15 posts found
AI Data Readiness: Preparing Data for Artificial Intelligence
Over the last few years we have seen spectacular advances in the use of artificial intelligence (AI) and, behind all these achievements, we will always find the same common ingredient: data. An illustrative example known to everyone is that of the language models used by OpenAI for its famous ChatGP…
PET technologies: how to use protected data in a privacy-sensitive way
As organisations seek to harness the potential of data to make decisions, innovate and improve their services, a fundamental challenge arises: how can data collection and use be balanced with respect for privacy? PET technologies attempt to address this challenge. In this post, we will explore what…
New Year's resolution: Apply the UNE data specifications in your organisation
As tradition dictates, the end of the year is a good time to reflect on our goals and objectives for the new phase that begins after the chimes. In data, the start of a new year also provides opportunities to chart an interoperable and digital future that will enable the development of a robust data…
Application of the UNE 0081:2023 Specification for data quality evaluation
The new UNE 0081 Data Quality Assessment specification, focused on data as a product (datasets or databases), complements the UNE 0079 Data Quality Management specification, which we analyse in this article, and focuses on data quality management processes. Both standards 0079 and 008…
UNE 0081 Specification - Data Quality Assessment Guide
Today, data quality plays a key role in today's world, where information is a valuable asset. Ensuring that data is accurate, complete and reliable has become essential to the success of organisations, and guarantees the success of informed decision making.
Data quality has a direct impact not only…
The future of privacy in a world dominated by open data
In the era dominated by artificial intelligence that we are just beginning, open data has rightfully become an increasingly valuable asset, not only as a support for transparency but also for the progress of innovation and technological development in general.
The opening of data has brought enormou…
FAIR principles: the secret of the data wizards.
Books are an inexhaustible source of knowledge and experiences lived by others before us, which we can reuse to move forward in our lives. Libraries, therefore, are places where readers looking for books, borrow them, and once they have used them and extracted from them what they need, return them.…
Common misunderstandings in data anonymisation
Data anonymisation is a complex process and often prone to misunderstandings. In the worst case, these misconceptions lead to data leakage, directly affecting the guarantees that should be offered to users regarding their privacy.
Anonymisation aims at rendering data anonymous, avoiding the re-ident…
Free tools to work on data quality issues
Ensuring data quality is an essential task for any open data initiative. Before publication, datasets need to be validated to check that they are free of errors, duplication, etc. In this way, their potential for re-use will grow.
Data quality is conditioned by many aspects. In this sense, the Aport…
Technical Standards to achieve Data Quality
Transforming data into knowledge has become one of the main objectives facing both public and private organizations today. But, in order to achieve this, it is necessary to start from the premise that the data processed is governed and of quality.
In this sense, the Spanish Association for Standardi…
