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RESEARCH & RESOURCES

Available On-Demand - This webinar has been recorded and is now available for download.

New Data and New Use Cases Powered by the Modern Data Warehouse

Webinar Speaker: Philip Russom, Senior Research Director for Data Management

Date: Thursday, October 10, 2019

Time: 9:00 a.m. PT, 12:00 p.m. ET

Case studies in advanced analytics based on multi-structured, time series, streaming, and IoT data.

According to TDWI surveys, the leading driver for modernizing a data warehouse is enabling business-focused, data-driven use cases that are new to the organization. For example, many data warehouses were built to satisfy requirements for reporting and online analytic processing (OLAP) and so must be augmented, redesigned, replatformed, or otherwise modernized to provision data for advanced forms of business analytics, enabled by data mining, artificial intelligence, or machine learning. Likewise, some warehouses were designed for relational data only and so should be augmented by nonrelational platforms such as Hadoop to manage multistructured data in support of new business use cases for customer sentiment and the analysis of text-laden processes such as the claims process in insurance. Similarly, most data warehouse designs assume high latency and must be modernized to handle the frequent data ingestion and low-latency responses of real-time analytics for fast-paced business operations, fraud prevention, and sensor-intense environments such as logistics and the Internet of Things (IoT).

The new use cases enabled by a modernized data warehouse can transform a business that needs to become more digital, competitive, agile, analytically driven, and modern in every way. In addition, the value of these business-focused use cases proves that there is indeed an organizational need for data warehouse modernization and a return on the investment.

Attend this webinar to learn about:

  • New business-driven use cases that are uniquely supported by a modern data warehouse
  • Established use cases that are still relevant, but taken to new levels of functionality and business value by modernization
  • How a modern data warehouse brings both operations and analytics closer to real time
  • How new data sources, structures, and latencies are forcing an evolution of enterprise data that modern organizations can leverage for business advantages
  • The kinds of new analytics that most warehouse modernizations target, plus the business benefit
  • Innovative modernizations that tap natural language processing as a foundation for new analytics
  • Many real-world use cases and customer stories illustrating the above points

Philip Russom, Ph.D.

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