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Sunday, October 19, 2014 
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Data Quality Risk Mitigation


An Experis IT White Paper

www.experis.us/bi

Abstract
In any Enterprise Business Intelligence initiative, there will always be data quality issues. The key to successfully executing on an EBI strategy is to acknowledge these data issues and prepare your organization on how to deal with them. This white paper discusses how risk mitigating techniques addressing data quality can be applied throughout the phases of a successful methodology.

Introduction
By its very nature, data quality is a variable and often unknown quantity. Why? The data quality issues inherent in yesterday’s data sets do not equate to quality issues that will arise in tomorrow’s data sets. To successfully execute on your organization’s Enterprise Business Intelligence (EBI) strategy, you must acknowledge these issues and prepare your team to deal with them. This “fact” of EBI life demands a straightforward approach for success, and this white paper discusses how risk mitigating techniques addressing data quality can be applied throughout the phases of a successful methodology.

EBI INITIATION PHASE
When solving any issues, the first step is "admitting you have problem." The first step in data quality risk mitigation is accepting that "you don’t know what you don’t know." Just a few of the unknowns: diminished institutional knowledge of data over time, inadequate controls in source systems, and unknown points of manual intervention. While one risk mitigation is to quantify as many of these unknowns as possible (e.g., data quality analysis), it is critical your organization understands that "perfect knowledge" is unrealistic. It may be helpful to highlight relevant examples to illustrate this point to promote acceptance.

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