Location Analytics for Your Data Lake: Driving New Business Insights and Outcomes
TDWI Speaker: Philip Russom, Senior Research Director for Data Management
Guest Speakers:
Daniel Kernaghan, Big Data Solutions Evangelist, Pitney Bowes
Debashis Rana, Chief Solution Architect, RCC Global Services
Date: Tuesday, December 5, 2017
Time: 5:00 a.m. PT, 8:00 a.m. ET
Webinar Abstract
Location information has been a growth area in recent years in data management, as user organizations of many sizes and industries have realized how location information can inspire new business insights, practices, and outcomes. In response, many users have reworked older enterprise data environments to enrich the data with more location information. At the same time they have begun capturing data from new sources that include location information, especially from sensors, machines, devices, vehicles, and the Internet of Things (IoT). Much of this new data is being managed in data lakes, which in turn are usually deployed atop Hadoop.
With all those data sets, data sources, and data platforms coming together, TDWI sees new opportunities for analytics and business operations that specifically tap the growing availability of location information, as well as tools for improving such data. For example, TDWI sees location information as an enabler for a broad range of logistics analytics that shorten shipping times, increase delivery accuracy, and make package tracking possible. In marketing, location data contributes to complete customer views, demographic granularity, and the siting of retail stores. Out on the leading edge, we are starting to see location information applied by “smart cities,” which adjust traffic lights on the fly, to speed traffic or clear a path for emergency vehicles.
In this webinar, you will learn about:
- Approaches to improving the quality, completeness, and standardization of location information in existing data environments, especially those for marketing, logistics, and analytics
- Data information issues in new environments for big data, Hadoop, and data lakes
- New data sources that inherently include location information, as with data from sensors, machines, devices, vehicles, and the Internet of Things (IoT)
- Real-world use cases for analytics and operational applications that depend on location information
- New or improved business practices that tap location information for greater speed, accuracy, tracking, analytic insights, and business outcomes
Philip Russom, Ph.D.