When we think of the potential of artificial intelligence (AI) within companies, several use cases are essential.
According to the recent IPSOS study on the use of AI by the French, 48 % of respondents use generative AI to do research, 38 % to write texts or documents, and 31 % to synthesize, summarize a subject or data. These elements, and many more, illustrate the impact of AI on modern companies. It is not only a technological advance, but a strategic asset. However, followers of these tools are also wary, because 43 % fear the use of false and unreliable data. So, in order to embark on any initiative, it is necessary to ensure in the first place to have data up to the task.
Why is data integrity important for the success of AI?
Data intended for AI must meet strict integrity requirements, encompassing precision, consistency and contextualization. Without this reliability, any advance in the field remains illusory: corrupt or imprecise data directly compromises the relevance and efficiency of AI systems.
The absence of robust data even exposes the most sophisticated models to biases, a lack of reliability and a contextual inadequacy, thus weakening the confidence granted to these technologies. Each use case includes risks that it is imperative to anticipate. Ultimately, investments will only be profitable if AI systems are based on reliable data.
However, many organizations come up against major data integrity challenges, in particular:
- Rapid and effective integration of data;
- The implementation of governance for responsible use of information;
- Continuous monitoring and improvement in data quality;
- The enrichment of datasets by third party sources and spatial information for in -depth contextualization;
- The guarantee of a high level of security and confidentiality.
Three considerations on data integrity to meet AI challenges
An organization can be faced with harmful biases, unreliable results and a lack of contextual relevance. In this context, the fair and responsible development of AI is based on strategic data integration, rigorous management of their quality, suitable governance practices, in -depth spatial analysis and data enrichment.
In addition, obtaining reliable results in AI requires an approach based on three essential pillars: completeness, reliability and contextualization of data. Each of these aspects must be discussed in a methodical manner in order to guarantee the robustness of AI models.
Indeed, the optimal exploitation of new technologies requires exhaustive dataset. For this, it is essential to eliminate information silos and integrate critical data sources from various relevant environments. This approach guarantees full access to datasets, minimizing biases and improving the accuracy of AI models.
The reliability of the data is another fundamental issue. The implementation of rigorous data control mechanisms is essential to guarantee their accuracy, consistency and normalization. A solid governance framework must also be established in order to maintain this quality in the long term. The use of confidence data for training and adjusting machine learning and generative AI models is essential for reliable predictions and decisions.
Also, adding a context relevant to data makes it possible to obtain more precise and nuanced analyzes. The enrichment of datasets by third -party sources and spatial information strengthens their depth and relevance, thus guaranteeing responses adapted to the specific needs of the different AI applications.
Faced with these challenges related to data integrity, the complexity of the choices to be made may seem complex. However, the adoption of a structured and progressive approach makes it possible to better understand these issues and to ensure the development of a truly effective and responsible AI.
Move towards an artificial trusted intelligence
AI failures have already caused a lot of ink, commercial chatbots recommending competing products to the memories generated by the AI truffled with false quotes. These incidents tangibly illustrate the consequences of inadequate management of the performance and reliability of models, regardless of the field of application.
So how can we avoid becoming an additional example of AI having failed? As these technologies progress, their success is based on a firm commitment to data integrity from the first stages of their development. Making an absolute priority makes it possible to avoid the domino effect of corrupt data and to guarantee artificial intelligence solutions that are both robust and trustworthy.
Consequently, the future of AI is intrinsically based on data integrity, an essential pillar to guarantee reliable, precise and exempt models. Ensuring the quality, consistency and contextualization of the information used not only improves the performance of artificial intelligence solutions, but also to establish lasting confidence in these technologies.
