On Demand
Data “democratization” is here. Data is in demand today from an ever-widening community of users who seek data-driven insights to inform critical decisions and actions. To move business intelligence and analytics beyond just the specialists, applications must be “beautiful” and the user experience must be rich, visual, interactive, and actionable.
David Stodder
Sponsored by
Actuate - now OpenText
Increasing interest in using Hadoop for data management, transformation, and analysis has led to significant development efforts by commercial vendors to enhance and extend the open source Apache Hadoop framework and offer a range of different Hadoop solutions. Many of these solutions can be used to enhance and extend the current BI and data warehousing environment.
Colin White
Sponsored by
Teradata
For years, experienced data warehousing (DW) consultants and analysts have advocated the need for a well-thought-out architecture for designing and implementing large-scale DW environments. The benefits from these architectures are well documented, but enterprises are faced with new and disruptive demands from their business users. The question becomes: How do we maintain a stable analytical environment, yet bring in the technological innovations so desperately needed?
Claudia Imhoff, Ph.D.
Sponsored by
SAP
The incremental movement toward real-time operation is the most influential trend today in data-driven IT disciplines such as business intelligence (BI), data warehousing (DW), and data integration (DI). From a technology viewpoint, collecting, processing, and delivering data is hard enough; doing it in real time requires effort that is downright Herculean. Thanks to the big data phenomenon, the volume of data continues to swell, exacerbating the situation.
Philip Russom, Ph.D.
Sponsored by
Teradata
Reports and dashboards that utilize historical data to gain insight are just the beginning of a company’s analytics journey. Advances in technology including predictive capabilities can help organizations gain competitive advantage by helping them discover trends, patterns, and relationships in data and guide their next course of action. In the past, predictive analytics has been the realm of statisticians and other quantitative individuals and was often separated from BI activities.
Fern Halper, Ph.D.
Sponsored by
TDWI and IBM Content
Big data analytics, mobile devices, cloud-based solutions, self-service BI, and predictive analytics—these are the major trends impacting today’s decision-making environments. Exciting, yes, but enabling these trends can also be quite disruptive to traditional data management processes and the implementers, analysts, and decision makers themselves.
Claudia Imhoff, Ph.D.
Sponsored by
Liaison Technologies
Speed, agility, and intelligence are competitive advantages that nearly all organizations seek. To seize these advantages, organizations require timely, diverse, complete, and accurate data. Unfortunately, traditional data warehouse extraction, transformation, and loading (ETL) processes are not fast enough. They put too much burden on ETL developers to understand every nuance of every data source, and it’s getting worse as Hadoop and other big data sources become part of the mix. How can organizations take advantage of new big data sources to deliver complete and diverse views of data—and get beyond the limits of traditional data warehouses?
David Stodder
Sponsored by
SAP