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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.
: provides the mechanism to access master data from various source systems, it is the Total Data Quality process that ensures integration with a high level of data quality and consistency. Once an organization s data is cleansed, its unique identifiers can be shared among multiple sources. In essence, a business can develop a single customer view — it can consolidate its data into a single customer view to provide data to its existing sources. This ensures accurate, consistent data across the
9/9/2009 2:32:00 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.
: because they don’t have access to it. INCOMPLETE DATA Incomplete data–such as blank data fields–causes a variety of problems for the marketer. First, and most obvious, when the address, email, or phone number field is blank, your ability to deliver the message is impacted. Second, if a field such as title, salutation, job code, or ethnicity is blank, your ability to segment prospects into the correct categories or demographics is impacted. And third, if any of those fields–and others like social
6/1/2009 5:02:00 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.
: - Responsible to provide access to necessary systems, architect of systems and integrations, and test environments. Data Cleansing Team - Responsible for facilitating data values and process reviews, system data analysis to find anomalies, documenting approved/allowed data values and business processes, obtain approval from all stakeholders for a specific field, cleanse the master data, support testing of data being passed to other systems, support functional requirements specifications for any system
1/14/2006 9:29:00 AM
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.
: tool for on-demand data access to a variety of data sources (BusinessObjects Data Federator), and a set of packaged data integration solutions (BusinessObjects Rapid Marts™) for enterprise applications such as Oracle, PeopleSoft, and Siebel. Data quality -a component for the profiling, cleansing, enhancement, matching, consolidation, and monitoring of data (BusinessObjects Data Insight XI and BusinessObjects Data Quality XI). This component is described in more detail below. Metadata management -a
6/1/2009 5:10:00 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.
: 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
Access Management: Efficiency, Confidence and Control
Managing access to protected resources can be risky business as new applications and services are deployed, as new users join the extended enterprise, or as existing users change roles. Strategy and technology choices—along with the policy, planning, process, and organizational elements of implementation—are critical factors in the overall success of an organization’s access management initiative.
: Access Management: Efficiency, Confidence and Control Access Management: Efficiency, Confidence and Control Source: SAP Document Type: White Paper Description: Managing access to protected resources can be risky business as new applications and services are deployed, as new users join the extended enterprise, or as existing users change roles. Strategy and technology choices—along with the policy, planning, process, and organizational elements of implementation—are critical factors in the overall
3/4/2011 2:28:00 PM
Data Quality Strategy: A Step-by-step Approach
Success start with data quality strategy: a step-by-step approach.Read Technology Evaluation Centers (TEC) whitepapers. To realize the full 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. The companies that approach this issue strategically are the companies that will be successful. Learn the six factors that go into a good data quality strategy, and find out how to go from strategy to implementation.
: profiling packages can directly access relational data sources identified in the storage factor. More sophisticated solutions are available to monitor nonrelational data sources, such as mainframe data and open systems flat files. Configuring the data profiling software involves establishing specific business rules to test. For example, a part number column may have two allowed formats: ###A### and ###-###, where # is any valid numeric character, and A is any character in the set A, B, C, and E. The user
1/25/2010 1:13:00 PM
Offering Employee Access Saves Money
Read how a mid-sized hospital immediately found savings of $43,500 a year by implementing a secure employee scheduling and human resource information system payroll solution. It was a cautious beginning, with an initial roll-out of just four of the many available with emPath®'s Employee Self-Service (ESS) features, but there is the promise of more dramatic savings to come.
: Offering Employee Access Saves Money Offering Employee Access Saves Money Source: NOW Solutions Document Type: Case Study Description: Read how a mid-sized hospital immediately found savings of $43,500 a year by implementing a secure employee scheduling and human resource information system payroll solution. It was a cautious beginning, with an initial roll-out of just four of the many available with emPath® s Employee Self-Service (ESS) features, but there is the promise of more dramatic savings to
4/6/2006 12:43:00 PM
Securing Data in the Cloud
When considering adopting cloud computing or software-as-a-service (SaaS), a question most potential customers ask vendors is “How secure will our data be in your hands?” Customers are right to ask this question and should closely examine any vendor’s security credentials as part of their cloud/SaaS evaluations. This document is intended to give a broad overview of one vendor’s security policies, processes, and practices.
: Securing Data in the Cloud Securing Data in the Cloud Source: Symantec Document Type: White Paper Description: When considering adopting cloud computing or software-as-a-service (SaaS), a question most potential customers ask vendors is “How secure will our data be in your hands?” Customers are right to ask this question and should closely examine any vendor’s security credentials as part of their cloud/SaaS evaluations. This document is intended to give a broad overview of one vendor’s security
8/13/2010 11:34:00 AM
Protecting Critical Data
The first step in developing a tiered data storage strategy is to examine the types of information you store and the time required to restore the different data classes to full operation in the event of a disaster. Learn how in this white paper from Stonefly.
: Protecting Critical Data Protecting Critical Data Source: Stonefly Document Type: White Paper Description: The first step in developing a tiered data storage strategy is to examine the types of information you store and the time required to restore the different data classes to full operation in the event of a disaster. Learn how in this white paper from Stonefly. Protecting Critical Data style= border-width:0px; /> comments powered by Disqus Related Topics: Data Warehousing, Database
10/19/2011 6:48:00 PM
Data Mining: The Brains Behind eCRM
Data mining has emerged from obscure beginnings in artificial intelligence to become a viable and increasingly popular tool for putting data to work. Data mining is a set of techniques for automating the exploration of data and uncovering hidden truths.
: (e.g., cell phones and accessories) are purchased by certain age groups. Supervised learning can create a model that records these known truths about customer buying habits which can then be used to qualify buying data that does not conform exactly to these truths. This can help the marketers adjust their strategy accordingly. For many companies the benefits of data mining are first realized in the area of marketing. The process of collecting and segmenting customer data for effective marketing campaign
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