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Documents related to » visual data manager


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.

VISUAL DATA MANAGER: 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

Four Critical Success Factors to Cleansing Data
Four Critical Success Factors to Cleansing Data. Find Guides, Case Studies, and Other Resources Linked to Four Critical Success Factors to Cleansing Data. Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology.

VISUAL DATA MANAGER: Four Critical Success Factors to Cleansing Data Four Critical Success Factors to Cleansing Data Source: PM ATLAS Business Group, LLC Document Type: White Paper Description: Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology. Four Critical Success Factors to
1/14/2006 9:29:00 AM

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.

VISUAL DATA MANAGER: Data Quality: A Survival Guide for Marketing Data Quality: A Survival Guide for Marketing Source: SAP Document Type: White Paper Description: 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
6/1/2009 5:02:00 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.

VISUAL DATA MANAGER: Developing a Universal Approach to Cleansing Customer and Product Data Developing a Universal Approach to Cleansing Customer and Product Data Source: SAP Document Type: White Paper Description: 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
6/1/2009 5:10:00 PM

Optimizing Gross Margin over Continously Cleansed Data
Optimizing Gross Margin over Continously Cleansed Data.Reports and Other Software System to Use In Your System for Optimizing Gross Margin over Continously Cleansed Data. Imperfect product data can erode your gross margin, frustrate both your customers and your employees, and slow new sales opportunities. The proven safeguards are automated data cleansing, systematic management of data processes, and margin optimization. Real dollars can be reclaimed in the supply chain by making certain that every byte of product information is accurate and synchronized, internally and externally.

VISUAL DATA MANAGER: Technology Products , Data Visualization Products , Dropship Product Data Feed , Extreme Data Products , General Data Products , Gross State Product Data , Information Data Products , Integrated Data Products , International Data Products , Market Data Products . EXECUTIVE SUMMARY Distributors have spent many years implementing both technology and business processes to reduce the cost of doing business. Great strides and advancements in “productivity” have been gained in the interest of profitability
6/20/2006 9:23:00 AM

The Evolution of a Real-time Data Warehouse
Real-time data warehouses are common in some organizations. This article reviews the basic concepts of a real-time data warehouse and it will help you determine if your organization needs this type of IT solution.

VISUAL DATA MANAGER: The Evolution of a Real-time Data Warehouse The Evolution of a Real-time Data Warehouse Jorge García - December 23, 2009 Read Comments Understanding Real-time Systems Today, real time computing is everywhere, from customer information control systems (CICSs) to real-time data warehouse systems. Real-time systems have the ability to respond to user actions in a very short period of time. This computing behavior gives real-time systems special features such as instant interaction: users request information
12/23/2009

The Value of Big Data
As the use of big data grows, the need for data management will also grow. Many organizations already struggle to manage existing data. Big data adds complexity, which will only increase the challenge. This white paper looks at what big data is, the value of big data, and new data management capabilities and processes, required to capture the promised long-term value.

VISUAL DATA MANAGER: The Value of Big Data The Value of Big Data Source: SAP Document Type: White Paper Description: As the use of big data grows, the need for data management will also grow. Many organizations already struggle to manage existing data. Big data adds complexity, which will only increase the challenge. This white paper looks at what big data is, the value of big data, and new data management capabilities and processes, required to capture the promised long-term value. The Value of Big Data
8/8/2013 5:27:00 PM

Metagenix Reverse Engineers Data Into Information
Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries of the existing legacy systems (which are often incorrect). Other unique Metagenix approaches include an

VISUAL DATA MANAGER: Metagenix Reverse Engineers Data Into Information Metagenix Reverse Engineers Data Into Information M. Reed - February 15, 2001 Read Comments M. Reed - February 15, 2001 Event Summary Metagenix, Inc. has designed its flagship product, MetaRecon to, as they put it, Decipher Your Data Genome . The product reverse engineers all of the metadata ( data about data ) from data sources and generates information that is very helpful to developers in designing specifications for a new data store, and assists
2/15/2001

Poor Data Quality Means A Waste of Money
Data quality sounds like a motherhood and apple pie issue, of course we want our data to be right. However, very few enterprises get serious about it. Maybe that's because the cost of data quality is hidden. That cost can be huge.

VISUAL DATA MANAGER: Poor Data Quality Means A Waste of Money Poor Data Quality Means A Waste of Money Olin Thompson - September 23, 2003 Read Comments What Is Data Quality? When reality and the data in your system do not agree, you have a data quality problem. The system says you have 10,000 cases but you actually have 9,850. A customer s ship-to address is out of date. You have the same piece of information in two places in two applications or even two systems, but they do not agree. Many things can cause data quality
9/23/2003

Master Data Management
It’s common to hear that master data management (MDM) projects are difficult to initiate. But pairing up an MDM project with another initiative already on your organization’s priority list might be easier than you think. Find out some of the basics surrounding MDM itself, including what MDM can refer to, as well as how to couple it with other projects that may already have momentum in your organization.

VISUAL DATA MANAGER: Master Data Management Master Data Management Source: Skura Corp Document Type: White Paper Description: It’s common to hear that master data management (MDM) projects are difficult to initiate. But pairing up an MDM project with another initiative already on your organization’s priority list might be easier than you think. Find out some of the basics surrounding MDM itself, including what MDM can refer to, as well as how to couple it with other projects that may already have momentum in your
6/26/2008 7:52:00 PM

Best Practices for a Data Warehouse on Oracle Database 11g
Best Practices for a Data Warehouse on Oracle Database 11g. Find Out Software and Other Solutions for Your Decision Associated with Best Practices and Data Warehouse Management. Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW performs and scales well, you need to get three things right: the hardware configuration, the data model, and the data loading process. Learn how designing these three things correctly can help you scale your EDW without constantly tuning or tweaking the system.

VISUAL DATA MANAGER: Best Practices for a Data Warehouse on Oracle Database 11g Best Practices for a Data Warehouse on Oracle Database 11g Source: Oracle Document Type: White Paper Description: Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW performs and scales well, you need to get three things right: the hardware configuration, the data model, and the data loading process. Learn how designing these three things
4/20/2009 3:11:00 PM


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