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Merchandising Systems
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...
 

 manage data


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 :

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Core PLM--Product Data Management - Discrete RFI/RFP Template

Product Data Management (PDM), Engineering Change Order and Technology Transfer, Design Collaboration, Process and Project Management, Product Technology Get this template

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Merchandising Systems
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...

Documents related to » manage data

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 extract useful information, is harder still. Even so, this data deluge has great potential for good—as long as consumers, companies, and governments make the right choices about when to restrict the flow of data, and when to encourage it. Find out more.

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 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, 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.

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

Logs: Data Warehouse Style


Once a revolutionary concept, data warehouses are now the status quo—enabling IT professionals to manage and report on data originating from diverse sources. But where does log data fit in? Historically, log data was reported on through slow legacy applications. But with today’s log data warehouse solutions, data is centralized, allowing users to analyze and report on it with unparalleled speed and efficiency.

manage data  quo—enabling IT professionals to manage and report on data originating from diverse sources. But where does log data fit in? Historically, log data was reported on through slow legacy applications. But with today’s log data warehouse solutions, data is centralized, allowing users to analyze and report on it with unparalleled speed and efficiency. Read More

About Big Data


There may not be a consensus with respect to just how big "big data" is, but not many people will disagree that managing these huge amounts of data represents a challenge. TEC research analyst Jorge Garcia discusses the key issues surrounding big data, the different ways to manage it, and the major vendors offering big data solutions.

manage data  and being able to manage it performing data discovery tasks, allowing you to build test scenarios, which is extremely important for building better analytic solutions and improving existing ones, as well as performing analysis on the go There’s also an economic angle to the big data hype. A corporate data warehouse can rapidly become expensive as the data volume increases. Scaling a data warehouse can be a burden when you’re dealing with such volumes. Meanwhile, some big data providers can produce Read More

Best Practices for a Data Warehouse on Oracle Database 11g


Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW performs and scales well, you need to get three things right: the hardware configuration, the data model, and the data loading process. Learn how designing these three things correctly can help you scale your EDW without constantly tuning or tweaking the system.

manage data  helping customers like you manage your business systems and information with reliable, secure, and integrated technologies. Source : Oracle Resources Related to Data Warehouse : Data Warehouse (Wikipedia) Best Practices for a Data Warehouse on Oracle Database 11g Data Warehouse is also known as : Data Warehouse , Data Warehouse Architecture , Data Warehouse Concepts , Data Warehousing Information Center , Data Integration Paper , Data Warehouse Software , Data Warehousing Analysis , Data Warehouse Read More

Data Integration: Creating a Trustworthy Data Foundation for Business Intelligence


Organizations combine their historical data with current data from operational systems to satisfy business intelligence analysis and government reporting requirements. This paper discusses the importance of data integration and helps you identify key challenges of integrating data. It also provides an overview of data warehousing and its variations, as well as summarizes the benefits and approaches to integrating data.

manage data  Integration: Creating a Trustworthy Data Foundation for Business Intelligence Organizations combine their historical data with current data from operational systems to satisfy business intelligence analysis and government reporting requirements. This paper discusses the importance of data integration and helps you identify key challenges of integrating data. It also provides an overview of data warehousing and its variations, as well as summarizes the benefits and approaches to integrating data. Read More

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.

manage data  years of database technology management experience and data warehouse design expertise, and has published 36 books and more than 350 articles in major computer journals. His books have been translated into nine languages. He is known globally for his seminars on developing data warehouses and has been a keynote speaker for every major computing association. Before founding Pine Cone Systems, Bill was a co-founder of Prism Solutions, Inc. Ralph Kimball Ralph Kimball was co-inventor of the Xerox Star Read More

Microsoft Goes Their Own Way with Data Warehousing Alliance 2000


Microsoft Corp. (Nasdaq: MSFT) today announced that 47 applications and tools from 39 vendors throughout the industry have qualified for Microsoft« Data Warehousing Alliance 2000. Alliance members and partners are committed to delivering tools and applications based on the Microsoft Data Warehousing Framework 2000, an open architecture based on the open standards and services built into the Windows« 2000 operating system, Microsoft SQL Server 7.0 and Office 2000.

manage data  stewardship of the Object Management Group (OMG), which has over 800 members. OIM is a standard developed by Microsoft and turned over to the MetaData Council (MDC) which has close to 50 members. For more information on the dueling standards bodies see Is There Finally A Metadata Exchange Standard on the Horizon? , ( http://technologyevaluation.com/news_analysis/09-99/NA_DW_MFR_9_28_99_1.asp ,September 28, 1999). The alliance criteria require compliance with OLE DB for data access and the Open Read More

MSI Data




manage data  Data 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 technology selection and access to historical 'legacy' data are also discussed.

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

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.

manage data  to configure and to manage data relationship consolidation, rules and metadata maintenance for relationship unification across all organizational sources, and exception-handling capabilities. The third module, Siperian Activity Manager (AM) , allows organizations to create relevant customer views that drive business actions based on the transactional data captured in the hub and distributed via analytical and operational activities. Oracle 's Siebel Customer Data Integration is comprised of three Read More

Big Data Comes of Age: Shifting to a Real-time Data Platform


New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose-built platforms more capable of meeting the real-time needs of more demanding end users and the opportunities presented by big data. Read this white paper to learn more about the significant strategy shifts underway to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support big data and the real-time needs of innovative companies.

manage data  limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose-built platforms more capable of meeting the real-time needs of more demanding end users and the opportunities presented by big data. Read this white paper to learn more about the significant strategy shifts underway to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support big data and the real-time needs of innovative companies. Read More

A Solution to Data Capture and Data Processing Challenges


Organizations are relying more and more on customer information to drive business processes. You probably spend a lot of time trying to make sure you get the right information to the right people at the right time—but is your data capture process as efficient as it could be? Learn about the issues surrounding data capture and data processing, and about a solution designed to help you address specific processing problems.

manage data  Solution to Data Capture and Data Processing Challenges Organizations are relying more and more on customer information to drive business processes. You probably spend a lot of time trying to make sure you get the right information to the right people at the right time—but is your data capture process as efficient as it could be? Learn about the issues surrounding data capture and data processing, and about a solution designed to help you address specific processing problems. Read More