New Integrated Data Management Tools for DBAs
Overview Let's face it, DBAs have a tough job. DBAs must manage a complex set of tasks including ensuring high performance and availability, maintaining business critical production applications and managing changes. In these tough economic times, mistakes have even more significant consequences. Using IBM's Integrated Data Management tools for the DBA, users can effectively manage database and application changes and reduce the long, after hours finger pointing though proactive performance monitoring all while facilitating collaboration and driving organizational efficiencies. Empowered DBAs translates to better application performance, greater revenue, more satisfied customers and greater ability to meet SLAs.
| Publisher | IBM | ||
|---|---|---|---|
| Live Date | 27th January 2009 00:00 UTC | ||
| Format | Webcast | ||
| Topics | |||
-
Did you find this white paper useful? 8 out of 9 users found this white paper useful
White Paper RSS3 Strategies for Reducing IT Support Costs
DownloadAs companies brace for more bumps in the economic downturn, many organisations are indiscriminately cutting costs. To ensure a seamless transition into the post-recession market, however, slashing and burning is...
Forrester Strategies for Assessing IT Business Satisfaction
If you aren't assessing customer satisfaction you are overlooking a potential goldmine. This valuable data is crucial to creating a successful IT strategy. But where do you start? This new...
MSC Industrial Direct- customer case study
"Following a company merger, MSC Industrial Direct Co. found that duplicate customer records were disrupting the business workflow and causing sales compensation issues. MSC Industrial Direct Co. implemented the Pitney Bowes Business...
Customer Data Quality Platform from Pitney Bowes Business Insight - a Butler Group Technology Audit report
Pitney Bowes Customer Data Quality Platform (CDQP) is a domain-specific customer data quality management system that enables tasks such as integration, cleansing, matching, profiling, monitoring, and enriching the data with...
Data Quality Considerations for a Master Data Management Structure
Companies acquiring companies. Human Resources sharing information with Finance. Businesses spanning multiple countries. What do all of these scenarios have in common? The sharing of data. What is the critical...
