On Demand
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
Cloud BI has been positioned as the next evolution in business intelligence because of the advantages it provides in terms of flexibility and elasticity. However, there is still confusion in the market around moving to a cloud model, and cloud BI adoption has been slow, although interest seems to be increasing. For instance, in a recent TDWI survey, a majority of respondents were either already using the cloud or were considering it for BI and analytics.
Fern Halper, Ph.D.
Sponsored by
GoodData, MicroStrategy
As big data continues to grow bigger and become more diverse and more real-time, forward-looking organizations are looking to manage and analyze this data using advanced analytics in an environment that might include multiple approaches and technologies. For real-time streaming data this could include utilizing technologies that support in-memory processing, where data and mathematical computations are performed in RAM rather than on disk, enabling processing thousands of times faster than data access from disk.
Fern Halper, Ph.D.
Sponsored by
SAP