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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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 we analyzed data


About Big Data
There may not be a consensus with respect to just how big

we analyzed data  of handling big data. We can distinguish two major categories in the big data space. Big data management systems are for administering big data volumes. Big Data File and Database Management Systems Product Vendor Commercial Provider of Related Products Aster Database Aster Data (acquired by Teradata ) Ayrris Appistry Cassandra Apache Software Foundation (open source) DataStax Hadoop Apache Software Foundation (open source) Cloudera , Hortonworks , MapR , Microsoft Big Data , IBM InfoSphere BigInsights

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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

ERP for Municipalities

In this model, we assume the municipality controls water, sewers and roads. We assume also that it collects municipal or county taxes and manages many of its own assets such as vehicles and water treatment plant. ERP software for Municipalities nearly always includes Finance, Customer Care and Billing For a complete municipal ERP suite, the following modules are usually included: - Human Resources - Maintenance Management - Asset Management - Project Management ( including Grant Management) - Quality Management - Analytics - Product Technology Of the above, Project and Quality Management modules may not be required for smaller Municipalities.    

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Asset Data for Accurate Lifecycle Management


Among the areas where modern enterprise asset management (EAM) systems provide substantial benefits is the driving out of inefficiencies in business processes. This is why the development of effective maintenance policies is generally the foremost consideration for modern asset managers.

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Leveraging Technology to Maintain a Competitive Edge During Tough Economic Times--A Panel Discussion Analyzed Part Four: RFID Software Issues


Logically, radio frequency identification (RFID) deployment will be a far cry from a minor development project that can be completed in a few months or weeks. It will take months and years to assess how RFID will affect manufacturing and shipping operations and IT systems. It will take time to bring software up to a pilot stage, and after that, years of fine-tuning and IT system development will be needed to fully realize the gains in operational efficiency that the technology promises.

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How Bar Codes Can Optimize Data Recording and Information Analysis


Bar code technology allows users to analyze information to develop more accurate maintenance, personnel, and financial planning. In particular it can hasten the data recording processes in a maintenance system, as seen in its use in the aircraft maintenance process.

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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 gained from analysis of such big data are immense, so are the inherent challenges, including need for rapid analysis. In his article, TEC BI analyst Jorge García discusses how in-memory analytics helps address these challenges and reap the benefits hidden in big data.

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Linked Enterprise Data: Data at the heart of the company


The data silos of today's business information systems (IS) applications, and the pressure from the current economic climate, globalization, and the Internet make it critical for companies to learn how to manage and extract value from their data. Linked enterprise data (LED) combines the benefits of business intelligence (BI), master data management (MDM), service-oriented architecture (SOA), and search engines to create links among existing data, break down data walls, provide an open, secure, and long-term technological environment, and reduce complexity—read this white paper to find out how.

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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 Evolution: Why a Comprehensive Data Management Platform Supersedes the Data Integration Toolbox


Today’s organizations have incredible amounts of information to be managed, and in many cases it is quickly spiraling out of control. To address the emerging issues around managing, governing, and using data, organizations have been acquiring quite a toolbox of data integration tools and technologies. One of the core drivers for these tools and technologies has been the ever-evolving world of the data warehouse.

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Data Masking: Strengthening Data Privacy and Security


Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating sensitive information, while maintaining data realism and integrity. Many Fortune 500 companies have already integrated data masking technology into their payment card industry (PCI) data security standard (DSS) and other compliance programs—and so can you.

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Optimizing Gross Margin over Continously Cleansed Data


Imperfect product data can erode your gross margin, frustrate both your customers and your employees, and slow new sales opportunities. The proven safeguards are automated data cleansing, systematic management of data processes, and margin optimization. Real dollars can be reclaimed in the supply chain by making certain that every byte of product information is accurate and synchronized, internally and externally.

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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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