On Demand
The number of options for storing, manipulating and accessing data have exploded over the last decade. Open source “NoSQL” (not-only SQL) have spread like wildfire among organizations with cutting-edge analytics. They, along with Hadoop, have lowered the cost barrier to powerful, flexible and incredibly scalable implementations of systems that access unstructured, semi-structured and flexibly-structured data in addition to relational data. However, the learning- and investment-curves have been prohibitive for many organizations. It is all too common that a company would like to advance its database and analytics capabilities with non-relational data but they don’t have the time, human resources or budgets to dedicate to spinning-up and learning how to use these new technologies.
Aaron Fuller
Sponsored by
IBM
Andy Hayler brings his wealth of practical experience of master data management projects to bear in order to explore best practice in MDM. Drawing heavily on practical project experience, supplemented by survey data from customer projects, he explains the most common problems that MDM projects encounter, and how to avoid them.
Andy Hayler
Sponsored by
IBM
The Internet of Things (IoT) is a computing paradigm where a widening range of physical devices—including smartphones, vehicles, shipping pallets, kitchen appliances, manufacturing robots, and anything fitted with a sensor—can transmit data about their location, state, activity, and surroundings. Depending on the device type, some may also receive data and instructions that control device behavior.
Philip Russom, Ph.D.
Sponsored by
SAP
In this presentation, we discuss the need for creating a managed data environment that supports the needs of all users of analytical data while ensuring the creation of governed, sharable, and portable data integration and governance work products.
Claudia Imhoff, Ph.D.
Sponsored by
IBM
Everyone is talking about machine learning—software that can learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in big data in a way that is beyond human capabilities. The technology is being used in a wide range of industries for use cases including fraud prevention, predicting crop yields, preventing and mitigating natural disasters, predictive maintenance of enterprise assets, and improving supply chain efficiencies.
Fern Halper, Ph.D.
Sponsored by
SAP
Predictive analytics is on the verge of widespread adoption. Enterprises are extremely interested in deploying predictive capabilities. In a recent TDWI survey about data science, about 35 percent of respondents said they had already implemented predictive analytics in some way. In a 2017 TDWI education survey, predictive analytics was the top analytics-related topic respondents wanted to learn more about.
Fern Halper, Ph.D.
Sponsored by
SAS, Alteryx, Cloudera
To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two.
Philip Russom, Ph.D.
Sponsored by
SAP