On Demand
Organizations today need to manage, analyze, and visualize diverse data sources such as machines, sensors, social media, Web applications, and so on. Fully leveraging granular data from these sources drives insights into customer behavior, business operations, information security, risk management, and competition, which in turn increase growth, efficiency, and competitive success.
Philip Russom, Ph.D.
Sponsored by
Splunk
The real value of a decision-making environment is not in the creation of reports or simple multi-dimensional analytics. It is in the creation and use of the more sophisticated analytics like statistics and data mining. And the way to support these critical capabilities is by extending the traditional data warehouse environment to include data sets in more fluid, less controlled components in addition to the enterprise data warehouse.
Claudia Imhoff, Ph.D.
Sponsored by
Dell EMC
Data and analytics are becoming more important than ever to businesses. However, the popularity of analytics is a double-edged sword when it comes to communicating results. Analytics professionals often fail to present results in a strong context and in a memorable, repeatable form. Some analysts have already turned to storytelling. Although results are often good, it also becomes clear that storytelling with data is both an art and a science. Its mastery improves with guidance.
Fern Halper, Ph.D.
Sponsored by
ClearStory Data, Cloudera
Big data and advanced analytics extend the traditional BI environment with new analytics solutions that enhance business decision making and operational efficiency. Examples of solutions include support for new analytics-driven operational applications, analytics accelerators, data transformation hubs, and investigative computing platforms.
Colin White
Sponsored by
SAP
Today’s CFOs and their IT counterparts are increasingly expected to “do more with less,” providing value-added analytics while maintaining or reducing Finance and IT spend. Analytic improvement is often complicated by the complexity of fragmented financial systems. In response, leading finance departments are increasingly taking a data-driven approach using a common data warehouse and data governance processes to radically simplify their finance architectures.
Fern Halper, Ph.D.
Sponsored by
Teradata
Traditional systems development projects are process driven, and the requirements-gathering activities proceed accordingly. Business intelligence initiatives are data driven, and this requires a different approach to understanding the needs. This Webinar examines three major differences and techniques for getting requirements.
Jonathan Geiger
Sponsored by
Tableau Software
Adoption of predictive analytics has been increasing thanks to a better understanding of the value of the technology, vendors making the technology easier to use, and the availability of computing power. The uses for predictive analytics are extensive and growing. Companies want to understand customer behavior, they want to better predict failures in their equipment, and they want to deploy analytics in order to take action.
Fern Halper, Ph.D.
Sponsored by
TDWI and IBM Content