Simplifying Analytics with Natural Language

What if you could directly ask questions of your data? Ask Data, Tableau’s new natural language capability, allows people to get insights by simply conversing with their data. In this latest Data Science Central webinar, members of Tableau’s Ask Data team will demonstrate how they are lowering the barrier to analytics and leveraging Natural Language Processing (NLP) as a tool for visual analysis. Looking to quickly make smarter, data-informed decisions and empower others to do the same? Watch this detailed overview of Ask Data’s capabilities to learn more.
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The Semantic Layer’s Critical Role in Modern Data Architectures

Many of the most exciting innovations and advancements in data management today are occurring within the semantic layer of data architectures. For example, we’re witnessing new or improved approaches to semantic modeling, data cataloging, data lineage, and more. Even older forms of semantics—such as metadata and virtualization—are being infused with new techniques for augmentation and automation, including intelligent tool algorithms driven by machine learning and the use of graph analytics to generate data maps and automatically document data elements found via graph.
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Lynx: A FAIR-fuelled Reference Data Integration & Look up Service at Roche

Roche, as a leading biopharmaceutical company and member of the Pistoia Alliance, has a diverse and distributed ecosystem of platforms to manage reference data standards used at different parts of the organization. These diverse reference data standards include ontologies and vocabularies to capture specifics of the research environment and also to describe how clinical trial data are collected, tabulated, analyzed, and finally submitted to regulatory authorities. In the context of the EDIS program, Roche has bridged these parts to improve reverse translation from studies into research and also embraced FAIR to emphasize machine-actionability and data-driven processes.
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Data Observability / DataOps using AI

Modern-day systems are transforming into complex, open-source, cloud-native services running on various environments and being developed/deployed at lightning speed by distributed teams. When working on these systems, identifying a broken link in the chain can be near impossible. Everything fails at one point or another, whether due to code bugs, infrastructure overload, or changes in end-user behavior or market driven factors or errors in data collection. This has led to the rise of DataOps with a focus on changing the organizational speed and trust in delivering data pipelines and the related artifacts by co-creating “decision quality” data with the consumers. This development has led to the idea of observability that includes monitoring, tracking, and triaging incidents to prevent downtime of the systems and around several factors such as freshness, distribution, volume, schema, lineage.
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Speed-Of-Thought Analytics: Query Billions Of Rows In Milliseconds

In this 30 minute demo, you'll see a demo of how easy it is to connect to Yellowbrick data warehouse from MicroStrategy, query tables with billions of rows from a TPC-DS workload, and see results in milliseconds or seconds.
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