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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 analysis modeling


Role of In-memory Analytics in Big Data Analysis
Organizations today need to handle and manage increasingly large volumes of data in various formats and coming from disparate sources. Though the benefits to be

data analysis modeling  In-memory Analytics in Big Data Analysis In a previous article , I discussed big data and the need for a big data solution to handle, manage, and derive valuable insight from large volumes of data in various formats and coming from disparate sources. Here I discuss the role that in-memory technologies plays in big data analysis and the potential of this technology to change the business intelligence (BI) landscape and the industry technology space. But first we need to understand the basics.   A Basic

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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.

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Recruitment and Staffing

Recruitment and Staffing functionality allows the user to select and hire the right people with the right skill sets, as well as track the information regarding their recruitment for later analysis. It covers criteria such as Organization Structures and Modeling, Corporate Branding, Sourcing, Applicant Tracking, Assessment and Selection, Governance and Compliance, Vendor Management Systems (VMS) Portal, Recruitment Analytics and Reporting, and Product Technology. 

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Documents related to » data analysis modeling

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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The Truth about Data Mining


It is now imperative that businesses be prudent. With rising volumes of data, traditional analytical techniques may not be able to discover valuable data. Consequently, data mining technology becomes important. Here is a framework to help understand the data mining process.

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The Post-implementation Agility of Enterprise Systems: An Analysis


Very few companies really run standard software applications as "standard" products, since every business changes constantly. In fact, these companies have found that the agility they wish to attain is in direct conflict with the rigidity of their enterprise systems.

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Data Discovery Applications


Over the last five years, the business intelligence (BI) space has undergone a huge transformation. The business user community has lobbied for data analysis tools that are easier to use, agile to deploy, and less expensive, thus encouraging the emergence of new products and vendors. These conditions, along with the acquisitions and mergers of software companies looking to offer new applications

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Next-generation Data Auditing for Data Breach Protection and Risk Mitigation


Data breaches and leaks are on the rise—and the consequences, from theft of identity or intellectual property, can seriously compromise a company’s reputation. Stolen laptops, hacking, exposed e-mail, insider theft, and other causes of data loss can plague your company. How can you detect (and respond!) to breaches and protect your data center? Learn about the functions and benefits of an automated data auditing system.

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Types of Prefabricated Modular Data Centers


Data center systems or subsystems that are pre-assembled in a factory are often described with terms like prefabricated, containerized, modular, skid-based, pod-based, mobile, portable, self-contained, all-in-one, and more. There are, however, important distinctions between the various types of factory-built building blocks on the market. This paper proposes standard terminology for categorizing the types of prefabricated modular data centers, defines and compares their key attributes, and provides a framework for choosing the best approach(es) based on business requirements.

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10 Errors to Avoid When Building a Data Center


In the white paper ten errors to avoid when commissioning a data center, find out which mistakes to avoid when you're going through the data center...

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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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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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A Roadmap to Data Migration Success


Many large business initiatives and information technology (IT) projects depend upon the successful migration of data—from a legacy source, or multiple sources, to a new target database. Effective planning and scoping can help you address the associated challenges and minimize risk for errors. This paper provides insights into what issues are unique to data migration projects and to offer advice on how to best approach them.

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