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Six Steps to Manage Data Quality with SQL Server Integration Services
Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business

manage data  Steps to Manage Data Quality with SQL Server Integration Services Melissa Data's Data Quality Suite operates like a data quality firewall ' instantly verifying, cleaning, and standardizing your contact data at point of entry, before it enters your database. Source : Melissa Data Resources Related to Six Steps to Manage Data Quality with SQL Server Integration Services : Data quality (Wikipedia) Six Steps to Manage Data Quality with SQL Server Integration Services Data Quality is also known as : Read More
Merchandising Systems
Merchandising systems are the enterprise back and front-office software solutions upon which the majority of retailers rely to manage and support their daily tasks. These systems typically record p...
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Documents related to » manage data


Information Life Cycle Management for Business Data
While companies have long seen their stores of data as valuable corporate assets, how they manage those stores varies enormously. Today, however, new government

manage data  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. Read More
Captured by Data
The benefits case for enterprise asset management (EAM) has been used to justify huge sums in EAM investment. But to understand this reasoning, it is necessary

manage data  determining how best to manage physical assets is based around data capture, then we are creating an environment that runs counter to the principles of responsible asset stewardship in the twenty-first century. The underlying theories of maintenance and of reliability are based on the theory of probability and on the properties of distribution functions that have been found to occur frequently, and which play a role in the prediction of survival characteristics. (Resnikov, H. L. 1978. Mathematical Read More
The Value of Big Data
As the use of big data grows, the need for data management will also grow. Many organizations already struggle to manage existing data. Big data adds complexity

manage data  organizations already struggle to manage existing data. Big data adds complexity, which will only increase the challenge. This white paper looks at what big data is, the value of big data, and new data management capabilities and processes, required to capture the promised long-term value. Read More
Data, Data Everywhere: A Special Report on Managing Information
The quantity of information in the world is soaring. Merely keeping up with, and storing new information is difficult enough. Analyzing it, to spot patterns and

manage data  play as organisations that manage data well are favoured over those that do not. Greater transparency in these three areas would improve security and give people more control over their data without the need for intricate regulation that could stifle innovation. After all, the process of learning to cope with the data deluge, and working out how best to tap it, has only just begun. Data, data everywhere Information has gone from scarce to superabundant. That brings huge new benefits, says Kenneth Read More
The Necessity of Data Warehousing
An explanation of the origins of data warehousing and why it is a crucial technology that allows businesses to gain competitive advantage. Issues regarding

manage data  the concept of metadata management. Metadata is data about data, such as table and column names, and datatypes. Managing metadata makes it possible to understand relationships between data elements and assists in the mapping of source to target fields. (For more information of Metadata see Metadata Standards in the Marketplace ) Next came the creation of Extract/Transform/Load (ETL) tools, which made use of the metadata to get the information from the source systems into the data warehouse. Additional Read More
A Road Map to Data Migration Success
Many significant business initiatives and large IT projects depend upon a successful data migration. But when migrated data is transformed for new uses, project

manage data  within the service to manage multiple data versions, sources, and status values. Your team can also use the staging area to created needed reference data for the target system. A sizing estimate for the staging database, which includes any needed replication required to support both migration code development and application A code life-cycle management plan Performance test requirements Backup and recovery planning Scalable connectivity requirements and approaches (for example, how will you extract and Read More
Master Data Management: Extracting Value from Your Most Important Intangible Asset
In a 2006 SAP survey, 93 percent of respondents experienced data management issues during their most recent projects. The problem: many organizations believe

manage data  The best way to manage data depends on the unique characteristics of the data. For instance, master data is reference data about key entities within the organization, such as customers, products, employees, and so on. Unlike transactional data ' for example, a sales order ' master data does not change frequently and typically involves a relatively small number of key attributes. Master data management ' developing a central corporate repository of standardized information ' can be a vital contributor to Read More
Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence

manage data  processes for master data management. Data through the Looking Glass Data quality issues can certainly be minimized in data sources. However, data warehouses are conglomerations of several data sources, often combining unstructured and structured data, and even including data external to the enterprise. While each application system may be self-consistent, it is important to homogenize data from multiple sources. In addition, simply constraining data to match business rules is only the first step toward Read More
Flexible Customer Data Integration Solution Adapts to Your Business Needs
Siperian's master data management and customer data integration (CDI) solutions allow organizations to consolidate, manage, and customize customer-related data.

manage data  or single organizations to manage their data quality activities across the organization, harmonizing the customer view across operational systems, and maintaining greater consistency across the organization. This provides the foundational building blocks to create a single view of the customer. The third style, Operational Views , through Activity Manager ( AM ), accesses the reliable master data in the centralized repository, and then aggregates and identifies the associated transactions and Read More
ESG - Riverbed Whitewater: Optimizing Data Protection to the Cloud
Riverbed Whitewater leverages WAN optimization technology to provide a complete data protection service to the cloud. The appliance-based solution is designed

manage data  - Riverbed Whitewater: Optimizing Data Protection to the Cloud Riverbed Whitewater leverages WAN optimization technology to provide a complete data protection service to the cloud. The appliance-based solution is designed to integrate seamlessly with existing backup technologies and cloud storage provider APIs. Read this ESG Lab report on hands-on testing of the Riverbed Whitewater appliance for ease of use, cost-effective recoverability, data assurance, and performance and scalability. Read More
Transactional Data: Driving Real-Time Business
A global survey of IT leaders shows that most organizations find it challenging to convert high volumes of fresh transactional data into knowledge that business

manage data  Data: Driving Real-Time Business A global survey of IT leaders shows that most organizations find it challenging to convert high volumes of fresh transactional data into knowledge that business users can efficiently access, understand, and act on. SAP and HP are tackling this challenge head-on. Download this article to learn more. Read More
How Companies Use Data for Competitive Advantage
Find out in Leveling the Playing Field: How Companies Use Data for Competitive Advantage.

manage data  Companies Use Data for Competitive Advantage How Companies Use Data for Competitive Advantage Today, many businesses find they are under siege dealing with an explosion of data. Yet the best performing companies are mastering their data—and using it for competitive advantage. How are they able to accomplish this? What best practices, approaches, and technologies are they employing? Find out in Leveling the Playing Field: How Companies Use Data for Competitive Advantage . In this Economist Business Read More
Meet PCI DSS Compliance Requirements for Test Data with Data Masking
Whether you’re working toward your first or your next payment card industry (PCI) data security standard (DSS) audit, you know compliance is measured on a

manage data  PCI DSS Compliance Requirements for Test Data with Data Masking Whether you’re working toward your first or your next payment card industry (PCI) data security standard (DSS) audit, you know compliance is measured on a sliding scale. But full compliance can’t be achieved with just one policy or technology. Using data masking, a technology that alters sensitive information while preserving realism, production data can be eliminated from testing and development environments. Learn more. Read More

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