Podcast Semarchy & Micropole: AI at the service of data quality

AI as a lever for data quality

Data Quality is essential for modern businesses, as it not only enables internal processes to be optimized, but also ensures that strategic decisions can be made quickly and reliably. "Data quality is crucial to making processes more reliable and improving business efficiency", Pascal Anthoine points out on the Rely on Your Data Podcast. stresses Pascal Anthoine on the Podcast Rely on your Data. However, with the explosion in data volumes, maintaining this quality on a large scale is becoming a major challenge. Artificial Intelligence, and Machine Learning in particular, can help automate and optimize data management, by processing large volumes faster and reducing human error.

Furthermore, experts point to the central role played by AI in sectors such as Retail, where product data, often multilingual and raw, needs to be enriched before publication on e-commerce platforms. It facilitates the translation, classification and standardization of data, guaranteeing its consistency and reliability, thus saving time and ensuring its quality across different formats and languages.

Business process automation

Data is not only used to make strategic decisions, it is at the heart of organizations' day-to-day operational processes. AI makes it possible to automate the processing of large quantities of data, thereby reducing the risk of human error. In this podcast, Julien Peltier gives the example of tools such as Google Translate, which facilitate the management of multilingual data from various sources in the same way as other integration systems. By automating these processes, AI enables teams to concentrate on higher value-added tasks, while guaranteeing the quality of the data used.

As Pascal Anthoine explains, "AI is just a process that consumes data; without data quality, there is no AI". In other words, structured, reliable data are essential to fully exploit AI.

AI for managing data quality rules

AI also facilitates the definition and application of data quality rules. Semarchy's MDM platform uses AI to enable companies to define flexible rules tailored to their specific needs. A virtual assistant guides users through the creation of these rules, while AI steps in to automate tasks such as cleansing, deduplication or data transformation, optimizing data management while reducing errors and costs.

AI's non-deterministic approach allows systems to adapt to changes in data, offering greater flexibility. " With AI, we'll be able to react to content, so the business will be able to say no, this is poor quality data for this reason or that reason" Pascal Anthoine explains to Rely on your Data.

Unlike pre-established rules, AI learns iteratively, adjusting to business feedback and variations in the data. "The AI engine will learn the quality rules iteratively, based on business feedback on the data. We're going to help the business react to the data and itself define what quality is in an iterative way, and enter into a data quality process without necessarily having to go through a very detailed design phase ," he adds. 

The evolution of data management professions with AI

In this podcast, Pascal Anthoine and Julien Peltier highlight the profound transformation that data management professions are undergoing with the rise of Artificial Intelligence. 

Indeed, while the automation of repetitive tasks such as data validation and enrichment reduces the workload on teams, they can concentrate on strategic missions such as supervising algorithms, controlling data quality and adapting systems to specific business needs. This evolution improves data accuracy and enhances process efficiency, while maintaining a crucial human role in system optimization.

Enrich your data with AI

Another major advantage of AI highlighted during this podcast is its ability to enrich data by combining internal and external sources. Ultimately, this could lead to the creation of an AI engine capable of instantly answering business questions related to data management, offering precise and relevant answers. Such enriched intelligence would enable companies to make more informed decisions, by accessing quality information from a variety of sources.

AI, accelerating decision-making

Finally, AI accelerates strategic decision-making by rapidly synthesizing large quantities of data. By reducing the time needed to obtain relevant information, it enables companies to react more quickly in a competitive environment.

Pascal Anthoine and Julien Peltier agree, however, that AI does not replace humans, but only assists them in decision-making by making processes more efficient and enabling employees to focus on special cases, which cannot be handled by AI. 

Conclusion: AI, an indispensable lever for data management

In short,Artificial Intelligence offers immense potential for transforming data management within companies. It can automate complex processes, guarantee data quality and accelerate decision-making.

However, to reap the full benefits, it is essential to define clear frameworks for use, particularly when it comes to managing sensitive data. The future of data management lies in the optimal use of AI, for more accessible, enriched and relevant data, at the service of innovation and corporate strategy.

Listen to the podcast to hear all the fascinating exchanges.