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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.
: company s database is its most important asset. It is a collection of information on customers, suppliers, partners, employees, products, inventory, locations, and more. This data is the foundation on which your business operations and decisions are made; it is used in everything from booking sales, analyzing summary reports, managing inventory, generating invoices and forecasting. To be of greatest value, this data needs to be up-to-date, relevant, consistent and accurate — only then can it be managed
9/9/2009 2:32:00 PM
Oracle Database 11g for Data Warehousing and Business Intelligence
Oracle Database 11g for Data Warehousing and Business Intelligence. Find RFP Templates and Other Solutions to Define Your Project In Relation To Oracle Database, Data Warehousing and Business Intelligence. Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data.
: and different business problems. Most data mining algorithms can be broadly separated into supervised learning and unsupervised learning data mining techniques. Supervised learning requires the data analyst to identify a target attribute or dependent variable (for example, customers who bought a specific product). The supervised-learning technique then sifts through data trying to find patterns and relationships between other attributes and the target attribute (for example, the characteristics that
4/20/2009 3:11:00 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.
: 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 data, its financial data. Even its
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.
: of Web services. Today, most commercial data quality software packages support Web services to some level. With Web services, a corporate IT group can install a centralized data quality server, such as Business Objects Data Services, and publish the data quality functionality to departments and business units within the enterprise. IT does not need to install its own software. The elegance of this approach is that the marketing department, for example, can see and leverage the customer processing rules
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Siemens’ JT Data Format Gets a Nod from ISO » The TEC Blog
these different formats are mostly complementary to JT. Some observers wonder what might happen with the upcoming JT 2.0 version, which will supposedly not be based on the Parasolid 3D modeling kernel. Will that mean yet another disruption and migration of data exchange standards? To that end, nothing is changing with regard to Siemens’ support for JT or commitment to update the aforementioned JT File Format Reference. Siemens will continue to work with the JT Open organization and keep JT useful for
: 3d visualization, CAD, JT ISO standard, plm, Siemens JT, Siemens JT data format, Siemens PLM Software, TEC, Technology Evaluation, Technology Evaluation Centers, Technology Evaluation Centers Inc., blog, analyst, enterprise software, decision support.
Addressing the Complexities of Remote Data Protection
Expert solutions for adressing the complexities of remote data protection in your enterprise.Experience data recovery solutions. Free white paper! As companies expand operations into new markets, the percentage of total corporate data in remote offices is increasing. Remote offices have unique backup and recovery requirements in order to support a wide range of applications, and to protect against a wide range of risk factors. Discover solutions that help organizations protect remote data and offer extensive data protection and recovery solutions for remote offices.
: many cases, only the most essential data can be recovered. Successful recovery from tape backups also assumes that all backup operations were completed successfully, with no tape errors, labeling errors or tape loss. Highlights The IBM Tivoli Storage Manager FastBack family of products offers an extensive and cost-effective data protection and recovery solution for remote offices. Most tape backup failures are due to tape errors, which can be caused by mishandling, excessive heat and humidity, and
4/23/2010 1:16:00 PM
The Bottom Line on Bad Customer Data
You can blame your sales people all you want, but if the lead data is bad, they’re not going to bring in business. You can blame your product managers for ineffective promotions, but if the target lists are redundant, the pitches fall on deaf ears. You can blame your customer service representatives for low satisfaction scores, but if customer data is missing, then no wonder the complaint resolution pipeline is backed up. Think it’s your customer resource management (CRM) system? Think again. It’s bad data, and it’s costing you millions. Request your copy of The Bottom Line on Bad Customer Data that delivers detailed advice from Jill Dyche, partner and co-founder of Baseline Consulting, about what you can do to address the impact of bad data on your company. The report gives you insight into how bad data is impacting your company and what you can do about it. How to identify where the bad data is and quantify its impact, and different approaches to determine the sources and causes of bad data are all offered in this paper.
: The Bottom Line on Bad Customer Data The Bottom Line on Bad Customer Data Source: Baseline Consulting Document Type: White Paper Description: You can blame your sales people all you want, but if the lead data is bad, they’re not going to bring in business. You can blame your product managers for ineffective promotions, but if the target lists are redundant, the pitches fall on deaf ears. You can blame your customer service representatives for low satisfaction scores, but if customer data is missing,
5/25/2005 10:37:00 AM
A Definition of Data Warehousing
There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.
: It is often the most time-consuming and costly effort in the data warehousing project, and is performed with software products known as ETL (Extract/Transform/Load) tools. There are currently over 50 ETL tools on the market. The data acquisition phase can cost millions of dollars and take months or even years to complete. Data acquisition is then an ongoing, scheduled process, which is executed to keep the warehouse current to a pre-determined period in time, (i.e. the warehouse is refreshed monthly).
Data Quality: A Survival Guide for Marketing
Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more.
: Data Quality: A Survival Guide for Marketing Data Quality: A Survival Guide for Marketing Source: SAP Document Type: White Paper Description: Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information
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The Why of Data Collection
Data collection systems work; however, they require a investment in technology. Before the investment can be justified, we need to understand why a data collection system may be preferable to people with clipboards.
: The Why of Data Collection The Why of Data Collection Olin Thompson - November 3, 2005 Read Comments Introduction Data collection systems work. However, they mean an investment in technology. Before we can justify that investment, we need to understand why we may want to use a data collection system in place of people with clipboards. What is data collection? In a general sense, it is the manual or automated acquisition of data. That definition has evolved to mean various automated methods of data
The Data Warehouse RFP
If you don’t have a data warehouse, you’re probably considering drafting a request for proposal (RFP) to screen vendors and ensure that your receive satisfactory information about the features of various hardware and software and their price. Writing an effective RFP that is well structured and includes different metrics will ensure that you receive the information you need and will make you look good.
: The Data Warehouse RFP The Data Warehouse RFP Source: Baseline Consulting Document Type: White Paper Description: If you don’t have a data warehouse, you’re probably considering drafting a request for proposal (RFP) to screen vendors and ensure that your receive satisfactory information about the features of various hardware and software and their price. Writing an effective RFP that is well structured and includes different metrics will ensure that you receive the information you need and will make
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