Building a trusted foundation for AI
Electronic connecter manufacturer embeds Qlik’s data visualization tool to set themselves up for AI success
LA SITUATION
Siloed data tools and programs regularly lead to data quality issues, inhibiting data-led action and a cohesive data culture within the Group.
Radiall is a world leader in electronic connectors for industries such as space, defense, telecoms and aerospace.
L'ORGANISATION
LE DÉFI
Exponential growth meant the company had outgrown its current data processes, leading to adaptation and scalability issues as analytics operations took on a life of their own.
LE BESOIN
Tracking and sourcing trustworthy data from various sources for effective analysis, while setting up the right data systems to reap the full benefits of any AI tools integrated with company systems.
“Nearly every time someone comes to me with a data question, I turn to Qlik. The next step is getting everyone else to take thi s approach. This is how we'll scale the data side of our business.”
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LA SOLUTION
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Radiall used Qlik Sense’s associative engine – including its embedded extract, transform and load (ETL) capabilities – to build operational and discovery dashboards that were simple and interactive for business users.
L'IMPACT
Staff are experiencing time savings of two days per month, without the burden of producing and navigating data dashboards manually. Reports that previously took almost a month are now completed in an working week, while improved data quality has accelerated business response times.
"L'amélioration de la qualité des données obtenue grâce à Qlik a permis d'améliorer les temps de réponse de l'entreprise et de créer une confiance incroyable dans les données utilisées. Cette confiance nous permet d'explorer en toute confiance de nouveaux cas d'utilisation des données en temps réel avec l'analyse prédictive et l'IA, ce qui nous aidera à trouver encore plus de valeur dans nos données."
LA DIFFÉRENCE
The intuitive design of Qlik Sense enables users to build their own dashboards, scale them up and down as required, and adapt them to suit their own data journeys and use cases – without creating the siloes associated with traditional EPM solutions.
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