These Trends Will Shape BI in 2019

Join us for this on-demand with Looker where we explore the top Business Intelligence trends for 2019. In this 50-minute webinar, you will learn about the issues affecting BI, the role of analytics as a critical success factor for organizational success, and how companies are leveraging BI and analytics for high-value insights.
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Spotlight

OTHER ON-DEMAND WEBINARS

Trends in Enterprise Advanced Analytics

McG

If you missed out on all the trends for 2019 published in December, or even if you caught some of them, this one merits your time. We’ll be going into 2019 and beyond, since the winners will have an eye on the long view for the source of competitive advantage that is analytics. It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
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Sharing and Deploying Data Science with KNIME Server - February 2019

KNIME

You’re currently using the open source KNIME Analytics Platform, but looking for more functionality - especially for working across teams and business units? KNIME Server is the enterprise software for team based collaboration, automation, management, and deployment of data science workflows, data, and guided analytics. Non experts are given access to data science via KNIME Server WebPortal or can use REST APIs to integrate workflows as analytic services to applications, IoT, and systems.
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Cloud Data Warehouse Modernization

tdwi.org

The economic model and elegance of cloud environments are motivating companies to assess their existing on-premises data warehouses and modernize their enterprise information environments. However, confusion about what is meant by “modernization” has led some to believe that “lifting and shifting” their on-premises implementation to a cloud environment is the default approach. In these cases, the results are mixed—while the system has technically been moved to a modern cloud platform, by no means is it modernized.
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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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