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Documents related to » what is data dictionary


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.

WHAT IS DATA DICTIONARY: 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

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.

WHAT IS DATA DICTIONARY: one time. No matter what data quality solution is ultimately chosen, at the very minimum it should accomplish the following: Trap inaccurate addresses before they enter your database. Process existing records to flag undeliverable mailing addresses for correction or deletion. Update current records when information changes (address change, etc). Enhance records by appending related mailing information to the record, (ie. ZIP + 4® codes and Carrier Route) for faster processing and discounts. Standardize
9/9/2009 2:36:00 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.

WHAT IS DATA DICTIONARY: clean....or clean enough...right? Wrong! What is dirty data? Dirty data occurs when reality is different than the data captured and stored, therefore it is dirty. It can also be described as anomalies in the data values that renders a wrong representation in: Sales order processing and reporting Manufacturing and logistics Administration, e.g keep track of employees and customers Business processes, e.g. using customer address lists for direct mailings, using unique item identification for processing
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.

WHAT IS DATA DICTIONARY: to be listed, and what is the relationship with the other fields, such as SKU code? At the very least, these rules rest in the minds of the marketing specialists, also known as subject matter experts (SMEs) . SMEs need to compose the rules, agree on them, and then load them into whatever tool is used, be it a commercial profiling tool or custom SQL code that is used to explore the data. Probably the most important part of an assessment, other than the initial rules gathering, is the reporting of the
6/1/2009 5:02:00 PM

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.

WHAT IS DATA DICTIONARY: 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. A Guide to Intelligent Data Auditing style= border-width:0px; />   comments powered by Disqus Related Topics:   Business Intelligence and Data Management,   Business Intelligence (BI),   Statistical Data Analysis,   Regulatory and Compliance,   Security,   Access Control,   Network Security Monitoring,   Encryption and Key Management (PKI)
3/19/2008 6:06:00 PM

A Road Map to Data Migration Success
The key to a successful data migration: read a Road Map to Data Migration Success, a SAP white paper. Many significant business initiatives and large IT projects depend upon a successful data migration. But when migrated data is transformed for new uses, project teams encounter some very specific management and technical challenges. Minimizing the risk of these tricky migrations requires effective planning and scoping. Read up on the issues unique to data migration projects, and find out how to best approach them.

WHAT IS DATA DICTIONARY: or months. Current Architecture What is your current architecture? Do you have data quality and data integration tools needed to accomplish a data conversion? Is there a database or server available to host your data and code? When and how will you test the full conversion to ensure that your performance will meet the schedule needs? The team should evaluate what servers, databases, and software tools are available to host a migration project. Leverage what you can and do not reinvent the wheel.
1/25/2010 11:36:00 AM

Customer Data Integration: A Primer
Customer data integration (CDI) involves consolidation of customer information for a centralized view of the customer experience. Implementing CDI within a customer relationship management initiative can help provide organizations with a successful framework to manage data on a continuous basis.

WHAT IS DATA DICTIONARY: of the truth is what organizations need to implement a successful CDI initiative. Data profiling identifies statistics about the data available in existing databases. Two main aspects of data profiling that are essential for a successful CDI implementation are interdependency and redundancy profiling. Checking for interdependency among tables within different databases across the organization will create similar database structures. For example, a customer number should be attached to the customer table
9/11/2009

Managing the Tidal Wave of Data
Despite the slowing economy, data growth continues due to the digitization of infrastructures, the need to keep more copies of data for longer periods, and the rapid increase in distributed data sources. This data growth creates a wide range of management challenges. Discover solutions that can help your company maximize its storage environment and reduce costs while improving service and managing risks.

WHAT IS DATA DICTIONARY: 2013 Market Survey Report: What Organizations Want in Human Capital Management (HCM) Software How to Navigate Today’s Supply Chain Challenges Analyst Reports: Pricing and Requirements Profile Acronym-Related White Papers: Business Intelligence (BI) |  Customer Relationship Management (CRM) |  Enterprise Resource Planning (ERP) |  Human Capital Management (HCM) |  Information Technology (IT) |  Key Performance Indicators (KPIs) |  Return on Investment (ROI) |  Software as a Service (SaaS) | 
4/29/2010 4:04:00 PM

Data Migration Best Practices
Large-scale data migrations can be challenging, but with the appropriate planning—and through careful execution of that plan—organizations can greatly reduce the risks and costs associated with these projects. This paper offers a handy checklist of issues to consider before, during, and after migration.

WHAT IS DATA DICTIONARY: data migration, data migration best practices, Globanet, migration software, data migration compliance.
8/8/2013 1:47:00 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.

WHAT IS DATA DICTIONARY: 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 Quality Strategy: A Step-by-Step Approach style= border-width:0px; />   comments powered by Disqus Related Topics:   Data Cleansing,   Conversion,   or Modeling,   Data Mining,   Data Warehouse,   Database Management System (DBMS),   File Management,   Dashboard,   Search Analytics Related Keywords:   data quality,  
3/16/2011 2:03:00 PM

Metagenix Reverse Engineers Data Into Information
Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries of the existing legacy systems (which are often incorrect). Other unique Metagenix approaches include an

WHAT IS DATA DICTIONARY: consolidation efforts to determine what type of solution is appropriate. Many market surveys have revealed that one of the largest hidden costs in these type of projects is incorrect or impure data, so any effort to automate the process of uncovering problems sooner rather than later (typically at the worst possible time, which is when the actual target data load is attempted) will yield significant cost savings to the company.
2/15/2001


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