On Demand
Today’s BI environments have split personalities. They must support the production of routine reports and analyses used every day for decision-making by line-of-business employees, and yet, also enable data scientists and data crunchers to “freewheel” through the data in an unplanned, experimental fashion. What magic is this? How can implementers create a sustainable BI environment with these two seemingly contradictory purposes? Does one replace the other? What are the technological requirements for this new world?
Claudia Imhoff, Ph.D.
Sponsored by
SAP
High-quality data visualization is critical to the success of business intelligence, analytics, and data presentation. Because graphical interaction with data is now the norm, users are excited—and also have increasingly high expectations. Technology is important, but you also need to execute best practices to avoid pitfalls and to create beautiful data visualizations that are clear, effective, and accurate.
David Stodder
Sponsored by
TDWI and IBM Content
According to multiple TDWI surveys, the vast majority of IT users feel that big data is an opportunity, because of the new and more granular insights it provides about customers, operations, partners, and many other business entities and processes. Likewise, most users now see advanced forms of analytics as the primary path to reaping insights from big data, whether big data comes from traditional enterprise applications or new sources, such as Web applications, application logs, sensors, machines, and social media. For these reasons, TDWI sees many user organizations diving deeper into big data analytics.
Philip Russom, Ph.D.
Sponsored by
Teradata
Valuable organizational data about a company’s business and its customers is often found in reports. This might include financial statements, billing invoices, healthcare patient records, or statements of benefits. Such data is often stored in a company’s enterprise content management (ECM) systems.
Fern Halper, Ph.D.
Sponsored by
Datawatch
In-memory database management systems have matured to the point where they predictably promise accelerated application performance. By adopting alternative storage layouts amenable to in-memory processing, these databases take advantage of efficient use of available memory to reduce or even eliminate the data latencies typically associated with significantly slower disk-based storage media.
David Loshin
Sponsored by
SAP
As companies seek to gain competitive advantage by utilizing analytics, a change is occurring in terms of the data and infrastructure that supports it. A number of technology factors—including big data, Hadoop, and advances in analytics—are coming together to form the fabric of an evolving analytics ecosystem. Advanced analytics, in particular, are becoming more important as companies embrace big data. This includes techniques such as advanced visualization and machine learning that can be particularly beneficial in big data discovery and analysis.
Fern Halper, Ph.D.
Sponsored by
SAS
The complexity of data warehouse environments has increased dramatically in recent years with the arrival of data warehouse appliances, columnar database management systems, NoSQL databases, Hadoop, and tools for multiple forms of advanced analytics or real-time operation. The new vendor and open source platforms come in response to users’ growing demands for platforms optimized for various forms of big data, analytics, real-time operation, and the workloads that go with them.
Philip Russom, Ph.D.
Sponsored by
Actian, Cloudera, Datawatch, Dell EMC, Hewlett Packard Enterprise, MapR