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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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 automation processes data

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

Sales Force Automation (SFA)

Sales Force Automation (SFA) systems help sales and marketing teams with functions related to taking orders, generating proposals or quotes, managing territories, managing partners, and maintaining contact data. Systems often include various levels of analytic and reporting capabilities. 

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Documents related to » automation processes data

Enhancing Interaction Center ROI in Turbulent Economic Times: Beyond Traditional Data Sources for Workforce Automation


Interaction center managers must deliver greater value on smaller budgets. But you can’t simply cut costs and services, or your customers won’t remain satisfied. To better weather the economic storm, release the untapped potential of your interaction center with software that maximizes your workforce management capabilities. Learn how to go beyond traditional channel data for bigger contact center return on investment.

automation processes data  Automation , Process Workforce Automation . By building on your investments in IC processes and workforce optimization software - and going beyond traditional data sources to drive these applications - you can do more with less. Executive Summary Increasing Visibility to Improve the Customer Experience Interaction center managers, along with their counterparts in sales, marketing, and service, are being asked to deliver greater value on shoestring budgets. While the conventional reaction in tough Read More

Evolution to Revolution: The Test Automation Maturity Curve


The evolution of test automation towards data-driven and key/action word frameworks reflects the realization that the process becomes more efficient if there is less code to develop and maintain. Instead of taking twenty years to evolve towards efficiency, you can take a revolutionary leap with a code-free approach that makes it easier to implement, manage, and maintain automated tests.

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10 Steps to Better Software Test Automation


Maybe you’re considering software test tools for the first time. Or maybe you have experience but think your existing software test automation process needs retooling. Whatever your perspective, there are tips and tricks you may not know about test design and development. These 10 steps to building software test automation can help you shorten your time to market, improve the quality of your products, and save you money.

automation processes data  improve your existing test automation efforts, we''ll provide helpful tips and ideas in this paper. We''ve described the entire road map of automation, including: Setting up goals and objectives Defining budget and ROI Analyzing existing QA and development processes Designing your test automation and transition process Identifying, hiring, and training automation resources Selecting tools Promoting internal sale and the automation project Achieving immediate results Measuring your progress You need to read Read More

Lawson Opportunity Analyzer: Managing Processes with a Purpose


Business strategy execution requires the control and automation of complex processes, and the ability to quickly analyze data relevant to the enterprise. Companies have to address these processes in a responsive, flexible way. However, this cannot be achieved simply by optimizing local activities. Multiple activities from different departments must be synchronized, toward the overall goal of increasing organizational profitability.

automation processes data  requires the control and automation of complex processes, and the ability to quickly analyze data relevant to the enterprise. Companies have to address these processes in a responsive, flexible way. However, this cannot be achieved simply by optimizing local activities. Multiple activities from different departments must be synchronized, toward the overall goal of increasing organizational profitability. Read More

Leveraging 3-D for Sales Automation


It’s no longer acceptable for salespeople to respond to customers’ inquiries with “I’ll get back to you.” In the time it takes to respond, a competitor with stronger product knowledge, customized proposals, or better pricing will win your business. Find out how to provide your salespeople with the ability to explain complex manufacturing processes and other key factors—before you lose your sales to your competitors.

automation processes data  TEC | Sales Process Automation 3D Prototypes | TEC Report Manufacturer Sales Reps Positions | TEC White Paper Representative Sales | Technology Evaluation Centers Rep Sales 3D Processes | Sales Rep 3 D TEC | Sales Automation with 3 D Prototypes TEC White Paper | Sales Reps 3D Configurators | Sales People and 3D | Sales Person and 3D Prototypes | Sales Representatives Technology Evaluation Centers | 3D Sales Opportunities TEC | Independant Sales Representative TEC White Paper | TEC White Paper Independant Read More

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.

automation processes data  data warehouse,data warehousing,data acquisition,metadata management,data mining,data cleansing,data capture,Data Warehousing definition,Bill Inmon,Ralph Kimball,database technology management experience,data warehouse design expertise Read More

Business Basics: Unscrubbed Data Is Poisonous Data


Most business software system changes falter--if not fail--because of only a few root causes. Data quality is one of these root causes. The cost of high data quality is low, and the short- and long-term benefits are great.

automation processes data  Basics: Unscrubbed Data Is Poisonous Data Introduction A manufacturing company experienced continually increasing overhead costs in its purchasing and materials departments. It also began to see increased inventory levels and production line delays. A consulting firm was brought in to analyze the company''s materials management processes and the level of competence of its materials and procurement staff. After mapping the entire process, from the creation of a bill of materials through post-sales Read More

Data Migration Management: A Methodology to Sustaining Data Integrity for Going Live and Beyond


For many new system deployments, data migration is one of the last priorities. Data migration is often viewed as simply transferring data between systems, yet the business impact can be significant and detrimental to business continuity when proper data management is not applied. By embracing the five phases of a data migration management methodology outlined in this paper, you can deliver a new system with quality data.

automation processes data  data migration,data migration management,data migration tools,data migration software,data migration plan,data migration tool,data migration services,data migration best practices,data migration process,data migration testing,crm data migration manager,data migration manager,data migration plan template,sql server data migration,legacy data migration Read More

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


Today’s business intelligence (BI) systems have to change, because they’re confronted with new technological developments and new business requirements, such as productivity improvement and systems as well as data in the cloud. This white paper describes a lean form of on-demand data integration technology called data virtualization, and shows you how deploying data virtualization results in BI systems with simpler and more agile architectures that can confront the new challenges much easier.

automation processes data  BI, business intelligence, operational intelligence, Hadoop, NoSQL, self-service BI, data virtualization, big data, on-demand data integration, lean data integration, JBoss Data Virtualization Read More

Informatica PowerCenter 5 Enables Enterprise Data Integration


Informatica Corporation’s Informatica PowerCenter 5 is a platform for integrating data to be deployed in e-Business applications, analytic applications and data warehouses, including a wide range of data sources, from enterprise resource planning (ERP) systems such as SAP R/3 and PeopleSoft, to web logs and Siebel applications. Market validation of its offerings is shown in a record Q4 of 2000, with a 150% increase in revenue over the previous year.

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Data Loss Prevention Best Practices: Managing Sensitive Data in the Enterprise


While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to an equally dangerous situation: data loss from the inside. Given today’s strict regulatory standards, data loss prevention (DLP) has become one of the most critical issues facing executives. Fortunately, effective technical solutions are now available that can help.

automation processes data   Read More

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.

automation processes data  data blending, data analyst, data source, data analysis, data software, data cleansing, data access, data blending software Read More

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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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.

automation processes data  Steps to Manage Data Quality with SQL Server Integration Services Melissa Data''s Data Quality Suite operates like a data quality firewall '' instantly verifying, cleaning, and standardizing your contact data at point of entry, before it enters your database. Source : Melissa Data Resources Related to Six Steps to Manage Data Quality with SQL Server Integration Services : Data quality (Wikipedia) Six Steps to Manage Data Quality with SQL Server Integration Services Data Quality is also known as : Read More

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.

automation processes data  data warehouse, data integration tools, data integration toolbox, big data management, data exploration tools, master data management, data management platform Read More