December 7, 2012
By Dave Wells
Data is an essential business resource that is as critical to business success as financial and human resources are. The disciplines of data resource management include strategy, architecture, and governance. Mistakes to avoid in data resource management span all three disciplines.
November 15, 2012
Download this TDWI E-Book to find out why turning business people into self-empowered “analytic explorers” involves more than just equipping them with discovery tools and turning them loose on massive data sets.
Sponsored By SAP, WCI Consulting
September 26, 2012
This TDWI Checklist Report presents tips for aligning geospatial information with the business intelligence environment, empowering analytics to use location data to yield operational efficiencies, revenue growth, and other potential benefits.
Sponsored By Tableau Software
August 29, 2012
There are good technology and business reasons why master data management needs data governance. This TDWI Checklist Report drills into seven of these reasons as well as use cases and organizational situations where DG and MDM work well together.
Sponsored By SAS Dataflux
July 2, 2012
Customer analytics and intelligence initiatives are undergoing major changes with the explosion in detailed customer behavior data, including that generated by social media. Today, organizations implement a variety of technologies to analyze customer data, including business intelligence (BI) tools, data warehouses, analytic databases, predictive analytics, and more. This report examines organizations’ experiences with customer analytics technologies in the age of social media and recommend best practices for improving customer intelligence and reaching customer-centric goals.
Sponsored By Greenplum, IBM, Informatica Corporation, SAP, SAS, Tableau Software, Teradata Aster, Vertica
June 27, 2012
Mounds of structured data, unstructured data, big data, and advancements in cloud technology are imposing new requirements for data integration. This TDWI Checklist Report will explore some of the key drivers these new requirements are intended to address. Whether you are looking to support the performance needs of big data applications, filter concepts from unstructured data, monitor hundreds of data feeds for unexpected behavior, export data across enterprise boundaries, or provide real-time reporting and analysis, there is a rapidly expanding need for data integration competency that extends well beyond traditional ETL.
Sponsored By Oracle
June 20, 2012
Data integration has long been a challenge for BI professionals. Now big data is adding a few wrinkles to the process. Download this TDWI E-Book to learn more about the current state of big data integration, why more data can create more problems, and to read expert Q&A about addressing this important new trend.
Sponsored By Syncsort