Data and Analytics Trends for 2019 and Beyond: A Panel Discussion

Gartner

Discussion Topics:
- How effective are your current data and analytics initiatives
- What trends will most impact how you utilize data and analytics in 2019 and beyond
- What must you do to maximize data and analytics in your organization
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Spotlight

Hadoop is great for data storage and processing, however the same architectural features that make it great for running complex transformations on massive amounts of unstructured data make it less adequate for interactive business analysis. Hadoop’s challenges when serving large numbers of users at business speed and its lack of a business user interface necessitate a new kind of data architecture. Forward thinking companies that are adopting Hadoop in their next generation architecture need to adopt a Business Intelligence architecture that separates data storage and processing workloads from end-user analytical workloads.

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Top 10 Data and Analytics Trends That Will Change Your Business

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• The top ten strategic data and analytics technology trends and what they enable. How these trends enable you to build an intelligent and emergent data and analytics portfolio of capabilities that scale to the needs of digital business. Why these trends are growing and having an impact now. How these trends will change your organization, data and analytics program and skills needed. Strategic technology trends have significant disruptive potential over the next 5 years. You must examine your business impacts of these trends and appropriately adjust investments, business models and operations or else your company is at risk of losing competitive advantage to those who do. Data and analytics leaders cannot afford to ignore these 2019 top data and analytics trends.
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This session will discuss how the master data management platforms are evolving to meet needs of digital economy. A modern master data management platform incorporates graph technology, infuses insights from the data using advanced analytics and ML, and offer big data scale performance in the cloud. Join this webinar to learn about these and other critical capabilities that power connected customer experience, compliance, and business alignment.
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Join data management guru Dave Wells for this educational webinar on the data catalogs role, and how to get the best fit for your enterprise.Managing data in the age of big data and self-service analytics is a complex job. Managing data without a data catalog is difficult and fraught with risk. Data cataloging is the new gold standard for metadata and an essential component of modern data management. Data catalogs support data curation and data stewardship with intelligent and automated tagging, profiling, and linking to business glossary. They help business analysts and data scientists to quickly find data and evaluate the fit for their specific use cases. Strategically, cataloging is at the core of data asset management with direct impact for data governance, regulatory compliance, and analytic quality and efficiency.
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Modernize Your Data Architecture to Deliver Oracle as a Service

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The migration to cloud-based data architectures continues at a rapid pace, including databases and data management. Oracle databases are part of this trend, and during this webinar you will learn how to automate the provisioning and management of Oracle databases so that you can deliver an “as-a-service” experience with 1-click simplicity. Experts will walk you through the process of
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Spotlight

Hadoop is great for data storage and processing, however the same architectural features that make it great for running complex transformations on massive amounts of unstructured data make it less adequate for interactive business analysis. Hadoop’s challenges when serving large numbers of users at business speed and its lack of a business user interface necessitate a new kind of data architecture. Forward thinking companies that are adopting Hadoop in their next generation architecture need to adopt a Business Intelligence architecture that separates data storage and processing workloads from end-user analytical workloads.

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