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


Flow
4DIQ Flow is business analytics software that provides customers simple, powerful, cost-effective solutions that allow them to move quickly from data to

data flow  to move quickly from data to decisions.

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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

HCIMS - Clinical Information System RFI/RFP Template

ADT (admission, discharge, and transfer), Global Requirements, Patient Information, Orders, Plan of Care, Work Plan, Kardex and Summary, Flow Sheets and Vitals, MAR and Medications, Critical Care, L&D Fetal Monitoring, Clinical Record, Reference and Reports, HIPAA (Health Insurance Portability and Accountability Act), Interfaces, Ease of Use, Technical and Support, and Product Technology  

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Documents related to » data flow

Data Pro Accounting Software


Data Pro Accounting Software, Inc., privately owned, is based in St. Petersburg, Florida and was originally incorporated in June of 1985. The goal of the corporation has always been to develop and market a full line of accounting software products for a wide range of market segments, on a broad spectrum of operating systems environments such as DOS, Windows and UNIX.

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The Why of Data Collection


Data collection systems work; however, they require a investment in technology. Before the investment can be justified, we need to understand why a data collection system may be preferable to people with clipboards.

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Data Center Projects: System Planning


System planning is the Achilles’ heel of a data center physical infrastructure project. Planning mistakes can propagate through later deployment phases, resulting in delays, cost overruns, wasted time, and a compromised system. These troubles can be eliminated by viewing system planning as a data flow model, with sequenced tasks that progressively transform and refine data from initial concept to final design. Learn more.

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Data Quality: Cost or Profit?


Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and company policies that set expectations and establish data management procedures, we will explore applications and tools that help reduce the negative impact of poor data quality. Some CRM application providers like Interface Software have definitely taken data quality seriously and are contributing to solving some data quality issues.

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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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Agile Data Masking: Mitigate the Threat of Data Loss Prevention


You may not be as protected from data loss as you think. This infographic looks at some ways in which an enterprise's data can be compromised and vulnerable to security breaches and data loss, and shows how data masking can mean lower security risk and increased defense against data leaks.

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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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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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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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Re-think Data Integration: Delivering Agile BI Systems with Data Virtualization


Read this white paper to learn about a lean form of on-demand data integration technology called data virtualization. Deploying data virtualization results in business intelligence (BI) systems with simpler and more agile architectures that can confront the new challenges much more easily.

All the key concepts of data virtualization are described, including logical tables, importing data sources, data security, caching, and query optimization. Examples are given of application areas of data virtualization for BI, such as virtual data marts, big data analytics, extended data warehouse, and offloading cold data.

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