Prioritize the use of top-notch data to fuel your artificial intelligence systems.
In the realm of AI development, the significance of data quality is often overlooked, yet it plays a pivotal role in the success of projects. Companies like Itexus have recognised this and focused on data quality in their early AI projects, delivering scalable, secure AI-driven solutions in fintech with demonstrable results such as smarter banking systems and fraud detection. These achievements are supported by ISO 27001 certification, ensuring data integrity and compliance.
However, broader studies show that 85-95% of AI projects fail, often due to poor data quality and governance. This underscores the critical importance of robust data management to avoid bias, security risks, and poor decision-making. Successful AI projects typically have clear use cases with high-quality data workflows, resulting in measurable ROI, particularly in areas like sales and project management.
The quality of data is a significant issue for organisations, often overlooked by companies and their top executives. To address this, companies should focus on creating the right data correctly with appropriate identifiers and tags from the beginning. Involving as many people as possible, at all levels, in data quality efforts is important. Commercial departments, not the IT department, risk director, or data directors, should be responsible for data quality.
When selecting the right data for a project, consider factors like relevance, comprehensiveness, integrity, correct representation, absence of biases, timeliness, clear definition, and appropriate exclusions. Companies should be wary when providers refuse to answer about data, citing their model as proprietary.
Clear responsibilities for teams and individuals should be established. The highest-level person directing the initiative should be responsible for ensuring data quality and assembling teams to develop requirements and delve into details. Managers should question model development teams and others about the problem being solved, appropriateness of training data, and potential biases in data.
In the medium term, companies should promote quality efforts in the early phases of a project by focusing on the customer and process improvement, clear management responsibilities, and involving as many people as possible. This proactive approach can help organisations find themselves in a challenging situation where they must protect themselves in the short term, develop necessary capabilities in the long term, and prepare to do so effectively.
Even in the world of football, the importance of data quality is evident. Paulo Autuori, the manager of Sporting Cristal, made a self-criticism for their recent draw and praised Cristian Benavente, stating that he has quality and thinks a lot about the game. This demonstrates that, whether in AI development or sports management, the focus on quality can lead to improved performance and success.
The potential for AI to cause damage to people, businesses, and societies is significant due to its tendency to make mistakes, hallucinate, deviate, and collapse. By prioritising data quality, we can mitigate these risks and ensure that AI serves as a tool for progress, rather than a source of harm.
Read also:
- Musk threatens Apple with litigation amidst increasing conflict surrounding Altman's OpenAI endeavor
- E-mobility continues its progress after a decade since the scandal, staying on course
- The Commission deems the assistance program to be in agreement with the domestic market regulations.
- Innovative Garments and Accessories Producing Energy: Exploring Unconventional Sources for Renewable Power
