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Data Quality
Many organizations have already produced several iterations of data warehouses, data marts and decision support services. Once these projects deliver data to users, data quality issues surface. Organizations often find that the benefits of the data warehouse and marts depend on improvements in data quality and reliable quality metrics.
Data Quality Expert, Larry English makes the following points:
'…the business costs of non-quality data, including non-recoverable costs, rework of products and services, workarounds, lost and missed revenue, may be as high as 10-20 percent of revenue or total budget of an organization. Furthermore, as much as 40-50 percent of the typical IT budget may actually be spent in the equivalent of manufacturing's 'scrap and rework.'
An organization's Data Quality should be examined if:
- End Users are questioning the validity of reports
- ETL jobs are failing at load time because of unexpected values and conditions
- There are an unusually high number of default values or 'unknown' conditions in your result sets
Data quality issues may be obscured by large volumes of data. They may be revealed by attempts to bring disparate system together to make 'apples to apples' comparisons.
Business processes may have arisen that changes the meaning of the data, creating a set of undocumented business rules that may be conflict.
These issues cannot be resolved by tools or automated processes alone and require skilled analysis. However, resolving these issues may be crucial to running an enterprise effectively.
An example of a data quality challenge is the analysis of profitability. Manufacturing, marketing, sales and corporate operations may all have significantly different ways of defining and executing profitability calculations. If differences are not resolved, an organization cannot understand its 'bottom line'.
Eclipse provides a Fast Path to evaluate the Data Quality in your current systems environment and make recommendations for methods and tools to improve and manage high quality data delivery to key business end-users.
Data Quality is a commitment…
Eclipse believes in addressing data quality at the initiation of the project, during implementation, at delivery and during maintenance. Eclipse's methodology sees data quality monitoring as an ongoing process. Data Quality issues are much more difficult to identify and to fix when the Data Warehouse has already been implemented. Eclipse believes that Data Quality should not be an afterthought.
The Eclipse Terra Firma Framework provides guidelines, tools and processes to integrate data in a way that helps the warehouse team make conscious choices on how to handle data inconsistencies. Sometimes inconsistencies in data definitions and usage reflect the real nature of the business; in these cases, experienced Eclipse consultants help business users make rational and explicit judgments.
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