Optimise the quality of your customer data with AI at every stage of the data lifecycle with DQE.
Artificial intelligence is profoundly transforming how organisations use and govern their data. At DQE, we integrate AI at the heart of our tools to provide enhanced, reliable and intelligent data quality that adapts to business contexts and use cases across industries.
With DQE’s built-in AI Agents and a dedicated Copilot, you get a true intelligent assistant right at the point data enters your systems, offering:
MyDaina is an AI engine that lets you build and generate custom deduplication rules based on your use cases, business constraints and operational goals. Through guided interaction with the user, MyDaina helps you formalise and create relevant configuration rules for deduplication processes.
Results : With MyDaina, you gain efficiency and control. Your deduplication rules are more relevant, more consistent, and perfectly aligned with your business context.
Our experts combine AI and their domain knowledge to audit your files and provide tailored recommendations for your customer data quality challenges.
Results : You get a clear, actionable view of data quality that helps identify issues, understand fixes, and define steps for sustained improvement.
AI projects—whether generative or predictive—rely on massive volumes of data to produce usable outcomes. If the data is incomplete, incorrect, or inconsistent, the results may be useless, biased or misleading—this is the well-known “garbage in, garbage out” problem.
In environments where AI ingests and processes data at unprecedented speed and scale, the trustworthiness of use cases depends directly on the quality of the data feeding them. For example, errors in customer contact data (duplicates, outdated or incorrect information) can distort customer understanding and automatic predictions.
Poorly qualified data can also create operational risks and undermine confidence, including: biased outputs, reduced adoption by business users and even security vulnerabilities
A proactive Data Quality approach—combining strong governance, data culture and suitable tools to collect, clean and maintain qualified data—improves AI model performance, strengthens business confidence and maximises impact.
of AI and data teams’ time is spent on cleaning and preparing data
models fail to hit performance thresholds in production due to poor or incomplete data
of AI projects fail mainly because of low-quality data
Discover how to transform raw data into a genuine strategic lever, boosting performance, informed decisions, and sustainable competitive advantage.
