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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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 centralized data integration


Customer Data Integration: A Primer
Customer data integration (CDI) involves consolidation of customer information for a centralized view of the customer experience. Implementing CDI within a

centralized data integration  touch points into one centralized data source using an SOA. Using a combination of data quality and identity management technology, the DataFlux CDI Solution creates a master data reference file to consolidate information, and then feed those records and update information within a database or application as needed. Data problems are inspected and analyzed before being migrated to the CDI repository. Also, the results of the analysis are used to build targeted data quality routines to correct,

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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

Business Performance Management (BPM) RFI/RFP Template

Data Visualization, Analytics, Workflow, Data Integration, Support, System Requirements 

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Documents related to » centralized data integration

Precision Software Achieves Oracle Validated Integration


Ongoing mergers in the enterprise applications continue to make strange bedfellows of fierce competitors. Most recently, Precision Software, a transportation and global trade management (GTM) division of QAD and a Gold level member of the Oracle Partner Network (OPN), announced that it has achieved Oracle Validated Integration of Precision Software Transportation Management System (TMS) Parcel

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Curing the Data Integration Migraine


The potential value of centralized data integration is enormous. Once implemented, integration systems promise to deliver more accurate and higher quality data. However, for those who venture into the world of implementation, the promise rarely matches the reality. Avoiding the “data integration migraine” requires careful planning to reduce the risks associated with data relationship, transformation, and map discovery.

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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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Flexible Customer Data Integration Solution Adapts to Your Business Needs


Siperian's master data management and customer data integration (CDI) solutions allow organizations to consolidate, manage, and customize customer-related data. The type of CDI hub implemented depends on the CDI environment's maturity, requirements, and alignment with an organization's internal processes.

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Global Software Integration: Why Do So Many Projects Fail?


The IT field is littered with failed global software integration sagas. The many reasons for these failures include mismatched capabilities, geographical requirements, and project technical management deficiencies. Global software projects should start with in-depth analysis of features and functions, so the software’s capabilities meet corporate requirements. Find out how to avoid a failed software integration project.

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Making Big Data Actionable: How Data Visualization and Other Tools Change the Game


To make big data actionable and profitable, firms must find ways to leverage their data. One option is to adopt powerful visualization tools. Through visualization, organizations can find and communicate new insights more easily. Learn how to make big data more actionable by using compelling data visualization tools and techniques.

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Addressing the Complexities of Remote Data Protection


As companies expand operations into new markets, the percentage of total corporate data in remote offices is increasing. Remote offices have unique backup and recovery requirements in order to support a wide range of applications, and to protect against a wide range of risk factors. Discover solutions that help organizations protect remote data and offer extensive data protection and recovery solutions for remote offices.

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Data Management and Analysis


From a business perspective, the role of data management and analysis is crucial. It is not only a resource for gathering new stores of static information; it is also a resource for acquiring knowledge and supporting the decisions companies need to make in all aspects of economic ventures, including mergers and acquisitions (M&As).

For organizational growth, all requirements and opportunities must be accurately communicated throughout the value chain. All users—from end users to data professionals—must have the most accurate data tools and systems in place to efficiently carry out their daily tasks. Data generation development, data quality, document and content management, and data security management are all examples of data-related functions that provide information in a logical and precise manner.

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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 Trends and Adoption


While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies reveal about DQ goals and drivers.

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