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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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 data discrepancies


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

data discrepancies  in the process. Dirty data manifests itself in many different anomalies, below are just a few: Discrepancies in the structure of the data items and specified format Irregularities Integrity constraint violations Contradictions Duplicates Invalid Missing values (part or whole records) Orphaned data Examples of data anomalies: Multiple addresses for IBM Same addresses for IBM 10005 Park Lane 10005 Park LN 1005 Park Lane Multiple ways to identify a vendor...Is it Coca Cola, Cocacola, CocaCola - Uppercase,

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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

Outsourcing, IT Infrastructure

The IT Infrastructure Outsourcing knowledge base focuses on the selection of companies who provide outsource services in the areas of information technology (IT) infrastructure. The typical types of activities that these providers perform include data center operations; network operations; backup/recovery services, data storage management services; system administration services; end user support of desktop PCs, laptops, and handheld devices; web site, or application hosting, etc.  

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Documents related to » data discrepancies

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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Project Costing for Maximum Profitability


By implementing timekeeping solutions for costing, organizations obtain valuable insight into project profitability. Project-oriented timekeeping data is also used to bid on future projects, stay within budgeted costs, allocate appropriate resources, and track projects already under way. These solutions deliver more than just attendance tracking systems. They provide tools for understanding the impact of time and resources on project profitability.

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Supplier Logistics Management (SLM) Part 2


Supplier Logistics Management (SLM) offers the opportunity for considerable improvement in efficiency as well as cost reductions. SLM enables companies and their suppliers to successfully synchronize information.

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AspenTech Releases AspenONE V8.5


AspenTech, a leading provider of software and services to the process industries, releases version 8.5 of aspenONE® manufacturing and supply chain software.

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Achieving a Successful Data Migration


The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.

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Data Management and Business Performance: Part 1-Data


Research for one of my projects led me to ask both software vendors and customers about the factors most important to software users in the selection of a business intelligence (BI) solution. Two topics resounded: the use of BI tools to improve data management and business performance management. Consumers are continuously looking for innovative ways to move, store, and improve the quality of

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Agile Data Masking: Mitigate the Threat of Data Loss Prevention


You may not be as protected from data loss as you think. This infographic looks at some ways in which an enterprise's data can be compromised and vulnerable to security breaches and data loss, and shows how data masking can mean lower security risk and increased defense against data leaks.

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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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The Operational Data Lake: Your On Ramp to Big Data


Companies recognize the need to integrate big data into their real-time analytics and operations, but this poses a lot of technical and resource challenges. Meanwhile, those organizations that have operational data stores (ODSs) in place find that, while useful, they are expensive to scale. The ODS gives real-time visibility into operational data. While more cost-effective than a data warehouse, it uses outdated scaling technology, and performance upgrades require very specialized hardware. Plus, ODSs just can't handle the volume of data that has become a matter of fact for businesses today.

This white paper discusses the concept of the operational data lake, and its potential as an on-ramp to big data by upgrading outdated ODSs. Companies that are building a use case for big data, or those considering an upgrade to their ODS, may benefit from this stepping stone. With a Hadoop relational database management system (RDBMS), companies can expand their big data practices at their own pace.

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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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