• Focus first on the most critical quality characteristics (accuracy, security, availability, etc.) and the most strategic or high-risk processes.

3. Develop Policies and Procedures

• Translate standard requirements into practical, organization-specific policies and SOPs.
• Align existing procedures with global best practices.

4. Invest in Training and Culture

• Provide awareness and role-based training to ensure all staff understand the importance of data quality and process maturity.
• Foster a culture of accountability and continuous improvement.

5. Implement Tools and Automation

• Leverage technology—such as Data Quality Management (DQM) platforms, workflow management tools, and automated process assessment solutions—to scale up and standardize measurement and remediation.

6. Measure, Monitor, and Improve

• Use the quantitative measures defined in ISO/IEC 25024 and the assessment indicators in ISO/IEC TS 33060/33061 for regular monitoring.
• Establish feedback loops and continuous improvement cycles, using metrics as guidance.

7. Engage with Certification and Audit

• When required, engage third-party auditors or certification bodies to validate compliance and maturity.

Best Practices

• Start small—with a pilot program focusing on a critical system or data set—then expand.
• Establish cross-functional teams ensuring business, IT, compliance, and operations are all involved.
• Regularly review and update measures, checklist, and process models to reflect new business requirements and technology trends.
• Document everything for auditability, knowledge transfer, and resilience.

Resources

• iTeh Standards Platform: Search, access, and stay updated on the latest international standards.
• Professional associations and communities focused on data quality, software engineering, IT governance (e.g., DAMA, ISACA, IEEE).
• ISO reference materials and training workshops.
• Consulting partners with experience in standards-based process assessment and data quality improvement.

Conclusion / Next Steps

Quality and reliability in information technology are no longer abstract goals—they are measurable, actionable, and foundational for thriving in a digital world. By embracing leading international standards like ISO/IEC 25012, ISO/IEC 25024, ISO/IEC TS 33060, and ISO/IEC TS 33061, organizations unlock a new level of discipline and transparency in how they manage data and software.

Key takeaways:

• These standards provide a practical and comprehensive framework for making quality measurable.
• They are essential for productivity, security, scalability, compliance, and stakeholder confidence.
• Implementation is achievable in manageable steps—start with assessment, customize measures, nurture a quality-focused culture, and iterate.

Recommendations:

• Begin by exploring these standards on iTeh Standards for full details and implementation guides.
• Consider which areas of your business stand to benefit most from improved data quality and process maturity.
• Engage leadership, technical teams, and business units in a unified quality improvement journey.

Stay ahead in the era of digital transformation by making quality measurable—with international standards as your foundation.