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Documents related to » meta data


Six Steps to Manage Data Quality with SQL Server Integration Services
Six Steps to Manage Data Quality with SQL Server Integration Services. Read IT Reports Associated with Data quality. Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

META DATA: Six Steps to Manage Data Quality with SQL Server Integration Services Six Steps to Manage Data Quality with SQL Server Integration Services Source: Melissa Data Document Type: White Paper Description: Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in
9/9/2009 2:32:00 PM

Achieving a Successful Data Migration
Achieving a Successful Data Migration. Solutions and Other Software to Delineate Your System and for Achieving a Successful Data Migration. The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.

META DATA: lineage capability, such as metadata management. Data profiling, validation, and cleansing are also important. In application environments data quality is also about business rules. Data must obey to unique business rules, as well as meet validation thresholds. Use Case Scenario Consolidate Systems Need: Simplify parts, materials, and supplies by migrating data from 15 incompatible mainframe and legacy systems to SAP R/3. Approach: Leverage data integration platform with easy-to-use development tools,
10/27/2006 4:30:00 PM

Developing a Universal Approach to Cleansing Customer and Product Data
Developing a Universal Approach to Cleansing Customer and Product Data. Find Free Proposal and Other Solutions to Define Your Acquisition In Relation To Cleansing Customer and Product Data. Data quality has always been an important issue for companies, and today it’s even more so. But are you up-to-date on current industry problems concerning data quality? Do you know how to address quality problems with customer, product, and other types of corporate data? Discover how data cleansing tools help improve data constancy and accuracy, and find out why you need an enterprise-wide approach to data management.

META DATA: and to report on metadata and data lineage (BusinessObjects Metadata Manager). BusinessObjects Data Insight XI BusinessObjects Data Insight XI is a data profiling tool that handles the data assessment task (see Figure 1) of a data quality program. It is used to monitor, measure, analyze, and report on the quality of data stored in relational database systems and flat files. It helps users gain visibility and a detailed understanding of the data quality defects in the source data. Key features of Data
6/1/2009 5:10:00 PM

Data Quality: A Survival Guide for Marketing
Data Quality: a Survival Guide for Marketing. Find Free Blueprint and Other Solutions to Define Your Project In Relation To Data Quality. The success of direct marketing, measured in terms of qualified leads that generate sales, depends on accurately identifying prospects. Ensuring data accuracy and data quality can be a big challenge if you have up to 10 million prospect records in your customer relationship management (CRM) system. How can you ensure you select the right prospects? Find out how an enterprise information management (EIM) system can help.

META DATA: information integration (EII)–through metadata management, data quality, and building specialized reporting marts. Data integration applications are a primary deployment mechanism for data quality functionality, which makes it convenient to cleanse data when it is being moved into or out of your CRM/CDI system. Today’s data quality vendors have built rich and deep functionality that can remediate almost any customer data problem, and they’ve structured their deployment mechanisms to give
6/1/2009 5:02:00 PM

What’s the Big Deal with Big Data? » The TEC Blog
White Paper Review (23) Meta Login --> More TEC Blogs Foro Empresarial TEC Resources Articles Decision Support Evaluation Centers RFP Templates Software Evaluation Reports White Papers Archives September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 October 2012 September 2012 August 2012 July 2012 June 2012 May 2012 April 2012 March 2012 February 2012 January 2012 December 2011 November 2011 October 2011 September 2011 August

META DATA: big data, TEC, Technology Evaluation, Technology Evaluation Centers, Technology Evaluation Centers Inc., blog, analyst, enterprise software, decision support.
23-07-2013

Got Big Data? Net Big Dollars!
Data is growing at unprecedented rates. Data on customers, producers, underwriting, claims, and service providers is just part of the picture. This increase is being driven by social media and mobile devices adding text and other nonstructured, as well as structured, data. Read this report to find out about the tremendous payback that comes from managing huge repositories of data.

META DATA: Got Big Data? Net Big Dollars! Got Big Data? Net Big Dollars! Source: Mindshare Technologies Document Type: White Paper Description: Data is growing at unprecedented rates. Data on customers, producers, underwriting, claims, and service providers is just part of the picture. This increase is being driven by social media and mobile devices adding text and other nonstructured, as well as structured, data. Read this report to find out about the tremendous payback that comes from managing huge repositories of
2/11/2013 1:26:00 PM

Data Quality Basics
Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at profit margins. It can also create useless information in the form of inaccurate reports and market analyses. As companies come to rely more and more on their automated systems, data quality becomes an increasingly serious business issue.

META DATA: Data Quality Basics Data Quality Basics Source: Trillium Software Document Type: White Paper Description: Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at profit margins. It can also create useless information in the form of inaccurate reports and market analyses. As companies come to rely more and more on their automated systems, data quality becomes an increasingly serious business issue. Data
10/27/2006 4:30:00 PM

Data Quality Trends and Adoption
While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies reveal about DQ goals and drivers.

META DATA: Data Quality Trends and Adoption Data Quality Trends and Adoption Source: SAP Document Type: White Paper Description: While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies
3/22/2011 9:46:00 AM

Jaspersoft 4 Goes Big Data » The TEC Blog
White Paper Review (23) Meta Login --> More TEC Blogs Foro Empresarial TEC Resources Articles Decision Support Evaluation Centers RFP Templates Software Evaluation Reports White Papers Archives September 2013 August 2013 July 2013 June 2013 May 2013 April 2013 March 2013 February 2013 January 2013 December 2012 November 2012 October 2012 September 2012 August 2012 July 2012 June 2012 May 2012 April 2012 March 2012 February 2012 January 2012 December 2011 November 2011 October 2011 September 2011 August

META DATA: bi, Business Intelligence, cassandra, couchdb, Greenplum, hadoop, hbase, Jaspersoft, Jaspersoft 4.0, mongodb, neteeza, nosql, open source, vertica, voltdb, TEC, Technology Evaluation, Technology Evaluation Centers, Technology Evaluation Centers Inc., blog, analyst, enterprise software, decision support.
27-01-2011

EMC to Buy Data General
WALTHAM, Mass., August 9th, 1999 (Reuters) - High-end data storage leader EMC Corp. (NYSE:EMC) on Monday moved to grab a chunk of the mid-range storage business with a deal to buy Data General Corp. (NYSE:DGN) for $1.1 billion in stock, the companies said.

META DATA: EMC to Buy Data General EMC to Buy Data General R. Krause - August 13, 1999 Read Comments EMC to Buy Data General WALTHAM, Mass., August 9th, 1999 (Reuters) High-end data storage leader EMC Corp. (NYSE:EMC) on Monday moved to grab a chunk of the mid-range storage business with a deal to buy Data General Corp. (NYSE:DGN) for $1.1 billion in stock, the companies said. EMC, which holds a 35 percent share of the $12 billion high-end storage market, said it will lend its considerable distribution and support
8/13/1999

A CRM System Needs A Data Strategy
A customer relationship management (CRM) system is inherently valuable for supporting customer acquisition and retention by gathering data from each contact with customers and prospects. Collecting data, however, cannot be isolated from a strategy for actually using that data. Here is an overview of how to evolve the focus of a data strategy to specifically suit both the acquisition and retention phases.

META DATA: A CRM System Needs A Data Strategy A CRM System Needs A Data Strategy David McNamara - July 3, 2003 Read Comments Introduction An underutilized customer relationship management (CRM) system - or one that cannot match its owner s expectations - will reflect poorly on both the vendor who sold it and the IT manager who authorized the purchase and installed it. Both, however, can help successfully manage such expectations (and add value to their respective roles) by wisely counseling about the strategic
7/3/2003


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