ALLOGISTX INTERNATIONAL IS A GLOBAL SUPPLY CHAIN COMPANY!

Companies often cannot rely on the information that serves as the very foundation of their primary business applications. Inaccurate or inconsistent data can hinder your company's ability to understand its current - and future - business problems. This leads to poor decisions that can cause a host of negative results, including lost profits,

operational delays, customer dissatisfaction and much more.

An effective data quality strategy can help you better understand your business environment, allowing you to maximize profitability and reduce costly operational inefficiencies.

Data quality technology allows companies to analyze, improve and control enterprise data, providing the infrastructure to enable data governance by transforming raw data into consistent, accurate and reliable corporate information. The building blocks of enterprise data quality methodology are:

Data Profiling - Inspect data for errors, inconsistencies, redundancies and incomplete information

Data Quality - Correct, standardize and verify data

Data Integration - Match, merge or link data from a variety of disparate sources

Data Enrichment- Enhance data using information from internal and external data sources

Data Monitoring - Check and control data

Many organizations create test plans and assume that testing is over when every single test condition passes their expected results. In reality, this is a very difficult bar to meet. Often, some requirements turn out to be unattainable when tested against production information. Some business rules turn out to be false or incredibly more complex than originally thought.

Collaboration between IT and the business will help identify the reasonable issues versus the issues that cannot be resolved for the current release of the data warehouse. Prioritizing the test plan can help the testing team make these trade-offs.