On Demand
Because BI/DW architectures have historically been engineered to maintain the performance of OLTP systems, implementation decisions of the past have forced analysts to sacrifice data accessibility and flexibility in return for reasonable reporting and analytics performance. The reliance on ERP and other operational systems to run the business negatively impacts the agility of the data analyst. Limited data accessibility and increased latency are effectively engineered into the analytics environment, creating complexities that preclude the rapid discovery of actionable insights.
David Loshin
Sponsored by
SAP
Much of the attention on big data has focused on the infrastructure—data warehouses, Hadoop, appliances—needed to support big data initiatives. However, because data is only useful if you can analyze and act on it, the question becomes: What does it mean to analyze data at scale?
Fern Halper, Ph.D.
Sponsored by
TDWI and IBM Content
Whether driven by cost, the need to deliver new capabilities, or a deluge of new and varied data of value to the business, organizations are re-thinking the data warehouse. As boundaries become more fluid to support emerging mobile, social, and cloud-based services, key challenges emerge: how to be flexible and agile in taking appropriate advantage of cloud-based solutions without compromising on security or service quality.
Colin White
Sponsored by
TDWI and IBM Content
Analytics is undergoing a renaissance. Its value is understood. It is becoming easier to use, even by the non-statistician. Organizations are gathering ever increasing volumes of disparate data to use for analytics. Forward-looking organizations are even using real-time data and beginning to embed analytics against this data.
Fern Halper, Ph.D.
Sponsored by
Actuate - now OpenText
Analytics is hot and getting hotter. As its value becomes better understood, many organizations are looking to expand their analytics efforts. To discover new opportunities, serve customers more effectively, reduce fraud, and improve operations, firms want to get to the next level with predictive and other forms of advanced analytics. They want to build a broader analytics culture that includes more types of users.
Fern Halper, Ph.D., David Stodder
Sponsored by
Cloudera, MicroStrategy, Tableau Software
Operational data stores (ODSs) are currently experiencing a dramatic evolution, as are many data platforms and practices within data warehousing and enterprise data management. The evolution of the ODS is driven mostly by users’ increased usage of big data and advanced analytics, but also by changing practices in data archiving, data staging, and data integration. The result is that ODSs today manage greater data volumes, handle more diverse data, and serve more practical uses than ever before
Philip Russom, Ph.D.
Sponsored by
Cloudera, Talend
Visual representation can be astonishingly beautiful. But for users, the real beauty comes when good data visualization shortens their time to insight and helps them become more productive. Executives, managers, and frontline users can be held back if they are limited to primitive spreadsheet views or simple tabular reports—or if accessing and integrating data is too complicated. Data visualization can help users discover insights, see trends and patterns in the data, and share what they find with others quickly and easily.
David Stodder
Sponsored by
SAP