malcolm chisholm

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Published By: TopQuadrant     Published Date: Mar 21, 2015
Data management is becoming more and more central to the business model of enterprises. The time when data was looked at as little more than the byproduct of automation is long gone, and today we see enterprises vigorously engaged in trying to unlock maximum value from their data, even to the extent of directly monetizing it. Yet, many of these efforts are hampered by immature data governance and management practices stemming from a legacy that did not pay much attention to data. Part of this problem is a failure to understand that there are different types of data, and each type of data has its own special characteristics, challenges and concerns. Reference data is a special type of data. It is essentially codes whose basic job is to turn other data into meaningful business information and to provide an informational context for the wider world in which the enterprise functions. This paper discusses the challenges associated with implementing a reference data management solution and the essential components of any vision for the governance and management of reference data. It covers the following topics in some detail: What is reference data? Why is reference data management important? What are the challenges of reference data management? What are some best practices for the governance and management of reference data? What capabilities should you look for in a reference data solution?
Tags : 
data management, data, reference data, reference data management, top quadrant, malcolm chisholm
    
TopQuadrant
Published By: iCEDQ     Published Date: Feb 05, 2015
The demand for using data as an asset has grown to a level where data-centric applications are now the norm in enterprises. Yet data-centric applications fall short of user expectations at a high rate. Part of this is due to inadequate quality assurance. This in turn arises from trying to develop data-centric projects using the old paradigm of the SDLC, which came into existence during an age of process automation. SDLC does not fit with data-centric projects and cannot address the QA needs of these projects. Instead, a new approach is needed where analysts develop business rules to test atomic items of data quality. These rules have to be run in an automated fashion in a business rules engine. Additionally, QA has to be carried past the point of application implementation and support the running of the production environment.
Tags : 
data, data management, data warehousing, data quality, etl testing, malcolm chisholm
    
iCEDQ
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