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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 migration checklists


Architecture-Centered Information Systems In The Manufacturing Domain - Part V - Applying the Methodology
Architecture bridges the semantic gap between the requirements and software. Application software systems must be architected in order to deal with the current

data migration checklists  hides the differences in data representation and procedure calling mechanisms to enable internetworking between heterogeneous systems. Location - makes the use of physical addresses, including the distinction between local and remote resource usage. Relocation - hides the relocation of a service and its interface from other services and the interfaces bounded by it. Migration - masks the relocation of a service from that service and the services that interact with it. Persistence - masks the deactivation

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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

Linux and Open Source IT Services (Excel Format) RFI/RFP Template

OS and Application Services, Support Services, Consulting, Implementatation, and Reselling, Migration and Version Maintenance Services, Customization Services, Development Services, Security Services, License Support and Applicability, Training and Community Participation, High-Level Market Characteristics 

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Documents related to » data migration checklists

Architecture-Centered Information Systems In The Manufacturing Domain - Part III - Steps in the Architecture Process


Architecture bridges the semantic gap between the requirements and software. Application software systems must be architected in order to deal with the current and future needs of the business organization. Managing software projects using architecture–centered methodologies must be an intentional step in the process of deploying information systems – not an accidental by–product of the software acquisition and integration process.

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Addressing Channels and the Low-End Market


Microsoft announced plans to increase resources and provide new tools and offerings for MBS independent software vendors (ISV) and value-added resellers (VAR). These efforts are designed to accelerate the MBS Group's partner-driven vertical strategy, providing stronger opportunities for partners to align their services and solutions with their customers' specific needs. Microsoft has to be in the small business market, which is a springboard or a feeding ground for more scalable products as these businesses grow over time.

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Job Scheduling Evaluation Checklist


When buying a job scheduling solution, you should gather as much information as possible to make an objective comparison of competitive products’ key capabilities. Seek out customer references, analyst recommendations, and press articles to supplement vendor marketing materials. And to make sure you don’t miss any important evaluation criteria, benchmark the functions of job-scheduling software with a detailed checklist.

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The Evolution of a Real-time Data Warehouse


Real-time data warehouses are common in some organizations. This article reviews the basic concepts of a real-time data warehouse and it will help you determine if your organization needs this type of IT solution.

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Data Management and Business Performance: Part 1-Data


Research for one of my projects led me to ask both software vendors and customers about the factors most important to software users in the selection of a business intelligence (BI) solution. Two topics resounded: the use of BI tools to improve data management and business performance management. Consumers are continuously looking for innovative ways to move, store, and improve the quality of

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Scalable Data Quality: A Seven-step Plan for Any Size Organization


Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

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Data Center Projects: Project Management


In data center design projects, flawed management frequently leads to delays, expense, and frustration. Effective project management requires well-defined responsibilities for every manager, tight coordination among suppliers, well-defined procedures for managing change, and consistent terminology. Learn how enforcing these requirements can help your company achieve an efficient process with a predictable outcome.

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Big Data: Operationalizing the Buzz


Integrating big data initiatives into the fabric of everyday business operations is growing in importance. The types of projects being implemented overwhelmingly favor operational analytics. Operational analytics workloads are the integration of advanced analytics such as customer segmentation, predictive analytics, and graph analysis into operational workflows to provide real-time enhancements to business processes. Read this report to learn more.

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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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2013 Big Data Opportunities Survey


While big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry groups are now leveraging big data. A survey of 304 data managers and professionals was conducted by Unisphere Research in April 2013 to assess the enterprise big data landscape, the types of big data initiatives being invested in by companies today, and big data challenges. Read this report for survey responses and a discussion of the results.

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