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Documents related to » data analyses


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.

DATA ANALYSES: 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.

DATA ANALYSES: 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 Cleansing Data
1/14/2006 9:29:00 AM

Scalable Data Quality: A Seven-step Plan for Any Size Organization
Scalable Data Quality: a Seven-step Plan for Any Size Organization. Read IT Reports In Relation To Data Quality. Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

DATA ANALYSES: Scalable Data Quality: A Seven-step Plan for Any Size Organization Scalable Data Quality: A Seven-step Plan for Any Size Organization Source: Melissa Data Document Type: White Paper Description: Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor
9/9/2009 2:36:00 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.

DATA ANALYSES: Oracle Database 11g for Data Warehousing and Business Intelligence Oracle Database 11g for Data Warehousing and Business Intelligence Source: Oracle Document Type: White Paper Description: 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
4/20/2009 3:11:00 PM

The Path to Healthy Data Governance
TEC research analyst Jorge Garcia explains why for a data governance initiative to be successful, it must be understood as a key business driver, not merely a technological enhancement. Many companies are finally treating their data with all the necessary data quality processes, but they also need to align their data with a more complex corporate view. A framework of policies concerning its management and usage will help exploit the data’s usefulness. TEC research analyst Jorge Garcia explains why for a data governance initiative to be successful, it must be understood as a key business driver, not merely a technological enhancement.

DATA ANALYSES: The Path to Healthy Data Governance The Path to Healthy Data Governance Jorge García - October 13, 2011 Read Comments This article is based on the presentation, “From Data Quality to Data Governance,” by Jorge García, given at ComputerWorld Technology Insights in Toronto, Canada, on October 4, 2011. Modern organizations recognize that data volumes are increasing. More importantly, they have come to realize that the complexity of processing this data has also grown in exponential ways, and it’s
10/14/2011 10:12:00 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.

DATA ANALYSES: 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

Information Life Cycle Management for Business Data
Information Life Cycle Management for Business Data. Find RFP Templates and Other Solutions to Define Your Acquisition In Relation To Information Life Cycle Management. While companies have long seen their stores of data as valuable corporate assets, how they manage those stores varies enormously. Today, however, new government regulations require that companies retain and control information for long periods of time. Find out what IT managers are doing to meet these new regulatory requirements, and learn about solutions for storing vast quantities of data for the lowest possible cost.

DATA ANALYSES: Cycle Management for Business Data Information Life Cycle Management for Business Data Source: Oracle Document Type: White Paper Description: While companies have long seen their stores of data as valuable corporate assets, how they manage those stores varies enormously. Today, however, new government regulations require that companies retain and control information for long periods of time. Find out what IT managers are doing to meet these new regulatory requirements, and learn about solutions for
4/20/2009 3:12:00 PM

Six Misconceptions about Data Migration
A truly successful data migration project involves not only an understanding of how to migrate the data from a technical standpoint, but an understanding of how that data will be used and its importance to the operation of the enterprise.

DATA ANALYSES: Six Misconceptions about Data Migration Six Misconceptions about Data Migration Norma L. Davis - June 23, 2008 Read Comments Originally published February 4th, 2008 As organizations embark on a system implementation, one of the most important and riskiest activities is data migration—the movement of data from the old legacy system to the new system. While the selection, implementation, and operation of an enterprise application for enterprise resource planning (ERP), enterprise asset management (EAM),
6/23/2008

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.

DATA ANALYSES: 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

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.

DATA ANALYSES: 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

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.

DATA ANALYSES: 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
11/3/2005


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