ISO 5259 establishes a framework for assessing and improving the quality of data throughout the different phases of its lifecycle, which is crucial for obtaining reliable results in data-driven approaches to AI systems such as Machine Learning (ML).
High-quality data is essential in artificial intelligence (AI) systems as it directly affects their performance, accuracy and reliability.
Why is ISO 5259 important?
ISO 5259 is a crucial standard for any organisation, as it addresses the fundamental need for high quality data in an era dominated by data-driven decision making and supported by AI systems.
As data becomes the raw material for analytics and machine learning, ensuring its quality has a direct impact on the accuracy and reliability of the results delivered by analytical models and ML systems.
This standard provides organisations with the necessary tools and methods to assess, manage and improve data quality, ensuring that the data used are fit for purpose.
It also provides a common language and set of practices that facilitate effective data quality management, which is crucial for consistent and reliable analytical results.
Key principles of ISO 5259:
- Accuracy of results – Quality data allows AI models to make more accurate predictions.
- Bias reduction – Poor quality data can introduce biases into models, leading to unfair or incorrect decisions.
- Better generalisation – With diverse and representative data, AI can better generalise to new scenarios.
- Efficiency in training – Quality data reduces the time and computational resources needed to train models, avoiding unnecessary processing.
- Avoid error propagation – With erroneous or inconsistent input data, models learn incorrect patterns.
- Better user experience – Well-trained AI systems with quality data deliver better interactions and more useful outcomes for users.
Benefits of certification
- Ensure Data Quality and Reliability Demonstrates your company's commitment to quality data management by ensuring that the information used in AI systems is accurate, consistent and reliable.
- Reduce Bias and Ensure Ethical Use of Data Facilitates the identification and mitigation of bias in data, promoting the development of fairer, more transparent AI models aligned with ethical principles.
- Compliance with Regulations and Standards. Supports compliance with regulatory frameworks related to privacy and data governance, helping to avoid legal risks and improving transparency.
- Drive Innovation and Sustainability Contribute to the development of responsible AI solutions, aligned with the Sustainable Development Goals (SDGs), promoting the ethical and sustainable use of technology.
Key requirements and processes
The assessment of the Data Quality of AI systems is performed taking into account the following Quality Characteristics as defined in ISO 5259:
- Accuracy: Data has attributes that correctly represent the true value of the desired attribute of a concept or event in a specified context.
- Completeness: The data associated with an entity has values for all attributes necessary for the representation of the entity.
- Consistency: Data is free of contradiction and consistent with other data in its given context of use.
- Credibility: Data has attributes that are considered true and credible to users.
- Currentness: Data has attributes with currently valid values for its given context of use.
Get certified with I2SC
At I2SC, we offer expert advice on the implementation and certification of ISO 5259. Our team will accompany you at every stage of the process to assess and improve the quality of the data that feeds and generates your AI systems.
Contact us today and start your journey towards ISO 5259 certification for your data repositories.
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