Mellon Bank looks to enhance its complex customer service relationships through IBM’s Intelligent Miner for Data.
While many large companies began installing enterprise resource planning systems several years ago, the push for Y2K systems remediation, combined with the cost-effectiveness of shared information over the Internet has prompted smaller companies to begin building their own. This growing interest has vendors rushing to find their niche.
Suprise! You thought that your little warehousing project was going to move along without a hitch. Well, guess again.
No matter what form of data conversion project your organization is involved in, the methods can be time-consuming and a little risky. But one thing is for certain: The critical first step in data migration is data profiling.
As an organization’s data warehouse grows and changes, managing the volumes of data stored in the warehouse becomes increasingly complex, paving the way for metadata and metadata management
Companies in industries that were handcuffed with only syndicated data have discoverd reservoirs of data within their own organizations, but are struggling with how to extract and use it. Business Intelligence systems that recognize and prioritize critical data can give companies the competitive edge they crave in their race against time.
After processing has been completed, it has been estimated that 30-40 percent of help desk problems concern output management. The issues, challenges and - most importantly - the solutions are addressed in thi article.
Web-enabling a data warehouse has a seductive lure. In this article, David Cook explores the strategic challenges that face businesses and navigates through integration, analysis, structuring and reporting to reach the next level of data retrieval.
The Web is driving OLTP to ever-higher demands for performance and scalability, so when the time comes to re-evaluate enterprise architecture, exploring a new database alternative can be truly rewarding.
While some IT professionals still view data mining as low priority within their day-to-day network operations; more and more, the software tools used in business technology management are borrowing from data mining methods to become more intelligent.
Data warehousing has changed the face of competition in many industries. But, to become a truly strategic resource, the warehouse must be able to accommodate more data than you think you have, more users than you know exist, more complexity than the best DBAs could tune for - and do these things in a cost-effective manner
The creation and enforcement of referential integrity - the practice of ensuring that relationships between rows of data exist and are used as they are defined - and especially bounded referential integrity give the user a feeling of confidence in the data and predefined relationships in completing complicated tasks.
As we move into the next stage of data mining, businesses see the need to automate more and more of the process, beginning with the collection and warehousing of the appropriate data and ending with the incorporation of the appropriate predictive model into operational systems.
The do's, do not's and the dare not's in data warehousing when designing, implementing and using a DB2-based data warehouse for OS/390.
The authors propose an enterprise architecture for delivering dependent data marts that will satisfy varying end user's DSS needs, and features the ability to allow the reuse of dimensions across the enterprise.
The right tools for the right job as the saying goes. The competitive edge goes to the company with the best information and the most-efficient processes for delivering it. But often lost in this mix is the price tag that accompanies the actual moving of legacy data from older to newer enterprise systems.
As solid state disks show signs of rebirth and growth, vendors and their integrators must identify what values SSD offers within the context of modern enterprise computing.