Revolutionizing data governance and literacy, and increasing governance team efficiency by 40% with Datagalaxy.
At a Glance
As one of France’s top mutual insurance and social protection providers, Malakoff Humanis needed a data catalog that could do more than just document assets. They faced a stagnant data governance strategy, slow user adoption, and failure to connect teams around a shared understanding of data. To scale their data governance strategy and drive business value, they turned to DataGalaxy.
With DataGalaxy, Malakoff Humanis rapidly built a more collaborative, business-friendly approach to data: Teams across IT, data, and business units gained a common language and a single point of access to trusted information. This shift not only improved efficiency and internal alignment but also enabled the organization to meet regulatory demands with confidence, reducing risk and accelerating data-driven decisions at scale.
“Using DataGalaxy has brought us many concrete results. Today, we have more than 1,000 pieces of data referenced, available, and accessible. We have around fifteen applications that we have managed to reference and link to the DataGalaxy tool.”
1000+
data assets cataloged
15
integrated applications
300
active internal users
Drove trust and compliance with end-to-end data visibility
A key priority for Malakoff Humanis was strengthening its regulatory compliance and risk management. DataGalaxy’s data lineage tools provided the team complete visibility into how data flowed through systems and processes, enabling faster, more accurate impact analyses. This transparency ensured audit readiness and deepened organizational trust in the data that informed both operational and strategic decisions.
Today, Malakoff Humanis transforms data governance into a powerful engine that helps to cut costs through reduced audit prep, remove tool redundancy and cloud migration errors, and increase user experience. Behind the scenes, DataGalaxy has also helped Malakoff Humanis enhance its data quality and trust, prepare its data teams for working with bespoke AI tools, cut response time to operational issues, and avoid redundant report or algorithm development. The result? Fewer regulatory fines, shorter time-to-value for data-driven projects, and improved operational efficiency.
