Data Management

Once a business clears the financial hurdle and begins to plan their data protection strategy the choices can appear daunting. Do they select tape or disk? Apply differential or incremental backups? How much will a new system cost and how much time will it take to implement? What data should be included in the backup? This article attempts to address these concerns.

Quocirca conducted two surveys separated by 12 months that looked at the data management infrastructure of UK and Irish businesses and how this was linked to their ability to respond to the whims of the regulators. In the year between the two surveys a lot had changed.

Many organizations have no policies in place for retaining, managing, and purging documents. This paper looks at some of the basic components of a document strategy—retention, management, and destruction—and highlights some of the current compliance drivers that make it all necessary.

Download this step by step guide to learning how mid-size companies can benefit from Business Intelligence. You'll learn the benefits of BI and why mid-size companies need it for effective decision-making and improved results.

Master Data Management is a cohesive strategy for managing all master data domains.  Where is your organization most likely to derive business value from a Master Data Management solutions?  This report answers these questions and details the path forward:  Adaptive Master Data Management.

This comprehensive report addresses industry-specific issues for the Financial Services sector, Insurance, Pharmaceutical companies, Retailers, Manufacturing, and High-Tech.

This white paper outlines the components of the Banking Data Warehouse (BDW) and how they assist financial institutions in addressing the data modeling and data consolidation issues relating to the SOX regulations.

This white paper will define different vendor approaches to developing business services and the relative cost implications of each approach for the customer. 

The purpose of this paper is to highlight the issues, requirements, and technologies available for automated advanced name recognition.

This paper gives the reader an overview of the functionality of IBM's WebSphere Customer Center, and explains why IBM is the strategic partner that more Fortune 500 companies select as their strategic platform for Customer Data Integration.

This white paper will outline the components of the Banking Data Warehouse (BDW) and how they assist financial institutions to address the data modeling and data consolidation issues relating to the Basel II Capital Accord. 

In this paper, we will discuss the benefits of BPM and SOA in more detail and show how leading companies have recognized that implementing BPM and SOA together gives them unique capabilities.

This white paper describes how the Clarabridge Content Mining Platform avoids the pitfalls of previous approaches to unstructured analysis, and capitalizes on lessons learned from solving similar problems in the structured domain.

As the demand for bandwidth grows, so does the reach of networks. Users need to access data globally, from multiple locations around the world, quickly and transparently without breaking budgets. Data centers locked into servicing only local users unnecessarily constrain growth and expansion.

This paper describes some of the best practices that many companies have adopted and are able to make mass changes to SAP data in record time with minimal resources.

This white paper describes how many companies are realizing significant productivity gains by empowering the business users with tools to transfer their own data between spreadsheets and SAP.

This paper describes 7 best practices that many companies have adopted in automating the upload of Excel data into SAP. Adopting these best practices will alleviate many of the pains that business users and IT analysts face in uploading Excel data to SAP.

This paper examines the nature, cause and impact of data quality problems in typical IT system implementations.  It argues that the extent and impact of poor data quality is largely misunderstood and often ignored.