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Documents related to » data flow brian steel


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 FLOW BRIAN STEEL: 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 FLOW BRIAN STEEL: 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 FLOW BRIAN STEEL: 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

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.

DATA FLOW BRIAN STEEL: 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 improve data constancy and accuracy,
6/1/2009 5:10:00 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 FLOW BRIAN STEEL: 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

Data Storage in the Cloud—Can you Afford Not To?
Storing data in the cloud using Riverbed Technology’s Whitewater cloud storage gateway overcomes a serious challenge for IT departments: how to manage the vast, and ever-growing, amount of data that must be protected. This paper makes the business case for cloud storage, outlining where capital and operational costs can be eliminated or avoided by using the cloud for backup and archive storage.

DATA FLOW BRIAN STEEL: Data Storage in the Cloud—Can you Afford Not To? Data Storage in the Cloud—Can you Afford Not To? Source: Riverbed Technology Document Type: White Paper Description: Storing data in the cloud using Riverbed Technology’s Whitewater cloud storage gateway overcomes a serious challenge for IT departments: how to manage the vast, and ever-growing, amount of data that must be protected. This paper makes the business case for cloud storage, outlining where capital and operational costs can be eliminated or
7/12/2011 2:19:00 PM

New Data Protection Strategies
One of the greatest challenges facing organizations is protecting corporate data. The issues that complicate data protection are compounded by increasing demand for data capacity, and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment, which impact infrastructure. IT organizations must meet these demands while maintaining flat budgets. Find out how.

DATA FLOW BRIAN STEEL: New Data Protection Strategies New Data Protection Strategies Source: IBM Document Type: White Paper Description: One of the greatest challenges facing organizations is protecting corporate data. The issues that complicate data protection are compounded by increasing demand for data capacity, and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment, which impact infrastructure. IT organizations must meet these demands while maintaining
4/23/2010 5:47:00 PM

Data Mart Calculator
Need a model to help calculate an estimate of manpower needs by role, timeline, and labor cost to build a data mart based on user-supplied variables? Here’s a calculator that provides two estimates. The first is based on using the traditional “develop by committee,” and the second on developing the same data mart at the developmental level. The model needs minimal input and can be changed to fit your needs. Find out more.

DATA FLOW BRIAN STEEL: Data Mart Calculator Data Mart Calculator Source: Glenridge Solutions LLC Document Type: White Paper Description: Need a model to help calculate an estimate of manpower needs by role, timeline, and labor cost to build a data mart based on user-supplied variables? Here’s a calculator that provides two estimates. The first is based on using the traditional “develop by committee,” and the second on developing the same data mart at the developmental level. The model needs minimal input and can be
5/22/2009 11:18:00 AM

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.

DATA FLOW BRIAN STEEL: Data Mining: The Brains Behind eCRM Data Mining: The Brains Behind eCRM Steve McVey - November 6, 2000 Read Comments S. McVey - November 6, 2000 Introduction While customer relationship management (CRM) gets a lot of attention, one important component is often overlooked - how a company effectively uses its existing customer data to improve customer loyalty while also identifying new potential buyers of products and services. Data mining has emerged from obscure beginnings in artificial intelligence to
11/6/2000

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.

DATA FLOW BRIAN STEEL: 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

Curing the Data Integration Migraine
The potential value of centralized data integration is enormous. Once implemented, integration systems promise to deliver more accurate and higher quality data. However, for those who venture into the world of implementation, the promise rarely matches the reality. Avoiding the “data integration migraine” requires careful planning to reduce the risks associated with data relationship, transformation, and map discovery.

DATA FLOW BRIAN STEEL: Curing the Data Integration Migraine Curing the Data Integration Migraine Source: Exeros Document Type: White Paper Description: The potential value of centralized data integration is enormous. Once implemented, integration systems promise to deliver more accurate and higher quality data. However, for those who venture into the world of implementation, the promise rarely matches the reality. Avoiding the “data integration migraine” requires careful planning to reduce the risks associated with data
10/27/2006 4:30:00 PM


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