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Nach AI: Neue Impulse für Daten, Erkenntnisse und Maßnahmen Als führende Anbieter im Bereich Daten und Analysen gestalten wir den heutigen und zukünftigen Einsatz von AI maßgeblich mit. Doch manchmal bestehen Zweifel am richtigen Weg und die Diskussionen sind chaotisch. In diesem Ratgeber finden Sie: wichtige Informationen, wie die drei Themen und die dazugehörigen Trends […]

Nach AI: Neue Impulse für Daten, Erkenntnisse und Maßnahmen Als führende Anbieter im Bereich Daten und Analysen gestalten wir den heutigen und zukünftigen Einsatz von AI maßgeblich mit. Doch manchmal bestehen Zweifel am richtigen Weg und die Diskussionen sind chaotisch. In diesem Ratgeber finden Sie: wichtige Informationen, wie die drei Themen und die dazugehörigen Trends

5 Pitfalls That Undermine Your AI Readiness | Qlik eBook As a data professional, it’s easy to get caught up in the AI hype, especially with top-level management pressuring you to adopt AI wherever possible (and sometimes where it isn’t!). While you might be worried that you’ll get AI adoption wrong, you don’t have to

Make Machine Learning A Reality Across Your Business Machine learning is still, for many business leaders, shrouded in mystery and somewhat intimidating. Many leaders are resistant to new technologies, yet other organizations have moved quickly and found tremendous success. Successfully deploying automated machine learning requires a balanced approach, with strong leadership, clear goals, and the

The 6 Principles of AI Ready Data According to Gartner, by 2025, Gen AI will be a workforce partner for 90% of global companies, and by 2026, over 80% of enterprises will have deployed GenAI-enabled applications in production.* But there’s a catch: AI can’t succeed without good data. So what is “good” data in the

Automating Data Lake Pipelines for AI Data engineers are facing increasing demands to deliver accurate, model-ready data to AI initiatives — fast. But traditional data integration tools require time-consuming coding by highly skilled staff. And these tools often lack the capabilities to fully prepare, cleanse, tag, and catalog the data. What’s the answer? Automated data

7 Data Integration and Quality Use Cases Managing data to meet modern needs is daunting, and so is searching for the technology to help. Until now, options have been limited to legacy products, Big Cloud hyperscalers, and point products — all of which have limitations and challenges. There’s a better way. Qlik Talend offers a

6 Data Integration Reference Architectures for Business Acceleration Your data needs to be able to contribute to real growth, innovation, and a competitive edge to your business.  Yet, as more data floods into your environment from more sources than ever, people-intensive integration tools can get in the way. Download Six Data Integration Reference Architectures for

5 Reasons Qlik and Snowflake Are Better Together: Automating the Data Warehouse for Faster Time-to-Insight Automating the Data Warehouse for Faster Time-to-Insight Now more than ever, data is moving to the cloud, where data warehousing has been modernized and reinvented. The result is an explosion in adoption. And for Snowflake users, Qlik offers an end-to-end

Transform Data in Your Cloud Warehouse Today’s data teams are being asked to integrate massive stores of data as quickly as possible. But traditional processes for managing data warehouses are hand-coded and complex. And that’s led to management complexity, developer resource constraints, cost overruns, and increased risk. You don’t have to settle for outdated data