On Demand
IT operations management (ITOM) deals with monitoring and controlling IT infrastructure and services such as networks, servers, and help desk. Today, IT management typically relies on “swivel chair” monitoring between unrelated reactive monitoring tools. However, this is changing. Modern, interrelated IT departments can benefit from a single view across IT to improve root cause analysis and reduce meantime to resolution.
Fern Halper, Ph.D.
Sponsored by
SAP
Big data and data science can provide a significant path to value for organizations. These technologies, methodologies, and skills can help organizations gain additional insight about customers and operations; they can help make organizations more efficient, be a new source of revenue, and make organizations more competitive.
Fern Halper, Ph.D.
Content Provided by TDWI and
IBM, MapR, OpenText, Snowflake
It’s no surprise that data warehouse professionals are quickly adopting Hadoop. According to a recent TDWI survey, the number of deployed Hadoop clusters is up 60% over two years. While Hadoop is an effective design pattern for capturing and quickly ingesting a wide range of raw data types, there have been a number of challenges organizations have faced in realizing the true business value from their Hadoop-based data lakes.
Philip Russom, Ph.D.
Sponsored by
Pentaho
In today’s demanding economic environment, companies that can develop and deploy analytics faster have a significant competitive edge. They can use analytics to detect patterns and changes in markets, learn customer preferences, be alert to fraudulent activity, and more. With the advent of cloud computing, users quickly gain access to new data sources and analytic techniques, enabling companies to finally unleash their analytics – they are no longer constrained by the limits of their on-premises computing, database platform, data warehouse, and data storage capacity. However, to avoid even more data siloes, data governance issues, and more, organizations should consider a hybrid analytics architecture that brings together on premises and cloud, enabling a more controlled journey to the cloud, while enjoying the flexibility, power, and speed they need to handle a range of analytics demands.
David Stodder
Content Provided by
TDWI, IBM
Organizations that seek to be data-driven are experiencing considerable change of late, because data itself, the management of data, and the ways businesses leverage data are all evolving at accelerated rates. These changes sound like problems, but they are actually opportunities for organizations that can embrace new big data, implement new design patterns and platforms for data, scale to greater volumes and processing loads, and react accordingly via analytics for organizational advantage.
Philip Russom, Ph.D.
Sponsored by
Cloudera, Teradata
In a 2015 survey by TDWI, 69% of respondents identified SQL on Hadoop as a must-have for making Hadoop ready for enterprise use. This is not surprising because both technical and business users know and love SQL, plus have portfolios of tools that rely on it. The catch is that early versions of Hadoop were devoid of ANSI-standard SQL.
Philip Russom, Ph.D.
Content Provided
IBM, Looker, Teradata
Many organizations need a more modern data warehouse platform to address a number of new and future business and technology requirements. Most of the new requirements relate to big data and advanced analytics, so the data warehouse of the future must support these in multiple ways, while still supporting older data types, technologies, and business practices. Hence, a leading goal of the modern data warehouse is to enable more and bigger data management solutions and analytic applications, which in turn help the organization automate more business processes, operate closer to real time, and through analytics learn valuable new facts about business operations, customers, products, and so on.
Philip Russom, Ph.D.
Sponsored by
Microsoft