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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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 data warehouse toolkit


SAS/Warehouse 2.0 Goes Live
SAS Institute has announced the production availability of SAS/Warehouse Administrator software, Version 2.0. This new version provides IT the ability to

data warehouse toolkit  ability to proactively publish data warehouse information and track its usage, plus aggressively manage the process of change in the data warehouse. Data warehouses and data marts have become a vital component of all successful data mining, knowledge management, business portal, e-intelligence, customer relationship management (CRM) and supplier relationship management (SRM) applications today, said Frank Nauta, product manager for SAS/Warehouse Administrator at SAS Institute. Nauta added, Successful

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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 » data warehouse toolkit

Should Your Software Selection Process Have a Proof of Concept? Part Two: Advantages, Disadvantages, and Conclusion


A proof of concept (POC) should be completed as part of the selection process when the risk of project failure is comparatively high. Risk can be measured by two key variables. These variables are complexity of requirements and level of expertise of the selection/implementation team. The more complex the system requirements, the greater the benefit obtained from a POC.

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In-memory Analytics: Leveraging Emerging Technologies for Business Intelligence


Traditional analytic tools run queries against a data warehouse (DW) with user queries being processed against the data stored on relatively slow hard drives. In-memory analytics leverages a significantly more efficient approach where all the data is loaded into memory. This results in dramatic improvements in query response time and end-user experience. Find out how in-memory analytics can help your organization.

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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.

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SAS Institute Shoots for the Two-Stop-Shop with new Release of Warehouse Administrator


SAS Institute, a vendor of integrated data warehousing, decision support and information delivery software, has announced the production availability of SAS/Warehouse Administrator® software, Version 2.1. With an open component-based architecture, improved data access and management capabilities, thin-client interfaces, and other enhancements, it is an important component of the new SAS® software V8.1.

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Data Management Wish List: IT Is Open to Big Changes


Data management, access, and analysis haven’t always received the same amount of consideration, or respect, as the corporate applications that generate and consume the data. In recent years, however, information technology (IT) leaders have focused much more attention on their organizations’ data management platforms and capabilities. Download this white paper to get a better picture of the current data management landscape.

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Warehouse Management Systems: Pie in the Sky or Floating Bakery? Part One: Myths of the Warehouse Management Systems and Implementation


When searching for a warehouse management system (WMS), a number of myths surface. "Huge staff reductions", "quick and easy implementation", and "fast and big" returns on investment are common promises. These combined with the enticing "bells and whistles" of a system can ultimately turn an eager customer into a patient suffering from confusion or at the very least disorientation. Knowing the stories behind the myths and determining what your warehouse needs are can lead to a profitable investment.

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Data Management and Analysis


From a business perspective, the role of data management and analysis is crucial. It is not only a resource for gathering new stores of static information; it is also a resource for acquiring knowledge and supporting the decisions companies need to make in all aspects of economic ventures, including mergers and acquisitions (M&As).

For organizational growth, all requirements and opportunities must be accurately communicated throughout the value chain. All users—from end users to data professionals—must have the most accurate data tools and systems in place to efficiently carry out their daily tasks. Data generation development, data quality, document and content management, and data security management are all examples of data-related functions that provide information in a logical and precise manner.

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The Fast Path to Big Data


Today, most people acknowledge that big data is more than a fad and is a proven model for leveraging existing information sources to make smarter, more immediate decisions that result in better business outcomes. Big data has already been put in use by companies across vertical market segments to improve top- and bottom-line performance. As unstructured data becomes a pervasive source of business intelligence, big data will continue to play a more strategic role in enterprise information technology (IT). Companies that recognize this reality—and that act on it in a technologically, operationally, and economically optimized way—will gain sustainable competitive advantages.

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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.

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Governance from the Ground Up: Launching Your Data Governance Initiative


Although most executives recognize that an organization’s data is corporate asset, few organizations how to manage it as such. The data conversation is changing from philosophical questioning to hard-core tactics for data governance initiatives. This paper describes the components of data governance that will inform the right strategy and give companies a way to determine where and how to begin their data governance journeys.

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