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Software Functionality Revealed in Detail
We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.
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 difference between data warehouse


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

difference between data warehouse  information can make a difference by enabling people to understand complex matters and find creative solutions. Valdis Krebs, a specialist in mapping social interactions, recalls being called in to help with a corporate project that was vastly over budget and behind schedule. He drew up an intricate network map of e-mail traffic that showed distinct clusters, revealing that the teams involved were not talking directly to each other but passing messages via managers. So the company changed its office

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

Warehouse Management Systems (WMS)

A warehouse management system (WMS) should provide database and user-level tools in order for a company to optimize its storage facilities while at the same time providing user level task direction and activity support. The WMS should enable warehouse operators to optimize pick, put-away, and replenishment functions by employing powerful system logic to select the best locations and sequences. 

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Documents related to » difference between data warehouse

Thinking Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio


Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the traditional ways to store and analyze large amounts of data are being replaced with new technologies and methodologies to manage the new volume, complexity, and analysis requirements. These include new ways of developing data warehousing, the

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Optimizing Gross Margin over Continously Cleansed Data


Imperfect product data can erode your gross margin, frustrate both your customers and your employees, and slow new sales opportunities. The proven safeguards are automated data cleansing, systematic management of data processes, and margin optimization. Real dollars can be reclaimed in the supply chain by making certain that every byte of product information is accurate and synchronized, internally and externally.

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Teradata 2013 Partners Conference: Interview with Dan Graham, General Manager for Enterprise Systems, Teradata


BI Analyst Jorge Garcia interviews Dan Graham, General Manager for Enterprise Systems at Teradata, live on the show floor at the 2013 Teradata Partners Conference. Listen and find out how the Aster 6 SNAP framework handles data types that don’t fit easily into rows and columns, how having a JSON data type in the data warehouse helps companies deal with the internet of things, and why for a discovery platform there’s no real difference between beta and production.

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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.

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How to Solve Your Warehouse Woes


Today’s manufacturers and distributors are under immense pressure to ensure their warehouse and supply chain activities are continually operating at peak performance. But before any improvements can be made, they must first develop a warehouse management improvement strategy.

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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.

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Managing Small Data Centers: A Short Guide to Running Secure and Resilient Data Centers for Mid-sized Businesses


To keep your growing business competitive, your data center must be secure, protected against disaster, and available 24 hours a day, 7 days a week. But if managing IT is not your core competence, what are your options? A managed service provider (MSP) can help. Learn about the benefits of outsourcing data center management, and make sure your crucial business applications are always available when you need them.

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The Importance of a Single Product for Data Quality and Data Integration


Companies are fighting a constant battle to integrate business data and content while managing data quality. Data quality serves as the foundation for business intelligence (BI), enterprise resource planning (ERP), and customer relationship management (CRM) projects. Learn more about software that unifies leading data quality and integration solutions—helping your organization to move, transform, and improve its data.

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TCO Analysis of a Traditional Data Center vs. a Scalable, Containerized Data Center


Standardized, scalable, pre-assembled, and integrated data center facility power and cooling modules provide a total cost of ownership (TCO) savings of 30% compared with traditional, built-out data center power and cooling infrastructure. Avoiding overbuilt capacity and scaling the design over time contributes to a significant percentage of the overall savings. This white paper provides a quantitative TCO analysis of the two architectures, and illustrates the key drivers of both the capex and opex savings of the improved architecture.

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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.

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