On Demand
While many believe that the maturation of end-user tools supporting visualization, reporting, and analytic signals the imminent demise of the data warehouse, nothing could be farther from the truth. The increasing business user demand for information highlights the need for a centralized nerve center provided by the organization’s data warehouse. In turn, the future data warehouse requires technologies that accelerate design and development, improve cycle time in producing reports and analyses, and enhance the IT-business collaboration.
David Loshin
Sponsored by
TimeXtender
What good is analytics if no one takes action on it? Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions—at the point of decision making. These systems might be automated or provide manual, actionable insights. Analytics is currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex, and organizations are at different stages of operational deployment. Newer examples of operational analytics include support for logistics, asset management, customer call centers, and recommendation engines—to name just a few.
Fern Halper, Ph.D.
Sponsored by
Information Builders, OpenText, Pentaho, SAP, SAS, Tableau Software, Talend
Excellence in analytics is a competitive advantage in nearly all industries. For this reason, organizations need their data scientists, business analysts, and business users to be able to access and interact with more sources and more types of data than ever before. The Hadoop ecosystem is flourishing, producing innovative technologies and frameworks such as Apache Spark, Apache Apex, and more that are becoming important for providing processing speed and power as well as data integration and preparation capabilities for fast, visual, and interactive analytics.
David Stodder
Sponsored by
Cloudera, Talend, DataTorrent, Platfora
There’s a lot of buzz currently about how many types of IT systems need updates, upgrades, extensions, and replacements, due to recent changes in business and technology requirements. Current parlance refers to these collectively as “modernization” projects.
Philip Russom, Ph.D.
Sponsored by
SAS
The term “data-driven” has become an accepted principle for modern organizations, but to drive modern, agile businesses, each data consumer’s view of enterprise data must both align with individual data quality and usability criteria and remain consistent with other data users in the organization. While traditional data quality/data preparation tools were intended to ensure accuracy and trust, the conventional wisdom centered on a technical, IT-centric usage model.
David Loshin
Sponsored by
Trillium Software
Analytics are today’s business weapon of choice. Changing business environments and competitive pressures have driven companies to seek a new edge from innovative technologies such as Hadoop, specialized data stores, and the cloud. This expanding and constantly evolving set of data sources means the enterprise data warehouse can no longer be the singular physical location for all large-scale information management.
Claudia Imhoff, Ph.D.
Sponsored by
TDWI and IBM Content
There is growing awareness that for practitioners to effectively manage data as an asset to the business, that data must not be simply collected and moved between systems, but must be validated to ensure the level of trust that the data is fit for its various downstream purposes. This demands conformance to business rules that accurately reflect meeting the needs of defined business policies. Understanding business policies, transforming them into data rules, and implementing those rules is the process of data governance. Organizations whose understanding enables their ability to effectively govern their data are gaining business advantage as they leverage data quality, metadata, and data governance tools to translate business policies into consistent, useful data.
David Loshin
Sponsored by
Oracle