Ensure more reliable data and more efficient processes with the power of DAISY, the Data Artificial Intelligence System built into the platform
All the capabilities for data sharing and a 360-degree view of information assets, from roles to processes
Technologies and methods (starting with >300 soluzioni) to measure, monitor, and ensure AI-ready data
Advanced tools for finding and linking metadata, with native integration for AI and ML-based logic
You will no longer lose data by moving from one system to another: reconciliation is crucial for accurate business
Scalable models and interactive graphs to visualize the relationships among your data and enhance the value of your data assets
If your company has ever wondered, “Why don’t the numbers in the CRM match those in the ERP?” or “Why don’t the e-commerce sales align with the payment gateway data?”, you’re not alone. It often happens, for example, that treasury balances show discrepancies compared to bank statements — or that inventory levels differ between the […]
In 2025, Irion was mentioned in several Gartner research reports touching on themes like Augmented Data Quality, Data Integration, Data Governance, and the latest trends in Enterprise Data Management. These reports are aimed at key figures such as CDOs, CIOs, CFOs, and more generally at corporate Data & Analytics teams. In this article, we have
A modern company’s data is a fragmented universe: it resides on cloud or on-premise management applications, data lakes, dozens of legacy systems and spreadsheets that are often beyond IT’s control. Against this backdrop, how can you gain reliable insight and accelerate migrations and digital transformation projects? And how to train effective Generative Artificial Intelligence models?
Data Integration in 2025: from ETL to modular orchestration with active metadata Read More »
Is your latest Generative AI initiative not delivering the expected results? Do business reports take days of manual reconciliation? The issue probably isn’t with algorithms or people, but with the foundation: the quality of your data. We don’t build houses on cracked foundations—so why build our business on unreliable data? According to Gartner, as early
Back in 2023, we published a post exploring a concept that’s been gaining traction among data management professionals: the Data Product. Building on ideas from the literature (most notably Zhamak Dehghani’s seminal work on Data Mesh) we suggested a hands-on approach for positioning Data Products within modern data architectures. In today’s data landscape, we’ve moved
Understanding Data Products: benefits, evaluation frameworks, and how to monetize them Read More »
Augmented Data Quality means applying advanced functions to automate some Data Quality (DQ) processes with the help of “active metadata” and such technologies as Artificial Intelligence and Machine Learning. Today, data specialists face increasingly complex challenges: growing data volumes, increasingly heterogeneous data sources, and the need for an “AI-ready” information ecosystem. These needs have guided
What is Augmented Data Quality and how does it work? Read More »
In recent years, banks have benefited from a certain “tolerance” by regulators regarding the quality of risk data and risk reporting. But that period is over. The ECB is clear: regulations on this matter have existed for some time; now is the moment to truly apply them. Imagine a financial institution under stress: markets are
In the fascinating world of Data Management, curious stories often circulate that highlight the importance of data quality. Sometimes they even make us smile—until it happens to us or one of our clients, perhaps in the dreaded production environment! As we enjoy the summer, why not have some fun with quirky anecdotes that remind us
Anyone who deals with data professionally or is interested in the subject will have heard of, or perhaps discussed, Data Mesh. This subject has recently been the focus of many writings and discussions. Not that there has been little new in the world of Data Management in recent years. We may define these new concepts
Augmented Data Management uses advanced technologies to optimize and improve data management processes in a company. Data Management (DM) is an essential enabling factor for analytics, Data Science, Regulatory Reporting and digital business transformation. This practice allows collecting and storing, transforming and using the data in compliance with policies and regulations. It helps to quickly
