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Documents related to » Extract 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.

EXTRACT 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

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.

EXTRACT DATA: in place or after extracting it into BusinessObjects Data Insight Automatic discovery of business rules and relationships Support for data redundancy profiling, drill-down frequency distributions, crosscolumn and cross-table comparisons, and pattern recognition Allows users to establish thresholds and set up automated alerts to notify if analysis results exceed a specific quality metric Graphical and dashboard reports (using Crystal Reports®) that provide summary profiles, frequency distributions, and
6/1/2009 5:10:00 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.

EXTRACT DATA: tables via ODBC and extract out the data needed for analysis. Most data cleansing vendors have built their own proprietary tools that they load and run to do the project and then remove once the project is complete. In summary, cleansing data is a big commitment of time and resources. Businesses are completely dependent on their data. It is a critical corporate asset and needs to be treated that way. From one perspective, a business is only its data - its customer data, its employee data, its product
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.

EXTRACT DATA: everything from data integration– extract, transform, and load (ETL) or enterprise 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
6/1/2009 5:02:00 PM

Beware of Legacy Data - It Can Be Lethal
Legacy data can be lethal to your expensive new application – two case studies and some practical recommendations.

EXTRACT DATA: Beware of Legacy Data - It Can Be Lethal Beware of Legacy Data - It Can Be Lethal Jan Mulder - August 23, 2002 Read Comments Beware of Legacy Data It Can Be Lethal Featured Author - Jan Mulder - August 23, 2002 Introduction The term legacy is mostly used for applications. For example, according to the Foldoc dictionary, legacy is: A computer system or application program which continues to be used because of the cost of replacing or redesigning it and often despite its poor competitiveness and
8/23/2002

Creating a Winning Data Transmission Service
Creating a Winning Data Transmission Service. Get Advice for Your Evaluation In Relation To Data Transmission Service. Today’s data transmission departments are battling for budget and relevance. Moving files and ensuring delivery is getting tougher every day. To successfully deliver data to an increasing number of target platforms and meet rising customer expectations, leading companies are adopting service-oriented architectures (SOAs) and upgrading their file transfer departments into data transmission services. Find out more.

EXTRACT DATA: Creating a Winning Data Transmission Service Creating a Winning Data Transmission Service Source: Sterling Commerce, an AT&T company Document Type: White Paper Description: Today’s data transmission departments are battling for budget and relevance. Moving files and ensuring delivery is getting tougher every day. To successfully deliver data to an increasing number of target platforms and meet rising customer expectations, leading companies are adopting service-oriented architectures (SOAs) and
11/3/2008 1:06:00 PM

SAS Puts the “E” in “Data”
SAS Institute has applied its data mining technology to the Internet. The company released products that will help companies analyze and predict the behavior of Web surfers. The target customer is one with large volumes of enterprise data that come from a variety of sources.

EXTRACT DATA: SAS Puts the “E” in “Data” SAS Puts the “E” in “Data” D. Geller - March 27, 2000 Read Comments Event Summary SAS Institute, Inc., the largest privately held software company, introduced a family of E-intelligence products that provide analysis of the data collected at a website. Although the announcement covers three products, the SAS strategy is to present a solution that encompasses all of the data mining needs in a large enterprise. The WebHound product is a high-end clickstream
3/27/2000

A Roadmap to Data Migration Success
Many large business initiatives and information technology (IT) projects depend upon the successful migration of data—from a legacy source, or multiple sources, to a new target database. Effective planning and scoping can help you address the associated challenges and minimize risk for errors. This paper provides insights into what issues are unique to data migration projects and to offer advice on how to best approach them.

EXTRACT DATA: A Roadmap to Data Migration Success A Roadmap to Data Migration Success Source: SAP Document Type: White Paper Description: Many large business initiatives and information technology (IT) projects depend upon the successful migration of data—from a legacy source, or multiple sources, to a new target database. Effective planning and scoping can help you address the associated challenges and minimize risk for errors. This paper provides insights into what issues are unique to data migration projects and
3/16/2011 11:27:00 AM

A Guide to Intelligent Data Auditing
Data auditing is a form of data protection involving detailed monitoring of how stored enterprise data is accessed, and by whom. Data auditing can help companies capture activities that impact critical data assets, build a non-repudiable audit trail, and establish data forensics over time. Learn what you should look for in a data auditing solution—and use our checklist of product requirements to make the right decision.

EXTRACT DATA: A Guide to Intelligent Data Auditing A Guide to Intelligent Data Auditing Source: Tizor Document Type: Checklist/Guide Description: Data auditing is a form of data protection involving detailed monitoring of how stored enterprise data is accessed, and by whom. Data auditing can help companies capture activities that impact critical data assets, build a non-repudiable audit trail, and establish data forensics over time. Learn what you should look for in a data auditing solution—and use our checklist of
3/19/2008 6:06:00 PM

Data Quality Strategy: A Step-by-Step Approach
To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality.

EXTRACT DATA: Data Quality Strategy: A Step-by-Step Approach Data Quality Strategy: A Step-by-Step Approach Source: SAP Document Type: White Paper Description: To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP
3/16/2011 2:03:00 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.

EXTRACT 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


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