On Demand
Hub-and-spoke patterns have long been the preferred architecture for data and application integration technologies. That’s because hubs provide an easily understood design, reduce the number of interfaces required, foster reuse, and control data centrally for the purposes of optimization, data standards, and governance. However, there’s a new generation of data integration hub coming that even nontechnical users can employ to publish data, so that other users can easily subscribe to that data and bring it into their applications with minimal involvement from IT.
Philip Russom, Ph.D.
Sponsored by
Informatica Corporation
Can business analysts effectively use predictive analytics? Adoption of predictive analytics and other advanced analytics has increased for a number of reasons, including a better understanding of the value of the technology and the availability of computing power. Economic factors are also a driving force in utilizing predictive analytics for business as companies strive to remain competitive. Companies want to better understand customer behavior. They want to better predict failures in their infrastructure. The uses for predictive analytics are extensive and growing.
Fern Halper, Ph.D.
Sponsored by
Alteryx
Most business intelligence (BI) implementations query data that is historical, representing business processes that have already completed. Even so called real-time BI reports are generated using historical data. While this view of data has some value, it does not represent the complete picture. Fully informed business decisions require real-time insight into high-volume streaming data sources, current events, and ongoing business processes.
Philip Russom, Ph.D.
Sponsored by
Splunk
Business intelligence (BI) applications are playing an ever-increasing and important role in driving and optimizing daily business operations. This trend is leading to major changes in both the functionality and usability of BI-related technologies and products. Developing an operational BI strategy in this dynamic and constantly changing environment is not a simple task.
Claudia Imhoff, Ph.D.
Sponsored by
SAP
Big data has arrived in healthcare, whether organizations are ready for it or not. Increasingly, healthcare provider and payer organizations have access to growing volumes of both structured and unstructured data. New sources, such as electronic health and medical records, doctors’ notations, claims data, clinical data, and external Internet/social media data, are mushrooming in size and importance for access and analysis. Big data holds the potential to give users a more complete and timely view. At all levels, users in healthcare organizations must leverage this data to discover insights for improving patient care, cost management, and operational process improvement.
David Stodder
Sponsored by
Hewlett Packard
Just a few years ago, big data was a problem in terms of scaling up IT systems and discovering the business value. Thanks to advances in vendor platforms and user practices, most enterprises today consider big data an opportunity—not a problem—because they can mine and analyze it for valuable business insight.
Philip Russom, Ph.D.
Sponsored by
Teradata
To compete effectively and keep the best customers loyal, organizations need to capture a variety of data about customers and analyze it effectively. This includes tapping new and promising big data sources such as social media data. However, if organizations cannot analyze data in time and deliver insights to business decision makers quickly, all the data in the world won’t make a difference.
David Stodder
Sponsored by
Actuate - now OpenText