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Predictive AI in Customer Experience: What Works Today

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Connect with Derek Martin: Main image: carles Tags customer experience predictive analytics customer engagement personalization customer retention customer support customer service ai Featured Research White Paper How Agentic AI Is Rewriting the Rules of Customer Experience Your chatbot may be polite, but is it actually pulling its weight?

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Self-Service BI vs Traditional BI: What’s Next?

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This led to the birth of separate systems for reporting: the enterprise data warehouse. For the first time, the focus of a system became business questions, where data was denormalized. In this approach — in contrast to business intelligence — users request analytical queries only for new data topics. Parting Words.

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Mainframe Data: Empowering Democratized Cloud Analytics

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The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale. BI platforms and data warehouses have been replaced by modern data lakes and cloud analytics solutions.

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Self-Service BI: A Case of Trust Working Both Ways?

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This technological shift placed computing power into the hands of the individual consumer — yet access to corporate data still resided with the “techies”. The Rise of the Data Warehouse. The birth of the enterprise data warehouse was heralded as the solution to limited access. You need to show them the “why to.”

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Data Mesh vs. Data Fabric: A Love Story

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Data mesh forgoes technology edicts and instead argues for “decentralized data ownership” and the need to treat “data as a product”. Gartner on Data Fabric. Moreover, data catalogs play a central role in both data fabric and data mesh. New to data catalogs?

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What Is a Data Fabric and How Does a Data Catalog Support It?

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Indeed, the foundation of your data architecture and strategy – and thus your business strategy – begins with choosing the best data catalog to support your business. But how do you go about selecting the right data catalog? These two resources can help you get started: White paper: How to Evaluate a Data Catalog.

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