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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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 web based data quality


Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence

web based data quality  in data. Consider a Web-based system that infers location from a user's Internet protocol (IP). If all users on a particular day are found to be localized in California, it may indicate that on that particular day, the IP-based inference did not function correctly and all users were set to the default location, which happened to be set to California. In a nutshell, data profiling provides information about organizational data. Key quality issues are identified and should be addressed before proceeding

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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

Customer Relationship Management (CRM)

Customer relationship management (CRM) focuses on the retention of customers by collecting all data from every interaction, every customer makes with a company from all access points whether they are phone, mail, Web, or field. The company can then use this data for specific business purposes, marketing, service, support or sales while concentrating on a customer centric approach rather than a product centric. Customer relationship management defines methodologies, strategies, software, and other web-based capabilities that help an enterprise organize and manage customer relationships. Customer relationship management applications are front-end tools designed to facilitate the capture, consolidation, analysis, and enterprise-wide dissemination of data from existing and potential customers. This process occurs throughout the marketing, sales, and service stages, with the objective of better understanding one’s customers and anticipating their interest in an enterprise’s products or services. 

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Documents related to » web based data quality

A Focused Web-based Solution for Chemicals, Drugs, and Mill-based Industries


SSI shows deep understanding of the requirements for chemical, drug, and mill-based industries. Consequently, it has developed such must-have capabilities as potency controls, container movements, top-down and bottom-up traceability, and controls for customs and excise, shelf life, and location validation.

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How to Evaluate Web-based BI Solutions


Web-based business intelligence (BI) is no longer an anomaly: organizations are ready for BI solutions that go beyond Web portals. However, when selecting Web-based BI applications, organizations must evaluate architecture, rather than features or functions. What differentiators do you need to look for before embarking on a full-scale BI implementation? And which vendors offer the solution your organization truly needs?

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The Hidden Role of Data Quality in E-Commerce Success


Successful e-commerce relies on intelligible, trustworthy content. To achieve this, companies need a complete solution at their back- and front-ends, so they can harness and leverage their data and maximize the return on their e-commerce investment.

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Six Steps to Manage Data Quality with SQL Server Integration Services


Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

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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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New Arena PLM Offering Handles Quality


Cloud-based PLM software provider Arena Solutions recently announced Arena Quality, a new solution that aims to better organize quality processes at high-tech companies. Quality processes are now directly connected to the product record in Arena PLM, which provides everyone with visibility into quality issues to rapidly identify, capture, collaborate, and resolve product quality problems.

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Data Security Is Less Expensive than Your Next Liability Lawsuit: Best Practices in Application Data Security


Insecure data. Heavy fines due to non-compliance. Loss of customers and reputation. It adds up to a nightmare scenario that businesses want to avoid at all costs. However, this nightmare is preventable: knowledge base-driven data security solutions can be critical tools for enterprises wanting to secure not only their data—but also their status in the marketplace.

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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 Blending for Dummies


Data analysts support their organization’s decision makers by providing timely key information and answers to key business questions. Data analysts strive to use the best and most complete information possible, but as data increases over time, so does the time required to identify and combine all data sources that might be relevant.

Data blending allows data analysts a way to access data from all data sources, including big data, the cloud, social media sources, third-party data providers, department data stores, in-house databases, and more, and become faster at delivering better information and results to their organizations. In the past, the challenge for data analysts has been accessing this data and cleansing and preparing the data for analysis. The access, cleansing, and preparing data stages are complex and time intensive. These days, however, software tools can help reduce the burden of data preparation, and turn data blending into an asset.

Read this e-book to understand why data blending is important, and learn how combining data means that you can get answers to your business questions and better meet your business needs. Also learn how to identify what features to look for in data blending software solutions, and how to successfully deploy these tools within your business. Data Blending for Dummies breaks the subject down into digestible sections, from understanding data blending to using data blending in the real world. Read on to discover how data blending can help your organization use its data sources to the utmost.

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Data Quality Basics


Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at profit margins. It can also create useless information in the form of inaccurate reports and market analyses. As companies come to rely more and more on their automated systems, data quality becomes an increasingly serious business issue.

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