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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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 three vs of big data


In-memory Computing: Lifting the Burden of Big Data
Business data is growing at an unprecedented speed, and organizations of all sizes, across all industries, have to face the challenge of scaling up their data

three vs of big data  address two of the three Vs of big data.

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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

Field Service Management (FSM)

Field service management (FSM) software is a set of functionalities for organizations or departments within organizations that have as main focus the intallation, maintanance, reparing, and meter reading for industries relying heaviling on equipment. FSM workers require functionality for customer engagement management, service and asset management as well as workforce management. Since most activities in FSM take place outside of the office, mobility is a big component of the a FSM software solutions. Typically, FSM software is not used as a stand-alone solution, as it needs to integrate with Financials, ERP, CRM and EAM to ensure accurate data exchange. Even if its main purpose is to maintain and repair equipment, it can also be used to gather customer satisfaction and equipment performance feedback. To allocate human resources efficiently, workforce management is an integral part of an FSM system. 

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Documents related to » three vs of big data

PDM vs. PLM: A Matrix View


Two recent blog posts by Oleg Shilovitsky (PDM vs. PLM: A Data Perspective and PDM vs. PLM: A Process Perspective) got me wondering about what a true product lifecycle management (PLM) system actually is. In his posts, Shilovitsky discussed the differences between product data management (PDM) and PLM from the perspective of data (scope and control of the data) and process (coverage of

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Challenges of the Future: The Rebirth of Small Independent Retail in America


By any measure, retailers are overwhelming small businesses. More than 95 percent of all retailers have only one store. Almost 90 percent have sales less than $2.5 million (USD), and more than 98 percent have fewer than 100 employees. To compete, small businesses need to be innovative, and understand both personalization and value, and how to execute best practices to build success.

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Resurrection, Vitality And Perseverance Of Former ERP 'Goners' Part Three: Market Impact


By being back from edge of the cliff, on a comeback trail under rejuvenated management, and with pruned but also more viable product sets, these vendors have become de facto trend setters as once high-flying and almost deceased and then again rebounding mid-market ERP vendors.

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Four Critical Success Factors to Cleansing Data


Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology.

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Data Quality Strategy: A Step-by-Step Approach


To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality.

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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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Thinking Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio


Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the traditional ways to store and analyze large amounts of data are being replaced with new technologies and methodologies to manage the new volume, complexity, and analysis requirements. These include new ways of developing data warehousing, the

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The New Era of Mobile Intelligence: the Convergence of Mobile Computing and Business Intelligence


Computing is entering its fifth generation with desktop Internet applications giving way to a new generation of mobile Internet applications. As consumers capitalize on the power of mobile devices, the same transformation is occurring in business. Learn how the convergence of business information and analytics with mobile technology is empowering business people in a way that was never possible—until now.

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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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A Solution to Data Capture and Data Processing Challenges


Organizations are relying more and more on customer information to drive business processes. You probably spend a lot of time trying to make sure you get the right information to the right people at the right time—but is your data capture process as efficient as it could be? Learn about the issues surrounding data capture and data processing, and about a solution designed to help you address specific processing problems.

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