On Demand
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
More and more, companies are looking to advanced analytics to compete effectively. These analytics include predictive analytics, text analytics, geospatial analytics, big data analytics, and more. As part of this analytics ecosystem, organizations also have to contend with the infrastructure to support the analytics. Appliances, analytics platforms, and unified information architectures have become an important component of this equation. This developing analytics ecosystem can be quite complex.
Fern Halper, Ph.D.
Sponsored by
Actian, Cloudera, Datawatch, Pentaho, SAP, SAS
Modern businesses must be faster, more flexible, and more responsive than ever before. Traditional BI, focused on predefined reports and rear-view queries, will no longer suffice.
Barry Devlin
Sponsored by
SAP
As the value of advanced analytics becomes better-known, many companies face challenges in getting started. A primary challenge is lacking the skill set to make advanced analytics happen. As the market evolves, innovations occurring in software, infrastructure, and organizational practices can open up analytics to new users. These include making analytics easier to use, algorithmic innovations, semantics and analytics, organizational innovation, and the cloud.
Fern Halper, Ph.D.
Sponsored by
TDWI and IBM Content
Hadoop is rapidly moving from prototyping and standalone solutions to an important component of the enterprise IT ecosystem. At the same time, the volume of data being managed by Hadoop systems in support of enterprise data hubs and refineries, as well as analytic applications, is growing rapidly.
Colin White
Sponsored by
MapR, Voltage Security