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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 definition


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

data definition  a copy of transaction data specifically structured for query and analysis . This definition provides less insight and depth than Mr. Inmon's, but is no less accurate. Data Warehousing: Data warehousing is essentially what you need to do in order to create a data warehouse, and what you do with it. It is the process of creating, populating, and then querying a data warehouse and can involve a number of discrete technologies such as: Source System Identification: In order to build the data warehouse, the

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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

Discrete Manufacturing (ERP)

The simplified definition of enterprise resource planning (ERP) software is a set of applications that automate finance and human resources departments and help manufacturers handle jobs such as order processing and production scheduling. ERP began as a term used to describe a sophisticated and integrated software system used for manufacturing. In its simplest sense, ERP systems create interactive environments designed to help companies manage and analyze the business processes associated with manufacturing goods, such as inventory control, order taking, accounting, and much more. Although this basic definition still holds true for ERP systems, today its definition is expanding. Today’s leading ERP systems group all traditional company management functions (finance, sales, manufacturing, and human resources). Many systems include, with varying degrees of acceptance and skill, solutions that were formerly considered peripheral such as product data management (PDM), warehouse management, manufacturing execution system (MES), and reporting. During the last few years the functional perimeter of ERP systems began an expansion into its adjacent markets, such as supply chain management (SCM), customer relationship management (CRM), business intelligence/data warehousing, and e-business, the focus of this knowledge base is mainly on the traditional ERP realms of finance, materials planning, and human resources. The foundation of any ERP implementation must be a proper exercise of aligning customers'' IT technology with their business strategies, and subsequent software selection. 

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Documents related to » data definition

AspenTech Searching for Definition in FY2000


Founded in 1981 as a developer of computer-aided chemical engineering software, Aspen's growth has resulted in a wide variety of applications for management and execution-level process manufacturing. Hit hard by flagging revenues, Aspen is distilling its product suite down to its most profitable elements in hopes of invigorating its business and capturing a share of the supply chain management market.

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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.

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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 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.

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Unified Data Management: A Collaboration of Data Disciplines and Business Strategies


In most organizations today, data are managed in isolated silos by independent teams using various data management tools for data quality, integration, governance, and so on. In response to this situation, some organizations are adopting unified data management (UDM), a practice that holistically coordinates teams and integrates tools. This report can help your organization plan and execute effective UDM efforts.

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Data Mining with MicroStrategy: Using the BI Platform to Distribute Data Mining and Predictive Analytics to the Masses


Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has been slow due to their lack of business intelligence (BI) functionality, proactive information distribution, robust security, and other necessities. Now there’s an integrated enterprise BI system that can deliver data mining and predictive analysis. Learn more.

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Agile Data Masking: Critical to Data Loss Prevention and Threat Reduction


Over the past several years data loss and data leaks have been a regular part of headline news. This surge in data leak activity has prompted many organizations to reevaluate their exposure to data leaks and institute automated, agile approaches to data masking. Well-implemented data masking secures data delivery and enhances compliance and security while accelerating data management processes.

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Enterprise Data Management: Migration without Migraines


Moving an organization’s critical data from a legacy system promises numerous benefits, but only if the migration is handled correctly. In practice, it takes an understanding of the entire ERP data lifecycle combined with industry-specific experience, knowledge, and skills to drive the process through the required steps accurately, efficiently, and in the right order. Read this white paper to learn more.

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Revamping Data Management: Big Data Proves Catalyst to Updating Data Management Strategies


Data management plays a key role in helping organizations make strategic sense of their data and how to best use it. Organizations with data management maturity have ushered in clear data goals, but many obstacles persist. This white paper reports survey results that help to establish a clear picture of how organizations are capitalizing on data management today, as well as what challenges and opportunities remain.

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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 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.

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